Category: 8. Health

Introduction

Etiology of acute vestibular syndrome (AVS) can be classified into peripheral vestibular and central lesions. The former primarily involves the peripheral vestibular structures (inner ear and vestibular nerve), such as vestibular neuritis and migraine, accounting for over 75% of AVS cases.¹ The latter may affect central vestibular structures, including the vestibular nerve nuclei, the root of the eighth cranial nerve at the ponto-medullary junction, the cerebellar flocculus, and the nodulus. These structures participate in controlling the perception of head and body movements, generating vestibular-driven eye movements, processing visual signals, and maintaining balance and posture.^2,3 Therefore, dysfunction of the central vestibular system can lead to dizziness, vertigo, oculomotor disturbances, and postural instability. Central AVS (eg, stroke) account for approximately 20% of all AVS cases.⁴ Central AVS is most commonly vascular in origin, primarily caused by ischemic stroke within the vertebrobasilar artery system,⁵ and it can often present solely as isolated vertigo, making it difficult to recognize during initial diagnosis.⁶ This article provides a detailed overview and summary of the recent diagnostic strategies and advances in clinical and laboratory testing for central AVS of a vascular cause, aiming to enhance clinicians’ understanding of such disease and improve their ability to make differential diagnoses.

Risk Factors of Central AVS

In neuro-otology outpatient clinics, approximately one-quarter of patients with vertigo have central AVS.⁷ Central AVS constitutes 20%-30% of posterior circulation ischemic strokes, with vertigo or dizziness as the dominant manifestation.⁸ While cases with neurological signs are easily recognized, isolated vertigo poses diagnostic challenges due to its mimicry of peripheral vestibular disorders (eg, vestibular neuritis).^9–12 Notably, 19%-42% of posterior circulation ischemia cases present with isolated vertigo,^13,14 and 20% lack focal neurological signs.¹⁴ In emergency departments, central vascular lesions account for approximately 3% of AVS patients.¹⁵ Comorbidities like atrial fibrillation or diabetes increase stroke risk sevenfold.¹⁶ Despite extensive emergency evaluations,¹⁷ emergency evaluations miss 35% of strokes,^18,19 partly due to false-negative MRI-DWI results in 50% of minor stroke-related isolated vertigo cases.²⁰

Patients diagnosed with peripheral vertigo had a significantly higher risk of ischemic stroke than propensity score-matched controls with renal colic (50-fold higher at 7 days; RR=9.3 at 30 days).²¹ And 0.4%-1.5% experiencing ischemic events within a year.²² Overreliance on dizziness classification systems and imaging,²³ coupled with unstandardized bedside exams,²³ perpetuates diagnostic errors. These findings underscore isolated vertigo as a critical “red flag” for central AVS, demanding vigilance even without classic signs or positive initial imaging.^14,20,24

Diagnostic Strategies for Central AVS

Clinical History Characteristics Aid in Diagnosis

For the differential diagnosis of central AVS, it is essential to thoroughly collect the patient’s clinical history and conduct detailed neurological examinations along with bedside tests. Traditionally, excessive emphasis has been placed on categorizing dizziness into specific types—such as vertigo (vascular), presyncope (cardiovascular), episodic (neurological), and nonspecific (psychiatric, metabolic)—to guide diagnosis. This approach has led to numerous cases of central AVS being missed or misdiagnosed.²⁵

Recently, a rapid bedside diagnostic approach, the Triage, Timing, Targeted Examination algorithm (TiTrATE) method, has been proposed.²⁶ It classifies dizziness and vertigo based on the timing of onset (episodic or continuous), trigger factors (such as positional changes), and then conducts a Targeted Exam of eye movements to differentiate between peripheral and central AVS. In emergency patients presenting with intermittent or continuous dizziness, based on the timing and triggering factors from the clinical history, three syndromes may arise: 1) AVS: bedside examination helps differentiate vestibular neuritis from stroke. 2) Spontaneous episodic vestibular syndrome: Clinical history aids in distinguishing vestibular migraine from transient ischemic attack. 3) Triggered episodic vestibular syndrome: the Dix-Hallpike and roll test can be used to differentiate benign paroxysmal positional vertigo (BPPV) from central positional nystagmus caused by posterior fossa lesions.

All these three vestibular syndromes can be caused by vascular diseases, such as transient ischemic attack, ischemic, and hemorrhagic stroke^27,28 (Table 1).

Table 1 Vascular Factors Leading to Four Types of Acute Vestibular Syndromes

Vestibular and Oculomotor Bedside Examinations

When a patient presents with AVS accompanied by other neurological symptoms and signs, stroke can be diagnosed in most cases, even without neuroimaging. However, central AVS with subtle neurological deficits may be undetectable by MRI, presenting a diagnostic challenge even for specialists.²⁹ Recent advances in clinical neurology suggest that systematic bedside evaluation is more advantageous for identifying AVS caused by posterior circulation ischemia than neuroimaging.³⁰ Key vestibular and oculomotor examinations include various nystagmus examinations, the head impulse test (HIT), HINTS bedside test, and other diagnostic approaches.

Various Nystagmus Examinations

Simple downbeat, upbeat, or torsional nystagmus is a significant feature of central vestibular lesions. Other forms of central nystagmus include periodic alternating nystagmus,³¹ see-saw or hemi-see-saw nystagmus,^32,33 and acquired pendular nystagmus.³⁴ Gaze-evoked nystagmus, which changes direction in the horizontal or vertical plane, often indicates impaired integration within the central nervous system network.³⁵ Intense, paradoxical downbeat nystagmus following horizontal head shaking is commonly seen in central lesions, such as stroke, degenerative diseases, and drug intoxication.³⁶ However, the direction of head-shaking nystagmus (HSN) depends on the location and extent of the central lesion, either ipsilateral or contralateral.^37,38 Intense horizontal HSN is frequently observed in lateral medullary infarctions.³⁹ A recent study suggests that enhanced anterior semicircular canal pathway responses may contribute to paradoxical downbeat HSN, representing one of the mechanisms of central lesions.⁴⁰

In both central and peripheral vestibular diseases, positional changes can induce or modulate spontaneous nystagmus. Since central positional nystagmus can resemble the positional nystagmus of BPPV,⁴¹ central lesions should be suspected in patients with persistent positional nystagmus despite repeated canalith repositioning maneuvers.⁴² Particularly in cases of nodular and uvular damage, secondary vestibular neurons’ responses to irregular afferent signals may be enhanced, leading to increased post-rotational signals and resulting in paroxysmal positional nystagmus.⁴³

Head Impulse Test (HIT)

The bedside HIT is an effective tool for distinguishing central AVS from benign inner ear diseases.^44,45 It tests the vestibulo-ocular reflex (VOR) by turning the head rapidly to one side. A positive HIT, characterized by reduced VOR gain with corrective saccades, indicates peripheral vestibular hypofunction.⁴⁶ Lee et al⁴⁷ found that patients with isolated cerebellar infarctions consistently had normal bedside HIT results. However, when lesions involve specific central vestibular structures such as the vestibular nuclei,^48,49 flocculus,^50,51 or nucleus prepositus hypoglossi (NPH),⁵² a positive HIT may be observed. Involvement of the unilateral flocculus or NPH often results in more prominent VOR gain reduction on the healthy side than on the affected side^52,53 (Table 2). A recent report indicated that approximately 20% of patients with posterior inferior cerebellar artery or superior cerebellar artery infarctions exhibited decreased VOR gain on the side contralateral to the lesion, with bilateral mean HIT gain of around 0.75, and 80% of these patients had abnormalities.⁵⁴ Therefore, although a negative HIT strongly suggests central AVS, a positive HIT is not an absolute marker of peripheral vestibular lesions. Moreover, other abnormal HIT patterns indicating central lesions include hyperactivity (increased gain with reverse corrective saccades) and paradoxical responses (upward eye movements during head turns with downward corrective saccades), previously reported in patients with diffuse cerebellar dysfunction.^40,55

Table 2 HIT Manifestations in Central Vestibular Structure Lesions

HINTS Bedside Test

The HINTS examination includes HIT, nystagmus assessment, and the test of skew.⁴⁴ Several studies have demonstrated that HINTS results are highly specific for distinguishing central from peripheral vertigo and are even more sensitive than early MRI-DWI, particularly in diagnosing lacunar infarction.⁵⁶ A study by Choi et al in Korea reported the use of HINTS in 34 patients with AVS caused by lacunar infarction, identifying central eye movement abnormalities in 33 cases, even though initial MRI-DWI scans in six patients did not detect lesions.⁵⁷ That same year, Kim et al conducted HINTS examinations on 91 AVS patients, with 7 of 8 stroke patients displaying central HINTS findings.⁵⁸

It’s noteworthy that HINTS is limited in its ability to detect anterior inferior cerebellar artery (AICA) infarction, with a false-negative rate of 17–29%.⁵⁹ This is because AICA supplies the inner ear and structures like the flocculus, and infarction in this region can result in both central and peripheral vestibular deficits.⁶⁰ A previously reported case of isolated unilateral flocculus infarction showed an increase in low-frequency horizontal VOR gain and decreased VOR gain during high-frequency stimuli. Despite HINTS often being normal in patients with central vestibular lesions, a positive HIT does not rule out cerebellar involvement affecting the flocculus.⁶¹ Therefore, in elderly patients with sudden onset of unilateral hearing loss and vertigo, particularly in the presence of vascular risk factors, isolated labyrinth infarction should be considered. Incorporating horizontal head-shaking and finger-rubbing hearing tests (HINTS plus) is helpful in detecting central lesions, proving more convenient and effective than repeat MRI scans.⁵⁷

Other Diagnostic Approaches

In addition to HINTS, other methods for diagnosing central AVS have been proposed. A retrospective study found that assessing gait and balance is crucial for ruling out AVS.⁶² Although normal gait does not exclude cerebellar infarction, abnormal gait is a significant clue for diagnosing cerebellar stroke.⁶³ The ABCD2 score—accounting for age, blood pressure, clinical features, duration, and diabetes—is used to predict stroke risk in AVS patients. It was shown that 8.1% of AVS patients with a score ≥4 developed a stroke, increasing to 27% with a score of6−7.⁶⁴ However, HINTS is superior to the ABCD2 score in evaluating stroke risk in AVS patients.⁶⁵ Additionally, the recently reported the Triage plus Gait (TriAGe+) score, which includes eight variables [no triggers (2), atrial fibrillation (2), male (1), blood pressure >140/90 (2), brainstem or cerebellar dysfunction (1), focal weakness or speech disturbance (4), dizziness (3), no history of dizziness/vertigo or labyrinth/vestibular disease (2)], has proven to be more sensitive than the ABC Vestibular and oculomotor bedside examinations D2 score. With a score of 10, the sensitivity reaches 83.4%.⁶⁶ A Posterior Circulation Ischemia (PCI) Risk Score specifically designed to assess the risk of posterior circulation stroke was developed in 2018 with higher sensitivity and specificity, and is particularly well used in patients with dizziness as a primary symptom. The PCI scale diagnosed posterior circulation stroke with a much higher sensitivity and area under the receiver operating characteristic curve value (0.82) than the ABCD2 score (0.69).⁶⁷ Another scale that screens AVS symptoms such as imbalance, floating sensation, nonspecific dizziness, unsteadiness, and vertigo showed a sensitivity of 100%, significantly reducing the risk of misdiagnosis in AVS.⁶⁸ A new scale was recently developed. The Sudbury Vertigo Risk Score [Male (1), Age>65 (1), Diabetes (1), Hypertension (3), Motor/sensory (5), Cerebellar (6), BPPV diagnosis (−5)] effectively identifies the risk of a serious diagnosis in patients with dizziness. The risk of a serious diagnosis ranged from 0% for a score of <5 to 16.7% for a score >8. Sensitivity for a serious diagnosis was 100% and specificity was 69.2% for a score <5.⁶⁹

Certain laboratory tests have been reported to be significant indicators for diagnosing AVS patients. The neutrophil-to-lymphocyte ratio (NLR) has become a widely used marker. An NLR >2.8 combined with the absence of horizontal nystagmus is a specific indicator for diagnosing stroke in AVS patients.⁷⁰ Elevated neuron-specific enolase levels in AVS patients are also independently associated with stroke.⁷¹

For years, the relationship between stroke risk factors and AVS has been a research focus. Univariate analysis has shown that age, diabetes, coronary artery disease, atrial fibrillation, and a history of dizziness are statistically significant.⁷² Bi et al also proposed that AVS patients with three or more risk factors (male, age >60, hypertension, diabetes, smoking, and a history of stroke) have a significantly higher risk of posterior circulation infarction.¹⁷ In addition to these factors, Kim et al demonstrated that in multivariate models, imbalance and extracranial atherosclerosis are independent risk factors for posterior circulation infarction⁷³ (Table 3).

Table 3 Diagnostic Methods for Central AVS

Imaging Diagnosis

The sensitivity of computed tomography (CT) in detecting posterior circulation infarction is relatively low, only 16%.^76,77 Diffusion-weighted imaging (DWI) in MRI can detect 80% of infarct lesions. However, it is important to note that within 24–48 hours of onset, 15–20% of posterior circulation infarctions may be missed on MRI scans.⁷⁸ Perfusion-weighted imaging (PWI) helps identify posterior circulation ischemia, especially in patients with initial DWI-negative results. In a prospective cohort study, 12 out of 26 posterior circulation infarction patients presenting with AVS who were DWI-negative had decreased PWI perfusion. The sensitivity of combining HINTS plus balance testing reached 83%, and further integration with PWI improved the sensitivity to 100%. This shows that the combination of neurological and neuro-otological assessments (neurological examination + HINTS plus + balance test) with PWI can accurately identify posterior circulation infarction in patients presenting with AVS.⁷⁹

When a vascular cause is suspected, neurosonography offers a non-invasive method to assist in diagnosis. In a study using duplex ultrasound of the vertebral artery, 25% (27/108) of AVS patients were diagnosed with posterior circulation infarction through ultrasound, showing that while the sensitivity of neurosonography is not high (40.7%), its specificity (100%), positive predictive value (100%), and negative predictive value (83.5%) are favorable⁷⁵ (Table 3).

Conclusions

Central AVS remains a diagnostic challenge, particularly when presenting as isolated vertigo without overt neurological deficits. This review highlights the critical importance of integrating clinical history, targeted bedside examinations (eg, HINTS, nystagmus assessments), and multimodal imaging to differentiate central AVS from peripheral vestibular disorders. Key findings include: 1) Bedside evaluation superiority: systematic oculomotor testing (eg, HINTS) demonstrates higher sensitivity than early MRI-DWI for detecting posterior circulation strokes, especially in lacunar infarctions. 2) Pitfalls in imaging: up to 20% of posterior circulation infarctions may be missed on initial MRI-DWI, necessitating adjunctive techniques like PWI or neurosonography when clinical suspicion persists.3) Complex vestibular pathways: lesions in specific central structures (eg, vestibular nuclei, flocculus) can mimic peripheral vestibular dysfunction, underscoring the need for nuanced interpretation of tests like the HIT. 4) Risk stratification tools: Scores such as TriAGe+ and PCI scale improve early identification of stroke risk in AVS patients, complementing traditional ABCD2 assessments. Although posterior circulation strokes account for the majority of central AVS cases, anterior circulation involvement (particularly insular, frontal, or parietal lesions) may rarely present with isolated vertigo, posing diagnostic challenges. AVS is not only seen in the posterior circulation but can also be seen in the anterior circulation.

It is critical to prioritize bedside examinations despite negative imaging, maintain high suspicion for central AVS in patient population at high-risk of stroke, and adopt a stepwise diagnostic algorithm to optimize outcomes. Moving forward, a multidisciplinary approach—combining advanced neuroimaging, laboratory biomarkers (eg, NLR), and dynamic follow-up—is essential to reduce misdiagnosis rates. Future research should focus on validating rapid diagnostic protocols and exploring the role of emerging technologies (eg, AI-assisted oculomotor analysis) in AVS management.

Acknowledgments

We gratefully appreciate all the participants and staff for their contributions.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was funded in part by the Project of Aerospace center hospital under Grant YN202411(Corresponding author: Zhirong Wan).

Disclosure

The authors report no conflicts of interest in this work.

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Introduction

The elderly population has been rapidly expanding in Japan, which has the most aging society in the world.¹ The ageing of populations leading to an increase in the prevalence of frailty. Frailty is a geriatric syndrome that can be vulnerable to stressors due to a decline in multiple organ function.² Frailty is highly associated with chronic obstructive pulmonary disease (COPD).^3,4 The prevalence of frailty is 32.1% in patients with COPD.⁵ Frailty leads to poor health outcomes in COPD, such as disability, hospitalization, and death.^6–8

The high prevalence of multimorbidity in COPD, including diabetes, cardiovascular diseases, chronic kidney disease, osteoporosis, and sarcopenia, is a key component of frailty in patients with COPD.⁹ Recently, a concept of syndemic, the occurrence of chronic disease clusters including COPD with shared risk factors (eg, ageing, smoking, and inactivity) and biological interactions, has been proposed to manage COPD with a multidisciplinary approach in the context of multimorbidity, moving away from considering COPD as just a single chronic respiratory disease.¹⁰

COPD is closely related to cardiovascular diseases as part of the complex interaction between multimorbid diseases. Compared with the general population, patients with COPD have a higher risk of developing major adverse cardiovascular events (MACE), including acute coronary syndrome (ACS), heart failure (HF), and stroke.^11,12 The underlying mechanisms between COPD and MACE are hyperinflation of the lungs, endothelial dysfunction, hypoxemia, sympathetic hyperactivity, hypercoagulability, and systemic inflammation.^13–17 MACE is a leading cause of mortality in COPD.¹⁸ Nonetheless, cardiovascular diseases are often undiagnosed and undertreated in patients with COPD, leading to worse outcomes.¹⁹

The identification of patients with COPD at risk for MACE is a cornerstone for reducing cardiovascular events and achieving healthy longevity in patients with COPD. Previous studies have demonstrated that frailty is a risk factor for poor prognosis in patients with cardiovascular disease.^20,21 However, the impact of frailty on MACE in patients with COPD remains unknown. In this multicenter, longitudinal, and population-based study, we aimed to evaluate the long-term association between frailty and MACE in patients with COPD using routinely collected clinical data from Sado-Himawari Net, a regional electronic health record (EHR) system in Sado City, Niigata Prefecture, Japan.

Material and Methods

Data Source

We used a routinely collected medical database from Sado-Himawari Net, an EHR system in Sado City, Niigata Prefecture, Japan. This regional EHR system was launched in April 2013 to facilitate cooperation between medical and long-term care resources and to effectively utilize the limited medical resources in the city. This EHR system covers the entire area of Sado across 81 facilities, including hospitals, medical clinics, dental clinics, pharmacies, nursing facilities, and health centers. This EHR system is based on common exchange protocols, such as Health Level Seven International (HL7) and Fast Healthcare Interoperability Resources (FHIR). The medical database includes information on age, sex, diseases, treatments, laboratory tests, and medical image data. All tables show consistent ID numbers for each patient across the tables. Data cleaning and pre-processing were conducted using Python package pandas (version 2.2.2). A total of 17,205 people living in Sado City participated in Sado-Himawari Net in March 2023. The geographical characteristics of Sado city, being on an isolated island (Sado island), lead to a small migration of people in this regional EHR system. Overall, healthcare in Sado City is covered within the regional EHR network system. Sado-Himawari Net continuously collects medical databases over time (every day) from 81 medical facilities across the entire area of this region (Sado city). These characteristics of this EHR system enable a continuous and longitudinal evaluation of clinical outcomes in a small population lost to follow-up in this cohort. We confirm that the data accessed complied with relevant data protection and privacy regulations.

Study Design and Participants

This was a retrospective multicenter longitudinal study. The study period was April 2013 to March 2023. A schematic representation of the study is illustrated in Figure 1. Patients in Sado-Himawari Net database were recruited for this study. Patients with COPD were identified using the corresponding codes from the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10), J42, J43, and J44 (any position). The eligibility criteria were patients with COPD diagnostic codes at least twice and subjects aged 40 years. The index date was defined as the earliest date of COPD diagnosis. The accuracy of these specific codes for COPD diagnosis was validated in previous reports, with a positive predictive value of 85.2–90.8% by COPD diagnostic codes alone.^22–25 We obtained the occurrence of MACE and the time to first MACE. The end of the follow-up period was defined as follows: (i) the date of the first MACE for patients with MACE, (ii) March 2023 (the end of the study period) for patients without MACE, or (iii) the date of loss to follow-up from Sado-Himawari Net. Demographic data, such as age, sex, and inhaled treatments for COPD, were collected from the Sado-Himawari Net at baseline (within 1 year prior to the index date). Comorbidities were identified using corresponding ICD-10 codes (Table S1). Comorbidities included hypertension, diabetes, dyslipidemia, chronic kidney disease, sleep apnea syndrome, depression, sinusitis, and asthma. We evaluated COPD exacerbation during the follow-up period. COPD exacerbation was defined as dispensation claims for systemic corticosteroids within 14 days. This study was approved by the ethics review committee of Yamaguchi University Hospital (approval number:2022–023). This study was conducted in accordance with the principles of the Declaration of Helsinki. This study was registered in the UMIN Clinical Trials Registry (UMIN000048551). Informed consent was waived by the ethics committee because of the retrospective nature of the study.

Figure 1 Study timeline. The index date was defined as the date of the earliest diagnostic code for COPD (ICD-10 codes: J42, J43, and 44). The end of follow-up was defined as the date of the first MACE occurrence, loss to follow-up, or March 31, 2023 (the end of the study period). Abbreviations: COPD, chronic obstructive pulmonary disease; ICD-10, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision. MACE, major adverse cardiovascular event.

Frailty Risk Assessment

In the present study, we evaluated the hospital frailty risk score (HFRS) by using hospital administrative data at baseline as a measure of frailty risk. We calculated the HFRS for individual participants according to the methods described by Gilbert et al.²⁶ HFRS is a frailty assessment tool using 109 ICD-10 diagnostic codes from a health administrative database. Briefly, each ICD-10 code was assigned a score, and HFRS was calculated as the sum of scores for ICD-10 codes that the subjects had received during the hospital visits. The HFRS is a well-validated risk score for frailty that is associated with hospitalization, length of hospitalization, disability, and death. The frailty scores were classified into four categories: no-frailty with HFRS=0, low with HFRS >0 and <5, intermediate with HFRS ≥5 and <15, and high with HFRS ≥15, as previously defined.²⁶ No ICD-10 codes for calculating HFRS were included in the specific codes for the definition of individual MACE.

Outcome Measurements

The time to the first MACE was evaluated. MACE were defined as ACS, HF, or stroke occurrence after the index date. We selected ACS, HF, and stroke because these three were the most utilized MACE endpoints.²⁷ In all participants, we defined individual MACE (ie, ACS, HF, and stroke) by a combination of both ICD-10 codes and medications that were specific to the respective MACE, as previously described.^28–30 ACS was defined as ICD-10 codes I20, I21, I22, I23, and I24 (codes for acute myocardial infarction and unstable angina) with antiplatelet agents. We defined HF using ICD-10 codes I50, I11.0, I13.0, and I13.2 (diagnostic codes for HF) for diuretics and/or cardiotonic drugs. Stroke was defined as ICD-10 codes I63 and I64 (specific codes for cerebral infarction occurrence) with medications for stroke (brain-protective drugs, antiplatelet agents, and/or anticoagulant drugs). The definitions of individual MACE (ACS, HF, and stroke) are detailed in Table S2.

Statistical Analysis

In this study, we describe the numerical variables using the mean and standard deviation for each value. The incidence of MACE for each frailty category (ie, no-frailty, low, intermediate, and high) was calculated in patients with COPD using the Kaplan–Meier method, and differences were compared using the Log rank test with Python package lifelines (version 0.29.0). We applied a multivariate Cox proportional hazard model to evaluate whether these frailty categories were independently associated with MACE, with adjustment for confounding factors such as age, sex, comorbidities (hypertension, diabetes, dyslipidemia, chronic kidney disease, sleep apnea syndrome, depression, sinusitis, and asthma), and inhaled treatments for COPD (ie, inhaled corticosteroids, long-acting β2 agonists, and/or long-acting muscarinic antagonists). We calculated hazard ratios (HR) and corresponding 95% confidence intervals (CIs) for the time to first MACE in the frailty categories (ie, no-frailty, low, intermediate, and high) using the Cox proportional hazard model. Patients were censored if they experienced any MACE or were lost to follow-up. Statistical analyses were performed using the SciPy package (version 1.7.3) in Python. All statistical tests were two-sided, and statistical significance was set at p < 0.05.

Sensitivity Analysis

We performed two sensitivity analyses to confirm the potential association between frailty and MACE in COPD patients. First, we performed a sensitivity analysis excluding patients who experienced COPD exacerbations during the observational period to verify whether the association between frailty and MACE was independent of COPD exacerbations. Second, we performed a sensitivity analysis to adjust for airflow limitation severity (ie, Global Initiative for Chronic Obstructive Lung Disease [GOLD] grade 1–4) in addition to age, sex, inhaled treatments, and comorbidities in subjects who underwent spirometry.

Results

Patients’ Demographics

A total of 1527 patients with COPD were enrolled from Sado-Himawari Net (Figure 2). The mean age was 79.2 years old (SD 10.0) and 691 patients (45.3%) were female (Table 1). The proportion of male was higher in patients with COPD with MACE than in those without MACE. The number (proportions) of patients in each frailty category (no-frailty, low, intermediate, and high) were 230 (15.1%), 702 (46.0%), 519 (34.0%), and 76 (5.0%), respectively (Table 2). Participants with a higher frailty risk were older than those with a lower frailty risk.

Table 1 Baseline Characteristics of COPD Patients with and without MACE During the Follow-up Term

Table 2 Baseline Characteristics of Patients with COPD Stratified by Frailty Categories

Figure 2 Flow diagram of the inclusion/exclusion of COPD participants in this study. For the analysis of the present study, a total of 1527 patients with COPD were enrolled. Abbreviations: COPD, chronic obstructive pulmonary disease; ICD-10, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision.

Clinical Outcomes

Table 3 shows cumulative proportions of occurring any MACE and individual MACE (ie, ACS, HF, and stroke) in all subjects and subjects in each frailty category: no frailty, low, intermediate, and high. A total of 363 (23.8%) patients with COPD experienced MACE during the 10-year follow-up. The number (proportion) of subjects with ACS, HF, and stroke occurrence were 113 (7.4%), 155 (10.2%), and 195 (12.8%), respectively (Table 3). The higher HFRS groups (eg high with HFRS≥15 points) showed a more frequent occurrence of any MACE.

Table 3 The Proportion of Major Adverse Cardiovascular Events Stratified by Frailty Categories During the 10-Year Follow-up

The Association Between Frailty and Risk of MACE Occurrence

The severity of frailty, as evaluated by the HFRS, was significantly associated with an increased risk of a composite of MACE occurrence in patients with COPD. Figure 3 shows the cumulative incidence curve of any MACE in the subjects during the 10-year follow-up period. Patients with COPD and a higher HFRS score showed a higher proportion of MACE (log-rank p<0.001). In the Cox proportional hazard model adjusted for age, sex, inhaled treatments, and comorbidities, frailty categories were significantly associated with MACE occurrence as follows: no-frailty versus low HFRS (HR 1.47 [95% confidence interval, 1.01–2.14], p<0.05), intermediate HFRS (HR 2.00 [1.34–2.97], p<0.001), and high HFRS (HR 2.62 [1.50–4.59], p<0.001) (Table 4).

Table 4 Cox Multivariate Proportional Hazard Ratio for MACE in Patients with COPD (n=1527)

Figure 3 The Kaplan–Meier curves show cumulative incidence of MACE (any of acute coronary syndrome, heart failure, and stroke) in COPD patients in four frailty categories: No-frailty, HFRS=0 (blue line); low, HFRS >0 and <5 (green line); intermediate, HFRS ≥5 and <15 (Orange line); high, HFRS ≥15 (red line). Abbreviations: MACE, major adverse cardiovascular events; COPD, chronic obstructive pulmonary disease; HFRS, hospital frailty risk score.

Sensitivity Analysis

The association between frailty and MACE in patients with COPD was generally consistent with the sensitivity analysis.

Even when including only patients without COPD exacerbations during the follow-up period (n=1339), COPD patients with a higher HFRS category had a higher incidence of MACE (Figure S1). In the multivariable analysis adjusted for age, sex, inhaled treatments, and comorbidities in this sub-population, the association between frailty and MACE remained statistically significant as follows: no-frailty versus low HFRS (HR 1.49 [1.01–2.18]), p<0.05); intermediate HFRS (HR 1.98 [1.32–2.98], p<0.005), and high HFRS (HR 2.48 [1.39–4.42], p<0.005), respectively (Table S3).

Similarly, in the subgroup of patients with spirometry (n=514), COPD patients with a higher HFRS category had a higher incidence of MACE (Figure S2). The association between HFRS and MACE was consistent even after adjusting for the severity of airflow limitation in the COPD subpopulation who underwent spirometry (n=514). After adjusting for the severity of airflow limitation by GOLD classification (ie GOLD 1–4) in addition to age, sex, inhaled treatments, and comorbidities, the association between frailty categories and MACE occurrences remained statistically significant as follows: no-frailty versus low HFRS (HR 1.31 [0.76–2.26]), p=0.33), intermediate HFRS (HR 2.08 [1.17–3.68], p<0.05), and high HFRS (HR 3.37 [1.35–8.43], p<0.05), respectively (Table S4).

Discussion

Utilizing routinely collected clinical data from the EHR system during the 10-year follow-up, we demonstrated that frailty, assessed using HFRS, was associated with a higher proportion of MACE (a composite of ACS, HF, or stroke) in Japanese patients with COPD. Frailty was independently associated with MACE in patients with COPD, even after adjustment for age, sex, comorbidities, inhaled treatments, COPD exacerbations, and severity of airflow limitation, although these results might not be generalized to patients in the other countries.

From the results of the sensitivity analysis, we found that frailty was an independent risk factor for MACE in patients with COPD, even after controlling for the effects of COPD exacerbations and airflow limitation severity. COPD exacerbations were highly associated with an increased risk of MACE in previous studies from Japan and other countries.^31–35 Nonetheless, even after excluding subjects who experienced COPD exacerbations during the follow-up period, the association between frailty and MACE in patients with COPD remained significant in the present study. The severity of airflow limitation has also been associated with MACE in previous reports.^36–38 In contrast, even after adjustment for the severity of airflow limitation defined by the GOLD classification (ie, GOLD 1–4) in a sub-population of COPD patients with spirometry, a higher HFRS category was associated with a risk of developing MACE.

To our knowledge, this is the first study to demonstrate a long-term association between frailty assessed by HFRS and the risk of developing MACE in patients with COPD, although HFRS is not a physical frailty assessment tool like Fried phenotype which can be prevented by rehabilitation and nutrition. Previous studies have shown that frailty is associated with worse outcomes in patients with pre-existing cardiovascular diseases. Previous study demonstrated that physical frailty measured by Fried phenotype was associated with a poor prognosis in patients with preexisting cardiovascular diseases.²⁰ Another previous prospective study showed that a multi-domain frailty (physical, social, and cognitive domain) was a prognostic factor of cardiovascular outcomes in patients with chronic heart failure.²¹ The frailty assessed by HFRS was related to poor prognosis following stroke and transient ischemic attack.³⁹ The results of the present study are in line with these previous reports. Taken together with the results of our study, these findings reinforce an association between frailty and MACE. The underlying pathophysiological mechanisms linking frailty and MACE are believed to be subclinical atherosclerosis due to oxidative stress, endothelial senescence, and systemic inflammation.^40–42 Furthermore, our study is the first to focus on this association in patients with COPD, while these previous reports showed an association in patients with pre-existing cardiovascular diseases. COPD patients with frailty showed an increased level of senescence-associated secretory phenotype proteins such as interleukine-6 and growth differentiation factor-15,^43,44 which potentially aggravate the association between MACE and COPD. These complicated and mutual interactions between the three conditions (frailty, MACE, and COPD) may be a plausible underlying mechanism linking frailty with COPD and MACE in the present study.

The association between frailty and MACE in COPD provides an opportunity to stratify the cardiopulmonary risk in patients with COPD. The coexistence of COPD and cardiovascular diseases is frequently observed. The coexistence of these two diseases is related to worse outcomes than those of either disease alone. However, cardiovascular diseases are often underdiagnosed and undertreated in COPD patients. Notably, a previous study reported that 70% of COPD patients were underdiagnosed with coronary artery disease identified by electrocardiography.¹⁹ Given the association between frailty and MACE in COPD in this study, a frailty assessment supports the identification of COPD patients with cardiopulmonary risk. Physical frailty assessment tools, such as Fried phenotypes, remain the gold standard for evaluating physical frailty status. However, it is difficult to implement these physical frailty assessments in routine clinical practice, owing to limitations in human resources, time, and space in medical facilities. In contrast, utilizing a readily available frailty screening tool such as HFRS and a patient-reported outcome measurement will help to easily detect COPD patients with frailty.^26,45 Consequently, in COPD patients with frailty, early screening with exercise electrocardiograms and/or cardio-echograms may aid early detection of subclinical cardiovascular risk and personalized intervention to mitigate cardiovascular risk. Further prospective studies are required to verify whether early multidiscipline approaches for COPD patients with frailty will reduce the incidence of MACE and ultimately improve longevity in this population.

As part of the complicated relationship between frailty and multimorbidity conditions, we focused on the association between frailty and MACE in patients with COPD. It is not clear how frailty results in adverse outcomes, including disability, hospitalization, and mortality in patients with COPD. Numerous factors such as environmental factors, social factors, genetic factors, and comorbidities might be synergistically and mutually involved in COPD patients with frailty and their accelerated ageing.⁴⁶ Our results may provide insights into understanding these complicated mechanisms in terms of cardiopulmonary relationships. The relationship between frailty and MACE in this study might explain the worse health outcomes (eg, disability, hospitalization, and mortality) in patients with both COPD and frailty. Further comprehensive approaches across multiple organs are required to understand the complex mechanisms of frailty progression in COPD patients. Tian et al showed that biological aging processes are driven by the multiple organ system network of participants in the UK biobank cohort,⁴⁷ which can be a clue for understanding the complicated mechanisms of frailty progression.

Our study, which used an EHR system, has several strengths. First, the utilization of this regional EHR system enabled a longitudinal assessment of clinical outcomes with a small proportion of loss to follow-up for the following reasons: (i) the geographical characteristics of the EHR system (isolated island, Sado Island, with few migrations); (ii) the healthcare system in this region (completion of the overall healthcare within this regional EHR system); and (iii) continuity of medical database collection in this EHR system (Sado-Himawari Net continuously collects the medical database across 81 medical facilities every day). These characteristics of the EHR system are advantageous in evaluating the natural history of COPD. Second, the regional EHR system used in the present study, Sado-Himawari Net, consists of 81 medical facilities, reflecting a real-world clinical setting. This finding supports the generalizability of our results. Third, using the EHR system, frailty could be automatically evaluated by calculating the HFRS based on ICD-10 codes. Frailty assessment, such as the Fried frailty phenotype in routine clinical practice, is difficult to implement owing to time constraints and limited space and human resources in hospitals. By contrast, the EHR-based frailty assessment HFRS can be automatically obtained using routinely collected ICD-10 codes, which overcomes the challenge of assessing frailty in routine clinical practice. Fourth, owing to the longitudinal nature of Sado-Himawari Net, this EHR system continuously collects routine medical information over time, which enables the calculation of HFRS before the occurrence of MACEs. The time between HFRS calculation and MACE occurrence reinforces the longitudinal association between frailty and MACE in patients with COPD.

This study had several limitations need to be mentioned. First, the mean age of patients with COPD was 79.2 years old in the present study. This older demographic, compared to the broader COPD population, might have influenced the study outcomes. Second, we did not obtain cardiovascular risk factors such as smoking, alcohol history, and body mass index. Third, we used a health administrative database to diagnose diseases, which may lead to misclassification of diseases, including COPD, in the present study, as with all studies using EHR systems. A significant limitation of the present study is that subjects with COPD were recruited based on ICD-10 coding rather than spirometry-defined COPD. Fourth, the rate of inhaled treatment was low in patients with COPD from Sado-Himawari Net, which may have affected our results. This may be because there were no pulmonologists in the medical facilities of the regional EHR system. Nonetheless, the rate of inhaled treatments in the present study was similar to that in previous studies of COPD using the EHR database.^33,48 Fifthly, this study did not include participants without COPD, and did not completely demonstrate the direct interaction between COPD, frailty, and MACE. The further study including subjects without COPD will reinforce this relationship. Finally, we did not exclude patients with MACE prior to the index date, possibly leading to left censoring or truncation. This potential bias in the EHR database might have affected the estimation of HR for MACE in this study. The EHR database was not collected for the purpose of this study, and the results of studies using EHR systems should be interpreted with caution.

Conclusion

In conclusion, we verified the long-term impact of frailty on MACE in patients with COPD during a 10-year follow-up, even after adjustment for age, sex, comorbidities, inhaled treatments, COPD exacerbations, and airflow limitation severity. Frailty assessment may play an important role in the identification of COPD patients at risk of MACE, leading to personalized and early interventions using a multi-discipline approach (eg, pulmonologists and cardiologists). Prevention of frailty progression in COPD may ultimately reduce the cardiopulmonary risk toward healthy longevity.

Data Sharing Statement

Clinical data from Sado-Himawari Net were only available to the participating researchers because the participants of the present study did not agree that their data would be shared publicly.

Ethics Approval and Informed Consent

This study was approved by the ethics review committee of Yamaguchi University Hospital (approval number:2022-023). Informed consent was waived by the ethics committee because of the retrospective nature of the study.

Acknowledgments

We thank Mari Shimizu, Kuniaki Imai, Tetsuo Akita, and Hajime Yokota at Healthcare Relations Co., Ltd. and Kenji Sato at Sado General Hospital for providing the EHR database from Sado-Himawari Net. We also thank Nanami Shiosaki at Yamaguchi University for support in obtaining the EHR database.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This work was funded by the AstraZeneca K.K. (Externally Sponsored Research Program [ESR-21-21503]). The funder of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the manuscript.

Disclosure

KH received speaker fees from AstraZeneca, Kyorin Pharmaceutical, Novartis Pharma and Sanofi. KO received speaker fees from AstraZeneca, Boehringer Ingelheim and Sanofi. TH received speaker fees from AstraZeneca, Novartis Pharma and Sanofi. KM received speaker fees from AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Kyorin Pharmaceutical, Novartis Pharma and Sanofi. The other authors have no conflicts of interest to declare related to our work.

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15. Stone IS, Barnes NC, James W, et al. Lung Deflation and Cardiovascular Structure and Function in Chronic Obstructive Pulmonary Disease: a Randomized Controlled Trial. Am J Respir Crit Care Med. 2016;193(7):717–726. doi:10.1164/rccm.201508-1647OC

16. Hohlfeld JM, Vogel-Claussen J, Biller H, et al. Effect of lung deflation with indacaterol plus glycopyrronium on ventricular filling in patients with hyperinflation and COPD (CLAIM): a double-blind, randomised, crossover, placebo-controlled, single-centre trial. Lancet Respir Med. 2018;6(5):368–378. doi:10.1016/S2213-2600(18)30054-7

17. Thomsen M, Dahl M, Lange P, et al. Inflammatory Biomarkers and Comorbidities in Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2012;186(10):982–988. doi:10.1164/rccm.201206-1113OC

18. Sin DD, Anthonisen NR, Soriano JB, et al. Mortality in COPD: role of comorbidities. Eur Respir J. 2006;28(6):1245–1257. doi:10.1183/09031936.00133805

19. Brekke PH, Omland T, Smith P, et al. Underdiagnosis of myocardial infarction in COPD -Cardiac Infarction Injury Score (CIIS) in patients hospitalised for COPD exacerbation. Respir Med. 2008;102(9):1243–1247. doi:10.1016/j.rmed.2008.04.010

20. Veronese N, Cereda E, Stubbs B, et al. Risk of cardiovascular disease morbidity and mortality in frail and pre-frail older adults: results from a meta-analysis and exploratory meta-regression analysis. Ageing Res Rev. 2017;35:63–73. doi:10.1016/j.arr.2017.01.003

21. Matsue Y, Kamiya K, Saito H, et al. Prevalence and prognostic impact of the coexistence of multiple frailty domains in elderly patients with heart failure: the FRAGILE-HF cohort study. Eur J Heart Fail. 2020;22(11):2112–2119. doi:10.1002/ejhf.1926

22. Kurmi OP, Vaucher J, Xiao D, et al. Validity of COPD diagnoses reported through nationwide health insurance systems in the People’s Republic of China. Int J Chron Obstruct Pulmon Dis. 2016;11:419–430. doi:10.2147/COPD.S100736

23. Mapel DW, Frost FJ, Hurley JS, et al. An algorithm for the identification of undiagnosed COPD cases using administrative claims data. J Manag Care Pharm. 2006;12(6):457–465.

24. Quint JK, Müllerova H, DiSantostefano RL, et al. Validation of chronic obstructive pulmonary disease recording in the Clinical Practice Research Datalink (CPRD-GOLD). BMJ Open. 2014;4(7):e005540. doi:10.1136/bmjopen-2014-005540

25. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130–1139. doi:10.1097/01.mlr.0000182534.19832.83

26. Gilbert T, Neuburger J, Kraindler J, et al. Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study. Lancet. 2018;391(10132):1775–1782. doi:10.1016/S0140-6736(18)30668-8

27. Bosco E, Hsueh L, McConeghy KW, et al. Major adverse cardiovascular event definitions used in observational analysis of administrative databases: a systematic review. BMC Med Res Methodol. 2021;21(1):241. doi:10.1186/s12874-021-01440-5

28. Ono Y, Taneda Y, Takeshima T, et al. Validity of Claims Diagnosis Codes for Cardiovascular Diseases in Diabetes Patients in Japanese Administrative Database. Clin Epidemiol. 2020;12:367–375. doi:10.2147/CLEP.S245555

29. Kanaoka K, Iwanaga Y, Okada K, et al. Validity of Diagnostic Algorithms for Cardiovascular Diseases in Japanese Health Insurance Claims. Circ J. 2023;87(4):536–542. doi:10.1253/circj.CJ-22-0566

30. Shima D, Ii Y, Higa S, et al. Validation of novel identification algorithms for major adverse cardiovascular events in a Japanese claims database. J Clin Hypertens (Greenwich). 2021;23(3):646–655. doi:10.1111/jch.14151

31. Matsunaga K, Yoshida Y, Makita N, et al. Increased Risk of Severe Cardiovascular Events Following Exacerbations of Chronic Obstructive Pulmonary Disease: results of the EXACOS‑CV Study in Japan. Adv Ther. 2024;41(8):3362–3377. doi:10.1007/s12325-024-02920-y

32. Yang HM, Ryu MH, Carey VJ, et al. Chronic Obstructive Pulmonary Disease Exacerbations Increase the Risk of Subsequent Cardiovascular Events: a Longitudinal Analysis of the COPDGene Study. J Am Heart Assoc. 2024;13(11):e033882. doi:10.1161/JAHA.123.033882

33. Graul EL, Nordon C, Rhodes K, et al. Temporal Risk of Nonfatal Cardiovascular Events After Chronic Obstructive Pulmonary Disease Exacerbation A Population-based Study. Am J Respir Crit Care Med. 2024;209(8):960–972. doi:10.1164/rccm.202307-1122OC

34. Hesse K, Bourke S, Steer J. Heart failure in patients with COPD exacerbations: looking below the tip of the iceberg. Respir Med. 2022;196:106800. doi:10.1016/j.rmed.2022.106800

35. Hawkins NM, Nordon C, Rhodes K, et al. Heightened long-term cardiovascular risks after exacerbation of chronic obstructive pulmonary disease. Heart. 2024;110(10):702–709. doi:10.1136/heartjnl-2023-323487

36. Krishnan S, Tan WC, Farias R, et al. Impaired Spirometry and COPD Increase the Risk of Cardiovascular Disease A Canadian Cohort Study. CHEST. 2023;164(3):637–649. doi:10.1016/j.chest.2023.02.045

37. Heidorn MW, Steck S, Müller F, et al. FEV1 Predicts Cardiac Status and Outcome in Chronic Heart Failure. CHEST. 2022;161(1):179–189. doi:10.1016/j.chest.2021.07.2176

38. Silvestre OM, Nadruz W, Roca GQ, et al. Declining Lung Function and Cardiovascular Risk. The ARIC Study. J Am Coll Cardiol. 2018;72(10):1109–1122. doi:10.1016/j.jacc.2018.06.049

39. Kilkenny MF, Phan HT, Lindley RI, et al. Utility of the Hospital Frailty Risk Score Derived From Administrative Data and the Association With Stroke Outcomes. Stroke. 2021;52(9):2874–2881. doi:10.1161/STROKEAHA.120.033648

40. Inglés M, Gambini J, Carnicero JA, et al. Oxidative stress is related to frailty, not to age or sex, in a geriatric population: lipid and protein oxidation as biomarkers of frailty. J Am Geriatr Soc. 2014;62(7):1324–1328. doi:10.1111/jgs.12876

41. Walker KA, Walston J, Gottesman RF, et al. Midlife Systemic Inflammation Is Associated With Frailty in Later Life: the ARIC Study. J Gerontol a Biol Sci Med Sci. 2019;74(3):343–349. doi:10.1093/gerona/gly045

42. Baylis D, Bartlett DB, Syddall HE, et al. Immune-endocrine biomarkers as predictors of frailty and mortality: a 10-year longitudinal study in community-dwelling older people. Age (Dordr). 2013;35(3):963–971. doi:10.1007/s11357-012-9396-8

43. Woldhuis RR, Heijink IH, van den Berge M, et al. COPD-derived fibroblasts secrete higher levels of senescence-associated secretory phenotype proteins. Thorax. 2021;76(5):508–511. doi:10.1136/thoraxjnl-2020-215114

44. Hirano T, Doi K, Matsunaga K, et al. A Novel Role of Growth Differentiation Factor (GDF)-15 in Overlap with Sedentary Lifestyle and Cognitive Risk in COPD. J. Clin. Med. 2020;9(9):2737. doi:10.3390/jcm9092737

45. Oishi K, Matsunaga K, Harada M, et al. A New Dyspnea Evaluation System Focusing on Patients’ Perceptions of Dyspnea and Their Living Disabilities: the Linkage between COPD and Frailty. J. Clin. Med. 2020;9(11):3580. doi:10.3390/jcm9113580

46. Burke H, Wilkinson TMA. Unravelling the mechanisms driving multimorbidity in COPD to develop holistic approaches to patient-centred care. Eur Respir Rev. 2021;30(160):210041. doi:10.1183/16000617.0041-2021

47. Tian YE, Cropley V, Maier AB, et al. Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nat Med. 2023;29(5):1221–1231. doi:10.1038/s41591-023-02296-6

48. Spece LJ, Epler EM, Donovan LM, et al. Role of Comorbidities in Treatment and Outcomes after Chronic Obstructive Pulmonary Disease Exacerbations. Ann Am Thorac Soc. 2018;15(9):1033–1038. doi:10.1513/AnnalsATS.201804-255OC

DALLAS, September 3, 2025 — Atrial fibrillation, or AFib, often goes unrecognized despite affecting millions and increasing stroke risk by up to 5 times[1]. New consumer patient research from the American Heart Association, conducted by The Olinger Group, finds that most people with AFib (62%) had no prior knowledge of the condition before being diagnosed[2]. During September, AFib Awareness Month, the American Heart Association, a relentless force changing the future of health for everyone everywhere, is raising awareness nationwide about the condition, and that early identification and treatment of AFib are critical to stroke prevention.

Anyone can develop AFib, and risk increases with age. It is important to know the signs and risk factors:

1. Recognize AFib symptoms and risks. Irregular heartbeat is a common symptom of AFib, while high blood pressure and family history are key risk factors that increase the likelihood of developing the condition.
2. AFib is manageable and treatable. With the right plan you can lower your stroke risk and live fully.
3. You are not alone on your AFib journey. Find support and connect with others at MyAFibExperience.org.

AFib is a quivering or irregular heartbeat that can lead to blood clots, stroke, heart failure and other heart-related complications. According to the latest statics from the American Heart Association, the heart rhythm disorder (arrhythmia) affects over 6 million people in the U.S., and that number is expected to double by 2030[3].

“This projected rise is driven by several factors, including the growing prevalence of high blood pressure, a major risk factor for AFib, as well as increasing rates of diabetes, obesity and an aging population,” said José Joglar, MD, American Heart Association volunteer, professor of cardiac electrophysiology at UT Southwestern Medical Center in Dallas and chair of the 2023 guideline for the diagnosis and management of atrial fibrillation. “It’s important for people to understand their risk factors, recognize potential symptoms and have regular conversations with their health care professional. Early detection and proactive management can make a life-saving difference.”

To better understand this growing public health issue, the Association conducted a nationwide online survey of 1,200 participants, including 770 patients with AFib and 430 caregivers, between January and March 2025. The study assessed awareness of the condition, as well as motivations and barriers to treatment

The findings reveal gaps in public knowledge about AFib and highlight areas where increased awareness is essential to promote earlier recognition and diagnosis of the condition.

Learn the signs and risk factors

Symptoms can vary widely or be completely absent. Many people associate AFib with a racing or irregular heartbeat, however, other symptoms like shortness of breath, fatigue, dizziness, chest pain or fainting may occur.

While anyone can develop AFib, risk increases with age and is higher among people with uncontrolled high blood pressure, Type 2 diabetes, overweight, have had a prior heart attack or a family history of the condition.

According to the research, AFib patients reported experiencing an average of three symptoms before receiving a diagnosis[4], highlighting the need to recognize early warning signs, understand personal risk factors and discuss them with a health care professional. 

Managing AFib

Being diagnosed with AFib may feel overwhelming. However, with the right care plan, you can effectively manage AFib and reduce your risk of stroke and other complications.

Collaborating with a health care team helps patients understand their specific type of AFib and develop a personalized plan. Treatment options for AFib may include medication, procedures and lifestyle changes such as weight management, increasing physical activity, quitting smoking and managing conditions like high blood pressure to support long-term health.

Support is within reach

You’re not alone on your AFib journey. People living with AFib and caregivers can find support and connect with others through the American Heart Association’s online community MyAFibExperience.

This AFib Awareness Month, take action and inspire change by understanding the signs of AFib and talking to your health care team to manage your risk factors. Learn more at Heart.org/AFib.

The HCA Healthcare Foundation is a national sponsor of the American Stroke Association’s Together to End Stroke® initiative and AFib Awareness Month. The research was sponsored by the American Stroke Association, a division of the American Heart Association, with funding support from the HCA Healthcare Foundation.

Additional Resources:

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About the American Heart Association

The American Heart Association is a relentless force for a world of longer, healthier lives. Dedicated to ensuring equitable health in all communities, the organization has been a leading source of health information for more than one hundred years. Supported by more than 35 million volunteers globally, we fund groundbreaking research, advocate for the public’s health, and provide critical resources to save and improve lives affected by cardiovascular disease and stroke. By driving breakthroughs and implementing proven solutions in science, policy, and care, we work tirelessly to advance health and transform lives every day. Connect with us on heart.org, Facebook, X or by calling 1-800-AHA-USA1.   

For Media Inquiries: 214-706-1173

Darcy Wallace: Darcy.Wallace@heart.org

For Public Inquiries: 1-800-AHA-USA1 (242-8721)

heart.org and stroke.org

Providence, RI, Sept. 03, 2025 (GLOBE NEWSWIRE) — As the school year begins, the Association of Migraine Disorders (AMD) is highlighting the challenges students with migraine face during the back-to-school transition. Returning to the classroom means changes in schedules, routines, and new stressors. This shift can increase the risk of migraine attacks for students.

“The transition back to school can be a particularly challenging time for students with migraine,” said Natalia Zorrilla, a board-certified pediatric nurse practitioner and member of AMD’s Advanced Practice Provider Education Committee. “After the more relaxed pace of summer, kids are suddenly faced with early wake-up times, a full day of classes, homework, extracurriculars, and social pressures. This change in schedule and increase in demands can be overwhelming and may trigger migraine attacks. The stress and anxiety that often come with school transitions can also make symptoms worse.”

Migraine in kids and teens

Migraine affects about 1 in 10 school-aged children. Although headache is the most recognized symptom, migraine can look different in kids—making it easier to overlook. Some kids experience the classic throbbing head pain. Some may primarily complain of stomachaches, nausea, and vomiting. Other symptoms may include dizziness, tiredness, irritability, paleness, dark under-eye circles, visual aura (seeing spots, colors, kaleidoscope), or sensitivity to light, sound and smell. 

Common back-to-school migraine triggers

AMD wants to help parents understand that no one trigger will “cause” a migraine attack. But exposure to multiple triggers can increase the risk of an attack. The most common triggers for kids are irregular and inconsistent sleep, dehydration, and changes in eating habits.

“Combined factors including earlier morning wake times, less sleep, prolonged time between meals, exposure to bright fluorescent lighting and noise—think busy hallways and cafeterias—are known to trigger an increase in migraine attacks,” said Deanna Duggan, a pediatric nurse practitioner, headache specialist, and member of AMD’s Advanced Practice Provider Education Committee. “Increased academic and athletic demands can also contribute to anxiety and stress.” 

Other back-to-school migraine triggers:

• Stress (tests/exams, presentations, family life, bullying, etc.)
• Bright lights, fluorescent lights, or flashing lights
• Changes in weather or barometric pressure
• Exposure to certain smells or loud noises
• Increased screen time
• Acute illness

How parents and schools can help

Creating and sticking to a consistent routine can help minimize the risk of an attack. Duggan and Zorrilla recommend the following to support kids living with migraine: 

• Plan ahead: Work with your child and the school to ensure access to water and snacks throughout the day.
• Protect downtime: Avoid overscheduling and leave room for rest or “blank space” in their day.
• Support a healthy sleep routine: Aim for a consistent bedtime and enough hours of rest each night.
• Partner with the school: Share your child’s diagnosis and treatment plan with the school and the school nurse. If needed, request a formal 504 accommodation plan.
• Get medical support: Talk with your child’s provider about lifestyle modifications and treatment options. 

Migraine attacks happen

“Give yourself and your child some grace,” said Duggan. “No matter how hard we try to minimize triggers, migraine attacks can occur abruptly, without warning. If a prescribed migraine treatment becomes less effective, discuss it with your child’s healthcare provider. There are other options.”

About The Association of Migraine Disorders

The Association of Migraine Disorders (AMD) is a 501(c)(3) nonprofit dedicated to advancing the understanding of migraine through research, education, and awareness. AMD is guided by an advisory board of diverse healthcare professionals who understand the wide-ranging symptoms of this complex disease.

• Migraine and Back-to-School

Introduction

Klebsiella pneumoniae (Kp) is a gram-negative bacterium that exists as normal flora in the respiratory and digestive tracts; however, it can cause opportunistic infections. The virulence-related factors contributing to Kp pathophysiology include bacterial capsular polysaccharides (polymorphic), lipopolysaccharides, pili (types 1 and 3), outer membrane proteins, and iron-binding siderophores (aerobactin, enterobactin, salmochelin, and yersiniabactin).^1,2 These virulence factors are resistant to antimicrobial peptides, enabling bacteria to resist the phagocytic influence of host immune cells, adhere to biological and abiotic surfaces, and alter their permeability to antibiotics.^3–5 Siderophores tightly bind to extracellular iron and re-enter bacteria via specific import mechanisms.⁶

Clinically isolated Kp can be divided into two types.^7,8 The first is classical (cKp), which is typically isolated from immunocompromised persons and can easily cause nosocomial infections. cKp strains are not virulent in mouse infection models but often harbor genes that confer multiple drug-resistance, including carbapenem resistance. Carbapenemases are classified into Ambler class A ((carbapenemase) KPC, GES, IMI, NMC, SME), B (IMP, VIM, New Delhi metal-β-lactamase (NDM), GIM, SIM, SPM), or D (OXA-48).^9,10 A previous study showed that the proportion of drug-resistant Kp carbapenemases increases with age, with the highest resistance rates found in patients > 60 years and higher proportions of drug-resistant strains in blood and urine than in sputum.^11,12 The antibiotics tigecycline and colistin are considered last-resort treatments for carbapenem-resistant Kp (CRKp). Key mechanisms of resistance to tigecycline in Kp include the overexpression of efflux pumps (such as AcrAB and OqxAB), inactivation of efflux-pump negative feedback factors, acquisition of the plasmid-borne tet(A) variant gene, and mutation of the rpsJ gene.^13–15 Colistin resistance is associated with genetic changes in lipid A modifications, including the overexpression of two-component regulatory systems (PmrAB and PhoPQ), inactivation of MgrB proteins, and the presence of mcr-1-carrying plasmids.^16–18

The second type, hypervirulent Kp (hvKp) (Figure 1), is characterized by capsular hyperproduction and a hypermucoviscous colony phenotype. hvKp typically causes community-acquired infections, as well as multi-site infections occurring rapidly in sequence, such as liver abscess, encephalitis, endophthalmitis, bacteremia, pneumonia, and empyema, and is usually sensitive to antibiotics.^19,20 The hypervirulent phenotype may be caused by the cumulative effect of different combinations of helper genes that work together to increase bacterial virulence. However, no clinical molecular diagnosis or microbial consensus currently exists for hvKp strains.^1,21,22 Moreover, although the high capsular production and hypermucoviscous phenotype may be closely related to the high virulence of hvKp, this correlation is not consistent.

Figure 1 Virulence mechanisms of hypermucoviscous Klebsiella pneumoniae: This figure illustrates the key mechanisms of virulence of the recently isolated hypermucoviscous Klebsiella pneumoniae.

In recent years, an increasing number of reports on drug-resistant Kp strains have led to an awareness of hypervirulent CRKp (hv-CRKp) strains. hv-CRKp strains emerge from hvKp acquiring mobile genetic elements carrying multiple antibiotic-resistance genes (such as genes encoding extended-spectrum beta-lactamases (ESBLs) and carbapenemases) or multi-drug-resistant (MDR)-Kp acquiring virulence genes (such as rmpA and siderophores), with the subsequent convergence of resistance and virulence.^1,23 Some studies suggest that MDR-Kp is more likely to acquire virulence genes.^1,23

Owing to the highly pathogenic nature of the hv-CRKp strain and its resistance to many antibiotics, many researchers have analyzed its characteristics. In this review, we summarize existing literature on the clinical characteristics, virulence, drug resistance, and treatment of hv-CRKp infection.

Epidemiological and Clinical Features

The combination of hypervirulence and multi-drug resistance in hv-CRKp represents a major medical and health challenge. Multivariate analysis has revealed that a strong biofilm-producing strain, an independent predictor of CRKp mortality, is associated with increased CRKp infection-related deaths.²⁴ The dominant strain of CRKp in the United States and European countries is Kp sequence type (ST) 258, whereas that in China is ST11.^25–28 The main carbapenemase genes in CRKp strains are bla KPC-2, bla NDM-1, and bla OXA-48. The prevalence of hypervirulence among CRKp strains ranges from 7.5% to 15%.^29–31 According to a study by the top three hospitals in Shanghai, China, the isolation rate of the hv-CRKp strain is 1.5%, and the dominant ST is ST11/K64, followed by ST11/K47, ST23/K1, and ST86/K2. ST11 is the most common ST among hv-CRKp isolates in China.²⁵ An Iranian study indicated that 85.7% of hvKp isolates produce ESBL; the carrying rates of bla NDM-6, bla OXA-48, bla CTX-M, blaSHV, and blaTEM were 7.1, 14.3, 21.4, 28.6, and 78.6%, respectively. Of the hvKP isolates, 42.9% were CR-hvKP. Moreover, XDR-hvKP isolates belong to ST15, ST377, ST442, and ST147, respectively.³² hv-CRKp is mainly isolated from respiratory tract and bile specimens but is also detected in other specimens, including urine, blood, and pleural effusion.^25,26 Among infected men, those aged 50–60 years have the highest risk of disease. Other studies have confirmed that older people are more susceptible to hv-CRKp infection, although CRKp has also been isolated from hospitalized infants.^25,26,33,34 Diabetes mellitus is a high-risk factor for hvKp infection.^35,36 Surgery and ICU are the major endemic departments. Furthermore, the mortality rate of hv-CRKp infection is approximately 17.1%.²⁶

Laboratory Analyses

Kp strains have been identified via traditional culture isolation, and virulence-related and drug-resistance genes have been detected using a variety of bacterial strain identification and gene detection methods.³⁷ Classical detection methods include antimicrobial susceptibility testing approaches, such as disk diffusion, AGAR dilution, broth microdilution, MALDI-TOF MS, VITEK MS, and biomsamrieux, which test for antibiotic sensitivity. The string test can be used to determine the hyperviscous phenotype; here, when a loop applied to a colony pulls a “string” of >5 mm, it indicates a positive result; however, this test is affected by many factors, such as culture conditions. The sedimentation assay is more reliable than the string test for determining the hyperviscous phenotype.⁸ The virulence profiles of Kp isolates have been evaluated using a Galleria mellonella infection model. In addition, virulence phenotype identification can be achieved using in vivo virulence models, biofilm formation assays, neutrophil assays, and iron carrier production assays.^38–40 PCR, sequencing, PCR-based multilocus sequence typing, and phylogenetic multilocus sequence typing are also used to detect strains and serotypes.⁴¹ A microdilution checkerboard method can be used to determine the activity of Kp strains against various drugs. In recent years, the application of next-generation and whole-gene sequencing technology, which can rapidly detect strains, drug resistance, and virulence genes, has gradually increased and been applied in clinical practice.⁴²

Virulence and Drug Resistance

Host factors and antibiotics may drive the adaptive evolution of Kp virulence-related and drug-resistance genes.⁴³ Prior antibiotic therapy, previous hospitalization for five days or more, invasive procedures, and mechanical ventilation are all notable risk factors for hv-CRKp strain colonization. When combined with underlying diseases (such as diabetes), carbapenem exposure is an independent risk factor for hv-CRKp strain colonization.^44–46 The common capsular antigens of hv-CRKp in China are K1, K2, and K54 and the ST is ST11.^31,45,47 A study in Malaysia confirmed that all strains isolated from hypermucoviscous CRKp contained carbapenemase-resistance genes and showed multi-drug resistance, whereas the virulence genes detected in hypermucoviscous CRKp harbored the aerobactin siderophore receptor gene (iutA), iroB, rmpA, and rmpA2, with no K1/K serotype, peg-344, allS, or magA.²⁹ As mentioned previously, hv-CRKp has two evolution patterns (Figure 2): CRKp acquiring virulence genes or hv-Kp acquiring resistance genes. Specifically, through the horizontal gene transfer of outer membrane vesicles (OMVs) carrying virulence or resistance genes, OMVs can carry blaNDM-1 genes and pass them to the hvKp strain NTUH-K2044; similarly, OMVs containing virulence genes isolated from hvKp can also be horizontally transferred to ESBL-producing cKp strains, thereby promoting the emergence of hv-CRKp.^38,48,49 The hypervirulence and multi-drug resistance of hv-CRKp are mainly due to the existence of large plasmids containing multiple virulence genes (such as pLVPK) or hybrid conjugation plasmids with both virulence-related and carbapenem-resistance genes.^28,50,51 Capsular evolution may lead to the convergence of carbapenem resistance and high virulence in Kp. According to research on the ST23-K1 strain, the wcaJ gene was interrupted by insertion sequence elements, resulting in small capsule synthesis and decreased virulence. However, the blaKPC-2 plasmid coupling frequency increased, which promoted high virulence and carbapenem resistance in the strain.⁵² Hypervirulent ST11-KL64 can rapidly diversify its resistance to tigecycline and polymyxin treatment. Sequencing analysis has revealed that ramR and lon frameshift mutations are the main causes of tigecycline resistance and that ceftazidime–avibactam (CZA) resistance is associated with the blaKPC-2 mutation. Several mechanisms have been shown to contribute to polymyxin resistance: increased expression of blaKPC-2 that increases the minimum inhibitory concentration of CZA; mutations in pmrB, phoQ, and mgrB; and the insertion of IS (ISKpn74 and IS903B) into the same location of mgrB, as well as a mutation associated with the efflux pumping system.^18,53 Deletion of the acyltransferase gene (act) at the cps site plays a crucial role in the virulence evolution of ST11 CRKp.^54,55 Wang et al isolated a strain of hv-CRKp from patients with scrotal abscess and urinary tract infection and observed its phenotypic transition from high viscosity to low viscosity, which was attributed to either defective or low expression of rmpADC or the capsule synthesis gene wcaJ, or mediated by ISKpn26 insertion/deletion or base pair insertion. Their experiments on mice confirmed that the invasiveness of the strain decreased significantly after transformation to the low-viscosity phenotype; however, the residence time of the strain in the urinary tract and gallbladder of mice was significantly extended.⁴³

Figure 2 Mechanisms of carbapenem-resistant hypermucoviscous Klebsiella pneumoniae formation: This figure presents the formation mechanisms contributing to carbapenem-resistant hypermucoviscous Klebsiella pneumoniae.

The hv-CRKp isolates reported in recent years often harbor multiple resistance genes and a large plasmid containing multiple virulence genes. The Kp0179 strain (found in routine monitoring of clinical samples isolated from a patient in China) was confirmed to belong to K2-ST375. Six resistance genes were identified, including blaSHV-99, fosA, oqxAB, blaNDM-1, qnrS1, and blaSHV-12, the last three of which were located on the binding plasmid pNDM-Kp0179 (IncX3 type), as well as the plasmid pLVPK, of approximately 121 kb (carrying iroBCDN, iucABCDiutA, rmpA, rmpA2, and other virulence genes).⁵⁶ KP18-3-8 and KP18-2079, which are ST11-KL64 CRKp clinical isolate strains, harbor the positive resistance genes blaKPC-2 and rmpA2. Two new hybrid virulence plasmids, KP18-3-8 (pKP1838-KPC-vir, 228,158 bp) and KP18-2079 (pKP1838-KPC-vir, 182,326 bp), have been identified. The IncFII/incr virulence plasmid, pKP18-2079-vir, may be the result of recombinant PLVPK-like virulence and MDR plasmids.⁵⁷ LABACER 01 has a genome sequence of 5,598,020 bp, belongs to ST25, and contains 19 antibiotic-resistance and virulence-related genes, including mrkA-F and ecpD, as well as iron acquisition systems, such as iutA, iron, entB, entS, and entH. The ferric enterobactin-binding periplasmic protein fepB-D is also encoded by basic structural genes cyoA/B, tamA/B, hemN, and gltB associated with dense intestinal colonization. LABACER 27 has a genome sequence of 5,622,382 bp, belongs to ST25, and contains 20 different antibiotic-resistance and virulence factor-related genes, including ycfM, mrkD, kpn, and entB.⁵⁸ SZ651 is a ST15/K19 clone containing multiple resistance genes, including aac(3)-IId, aac(6’)-Ib-cr, blaSHV-28, blaSHV-106, blaTEM-1B, blaOXA-1, blaCTX-M-15, blaKPC-2, mph(A), and tet(A). Several key virulence factors have also been identified in this strain, including genes encoding type 3 fimbria virulence determinants (mrkA, mrkB, mrkE, mrkF, mrkI, and mrkJ) and the iron-containing factor yersiniabactin (ybtA, ybtP, ybtQ, ybtS, ybtT, and ybtU).⁵⁹

A previous study analyzed RJ-8061, a urine isolate from an 86-year-old female patient with pneumonia, which contains KPC-2 and NDM −5 enzymes. This study identified the pRJ-8061-hybrid plasmid as a 294,249 bp hybrid plasmid that contains both resistance genes [blatemm-1b, mph(a), aac(3)-IId] and virulence genes (iucABCDiutA, rmpA2), although rmpA2 was truncated. In addition, blaKPC-2 and blaNDM-5 are located on the pRJ-8061-KPC-2 (IncFII/IncR) plasmid (171,321 bp) and pRJ-8061-NDM-5 (IncX3) plasmid (46,161 bp), respectively.⁶⁰ The Kpn216 strain (French isolate) is resistant to penicillin and its combination with beta-lactamase inhibitors, as well as carbapenems, third-generation cephalosporins, quinolones, and tigecycline. blaCTX-M-15, encoded by a new ST9-IncN plasmid, is found in an IS26-based composite transposon, the downstream of which is a truncated isecp1 insertion sequence. Each isolate carries one IncHI1B/IncFIB replicator and is numbered VIR-KPN154 and VIR-KPN2166.³⁰ KP75w, which belongs to ST11, harbors resistance genes, including carbapenemase genes (blaNDM-1), as well as highly virulent genes (rmpA2, iucABCD-iutA, fyuA, irp, mrk, ybt, fep, and virB2).⁶¹

Treatment

A meta-analysis of 77 studies from 17 countries showed widespread resistance among hvKp strains, including resistance to ampicillin sulbactam, cefazolin, cefuroxime, ceftazidine (57.1%), cefepime (51.3%), and carbapenems, with all resistance rates greater than 40%.⁴⁷ Drug-resistance analysis of hv-CRKp in China revealed resistance to ampicillin, ampicillin/sulbactam, cefoperazone/sulbactam, piperacillin/tazobactam, cefazolin, cefuroxime, ceftazidime, imipenem, meropenem, and amikacin, with resistance rates ranging from more than 60% for ciprofloxacin and 43.8% for benzidine-sulfamethoxazole.²⁵

CZA is a novel combination preparation with good antibacterial activity against MDR gram-negative bacteria that is well-tolerated by patients, with few adverse reactions. Accordingly, CZA is used as salvage therapy in patients infected with CRKp but is ineffective against carbapenemase B.^62–64 Polymyxins and tigecycline are considered critical therapeutics for resistant strains and represent the last line of defense against CRKp infections.^65–69 However, reports of polymyxin and tigecycline resistance have gradually increased. A Chinese study reported that the percentages of tigecycline- and colistin B-resistance in isolated CRKp strains were 1.2% and 4.8%, respectively.⁴⁷ Furthermore, elacycline is a novel preparation that can be used to treat hv-CRKp.

In recent years, non-antibiotic treatments have gradually received attention. For example, the application of Corynebacterium pseudodiphtheriticum to the nasal cavity of mice challenged with the MDR-Kp strain ST25 reduced lung bacterial cell counts and tissue damage.⁷⁰ This was attributed to modulation of the recruitment of white blood cells into the lung and the production of TNF-α, IFN-γ, and IL-10 in the respiratory tract and serum. Thus, this bacterium could represent a novel respiratory tract probiotic, replacing antibiotic therapy and reducing the generation of drug-resistance genes under the burden of antibiotics. However, further studies are required to confirm these findings. Additionally, treatment with the phage kpssk3 improved the survival rate of mice with hypermucoviscous CRKp infection by 100%, with no significant changes in the intestinal microbiota and no serious side effects.⁷¹ Thus, kpssk3 may represent an effective method for treating hypermucoviscous CRKp.

Conclusions

Owing to the annual increase in the number of hv-CRKp strains, the high mortality rate of hv-CRKp infection, and the lack of effective anti-infective drugs, hv-CRKp has become an important burden on global medicine and health. The dominant endemic CRKp strain in the United States and European countries is ST258, whereas that in China is ST11. hv-CRKp can emerge from two main pathways: CRKp acquiring virulence genes or hvKp acquiring drug-resistance genes via the horizontal gene transfer of OMVs. The detection rate of hv-CRKp is the highest in sputum specimens but also very high in bile specimens. The incidence of hv-CRKp is higher in males and increases with age; however, hv-CRKp is also detected in newborns. ICU stays, carbapenem exposure, and diabetes are major risk factors for infection with this strain. The identification of hv-CRKp strains primarily involves the detection of virulence-related and drug-resistance genes. Whole-gene detection has recently emerged as an efficient method, although other detection methods, including 16sRNA and PCR, are also commonly used. Despite this availability of various detection methods for rapid diagnosis for drug resistance genes, it is sometimes difficult to determine whether a specific strain is pathogenic, and the responses of some patients to antibiotics do not match the results of drug resistance gene tests. It is therefore necessary to develop more accurate detection methods to distinguish pathogenic Kp, especially for drug resistance gene detection and drug selection. The drug-resistance rate of hv-CRKp is high. Currently, the commonly used drugs for the treatment of hv-CRKp in clinical practice include CZA, tigecycline, and polymyxin. However, in recent years, the resistance rates to the above-mentioned drugs have gradually increased, including tigecycline resistance caused by ramR and long frame shift mutations, and CZA resistance caused by blaKPC-2 mutations. Additionally, the expression of blaKPC-2 increased, raising the minimum inhibitory concentration of CZA. Mutations in pmrB, phoQ and mgrB, insertion of IS (ISKpn74 and IS903B) into the same position of mgrB, and mutations related to the drainage pump system are all associated with polymyxin resistance. It is therefore recommended to combine antibacterial drugs for the treatment of hv-CRKp. Moreover, new drugs, including elacycline, have gradually emerged for the treatment of hv-CRKp. Furthermore, non-antibiotic therapies, such as C. pseudodiphtheriticum 090104 and kpssk3, represent promising therapies for hv-CRKp that require further research.

Abbreviations

Kp, Klebsiella pneumoniae; cKp, classical Klebsiella pneumoniae; CRKp, carbapenem-resistant Klebsiella pneumoniae; hvKp, hypervirulent Klebsiella pneumoniae; hv-CRKp, hypervirulent CRKp; ESBLs, extended-spectrum beta-lactamases; MDR, multi-drug-resistant; ST, sequence type; iutA, aerobactin siderophore receptor gene; OMVs, outer membrane vesicles; CZA, ceftazidime–avibactam; KPC, carbapenemase; NDM, New Delhi metal-β-lactamase.

Data Sharing Statement

No new data were analyzed or created in this article.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval ofthe version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was supported by grants from the Science and Technology Department of Jilin Province (No.20220508068RC).

Disclosure

The authors declare no conflicts of interest.

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David Hargroves, the NHS Clinical Director for Stroke, said: “This AI decision support technology is revolutionizing how we help people who have been affected by stroke. It is estimated a patient loses around 2m brain cells a minute at the start of a stroke, which is why quick diagnosis and treatment is so critical. AI decision support software provides real-time interpretation of patients’ brain scans – supporting expert doctors and other NHS staff to make faster treatment decisions.”

Dr Michalis Papadakis, CEO and Co-Founder at Brainomix, said: “Brainomix is helping clinicians every day improve the level of care they can deliver to stroke patients in the UK and worldwide. We are delighted to see a focus on the unique and powerful impact that our technology is having on patient outcomes, validated by an expanding base of published, real-world evidence.”

Brainomix is widely recognised as one of the UK’s most successful AI healthcare companies, having developed its technology through commercial scale up to market launch, and having secured a number of successful partnerships with NHS England and The Health Innovation Network.

Brainomix 360 Stroke has been deployed widely across the UK and Europe, where it is the established market leader, and in the United States, where it has been validated by a number of world-class stroke institutions and exhibiting a similar clinical impact on stroke care.

¹ The Times, The Guardian, The Telegraph, The Daily Mail, The Independent, The Sun, The Mirror

Notes to Editors

About Brainomix

Brainomix specializes in the creation of AI-powered software solutions to enable precision medicine for better treatment decisions in stroke and lung fibrosis. With origins as a spinout from the University of Oxford, Brainomix is an expanding commercial-stage company with offices in the UK, Ireland and the USA, and operations in more than 20 countries. A private company, backed by leading healthtech investors, Brainomix has innovated award-winning imaging biomarkers and software solutions that have been clinically adopted in hundreds of hospitals worldwide. Its first product, the Brainomix 360 stroke platform, provides clinicians with the most comprehensive stroke imaging solution, driving increased treatment rates and improving functional independence for patients.

To learn more about Brainomix and its technology visit www.brainomix.com, and follow us on Twitter, LinkedIn and Facebook.

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SOURCE Brainomix

Introduction

Urologic diseases represent a major public health concern for women worldwide.¹ These include both nonmalignant and malignant conditions such as urinary tract infections (UTIs), urolithiasis, kidney cancer, and bladder cancer, which are highly prevalent and associated with substantial morbidity and disability. Although urologic trauma related to obstetric complications is an important issue in some low-resource settings, data on its burden remain limited.² Accordingly, this study focuses on four major urologic diseases in women: UTIs, urolithiasis, kidney cancer, and bladder cancer. UTIs affect more than 40% of women during their lifetime, with Escherichia coli being the most common pathogen.^3,4 A prior history of urinary tract infections is one of the strongest risk factors for future UTIs.⁵ Approximately 30% of women experience recurrent infections within six months. Rising antimicrobial resistance has diminished the effectiveness of standard antibiotic treatments, prompting interest in alternative preventive strategies, such as vaginal estrogen and lactobacillus-containing probiotics in postmenopausal women.⁶ Meanwhile, the burden of urolithiasis has also increased, particularly among women.⁷ Compared to nulliparous women, pregnant women under 50 years of age face more than double the risk of stone formation.⁸ Contributing factors include metabolic syndrome, dietary habits, weight loss interventions, hypercalciuria, and environmental and socioeconomic conditions, all of which have been linked to elevated risk of stone recurrence.^9–11

Kidney and bladder cancers are two other urologic diseases with rising incidence in women. Kidney cancer is now the ninth most common cancer among women globally, with incidence rates increasing by 2–3% per year between 2015 and 2019.¹² Alarmingly, mortality rates for kidney cancer are twice as high in Native American individuals compared to individuals of White descent.¹³ Risk factors for kidney cancer include smoking, alcohol consumption, overweight or obesity, and hypertension.¹⁴ For bladder cancer, smoking remains a major modifiable risk factor, responsible for approximately 50% of cases in men and 40% in women in the United States.¹ While the overall incidence and mortality remain higher in men, women who are active or passive smokers still face significant risk.¹⁵ Additionally, emerging evidence implicates occupational exposures, specific dietary habits, microbiome dysbiosis, gene–environment interactions, diesel exhaust, and pelvic radiotherapy in bladder cancer development.¹⁶ These disparities highlight the complex interplay of biological, behavioral, and social factors in shaping disease burden.

Despite the considerable health impact of these urologic diseases, up-to-date, sex-specific epidemiological data are scarce. Regional and national differences in incidence and outcomes are influenced by healthcare access, sociodemographic development, and environmental exposures.¹⁷ To address this gap, we used data from the Global Burden of Disease Study 2021 to systematically evaluate the incidence and disability-adjusted life years (DALYs) of UTIs, urolithiasis, kidney cancer, and bladder cancer in women across 204 countries and territories from 1990 to 2021.^18–20 This analysis aims to uncover global patterns and temporal trends of four major urinary conditions in women to support evidence-based public health strategies and inform future research and clinical practice.

Patients and Methods

Data Source and Screening

This study utilized data from the Global Burden of Disease (GBD) Study 2021, which systematically estimates the incidence, mortality, DALYs, and age-standardized rates for 371 diseases and injuries across sexes, age groups, and 204 countries and territories worldwide.¹⁸ GBD 2021 employed three core analytical tools—Cause of Death Ensemble Model (CODEm), Spatiotemporal Gaussian Process Regression (ST-GPR), and DisMod-MR 2.1—to synthesize data and generate consistent estimates of disease burden.^18,20

For the present analysis, we extracted data specific to four major urologic diseases in women—urinary tract infections, urolithiasis, kidney cancer, and bladder cancer. We extracted global-level data to analyze overarching trends. For more detailed national and subregional comparisons, we selected Western Europe, China, and North Africa and the Middle East as representative regions based on their geographic diversity, data availability, and distinct epidemiological profiles of urologic diseases. “Incidence” and “DALYs” were chosen as the primary measures of disease burden. To provide a comprehensive overview, we examined age- and year-specific incidence and DALY rates for each of the four conditions across the selected regions.

The Socio-demographic Index (SDI), a composite indicator reflecting income per capita, average educational attainment, and fertility rates, was included to account for variations in development level, given its strong association with health outcomes.²¹ Using GBD 2021 data, countries and territories were categorized into five groups based on SDI: high, high-middle, middle, low-middle, and low. Additionally, the Human Development Index (HDI), a composite measure of overall human development obtained from the United Nations Development Programme, was employed.²² Correlation analyses between GBD data and HDI were conducted to examine the relationship between human development and disease burden.²³ Risk factor attribution was based on the GBD’s comparative risk assessment framework, which comprises six key steps to estimate the proportion of disease burden attributable to modifiable risk exposures.²⁴ This framework enabled further insight into the global patterns and drivers of urologic disease burden in women.

Statistical Analysis

The age-standardized rate (ASR), was used to account for differences in age structures between populations and over time. It was calculated using the following formula:

In the equation, _i represents the age-specific rate in the i^th age group, and w_i denotes the count of individuals in the same age group based on the GBD 2021 standard population.¹⁸

To evaluate temporal trends in the burden of urologic diseases in women, we calculated the estimated annual percentage change (EAPC) in age-standardized incidence rate (ASIR) and age-standardized DALY rate (ASDR) from 1990 to 2021.²⁵ EAPC was derived from a linear regression model fitted to the natural logarithm of the ASR, specified as:

EAPC was then defined as:

The 95% confidence interval (CI) of the EAPC was also obtained from the regression model.²⁶ We interpreted a trend as statistically significant if both the EAPC and its 95% CI were either entirely above or entirely below zero. If the 95% CI included zero, the trend was considered statistically insignificant.

Finally, to project trends through 2046, we conducted an age–period–cohort (APC) analysis using the “Nordpred” package in R. This approach considers both demographic changes and temporal trends and has been well-established in previous studies.²⁷ All statistical analyses were performed using R software (version 4.3.2), and rates were expressed per 100,000 population. Statistical significance was determined using a p value of <0.05.

Results

Global and Regional Patterns in the Burden of Urologic Diseases in Women

In 2021, the global incidence of urologic diseases in women showed a notable increase. The estimated number of new cases was 35,718.97 × 10⁵ for UTIs (95% UI: 31,808.47–39,914.82), 3,487.81 × 10⁵ for urolithiasis (95% UI: 2,913.36–4,247.25), 13.52 × 10⁵ for kidney cancer (95% UI: 12.41–14.42), and 12.26 × 10⁵ for bladder cancer (95% UI: 10.82–13.39). To better capture temporal trends while accounting for population growth and changes in age distribution, age-standardized rates (ASRs) were utilized. Analysis of the ASIR and ASDR from 1990 to 2021 revealed heterogeneous trends across different diseases.

The ASIR of UTIs remained relatively stable globally with EAPC of 0.03 (95% CI 0.02 to 0.05), whereas its ASDR generally declined with EAPC of −0.6 (95% CI: −0.76 to −0.25), except in China where a slight upward trend was observed. For urolithiasis, both the ASIR and ASDR declined steadily over the study period, with a global ASIR EAPC of −0.16 (95% CI −0.19 to −0.14) and ASDR EAPC of −0.26 (95% CI −0.35 to −0.08). In contrast, the ASIR for kidney cancer remained relatively stable with EAPC of 0.04 (95% CI −0.03 to 0.12), while its ASDR significantly decreased with EAPC −0.23 (95% CI −0.30 to −0.13). For bladder cancer, both ASIR and ASDR showed a favorable and consistent downward trend, with an ASIR EAPC of −0.15 (95% CI −0.22 to −0.07) and ASDR EAPC of −0.31 (95% CI −0.38 to −0.22). These results are detailed in Table 1.

Table 1 Global Incidence and DALYs of Four Female Genitourinary Diseases from 1990 to 2021

National and Subregional Trends in the Burden of Urologic Diseases in Women

At the national level, the ASDR for UTIs has declined in most countries or regions worldwide, with China showing the most pronounced decrease (EAPC: –0.60; 95% CI: –0.76 to –0.25). In contrast, several countries in North Africa and South America, such as Argentina, Uruguay, and Kuwait, have experienced a rapid increase in ASDR (Figure 1A). For urolithiasis, the ASDR has increased in several countries, including Libya, Brazil, and Guyana, whereas Czechia recorded the fastest decline (EAPC: –0.73; 95% CI: –0.80 to –0.65) (Figure 1B). Regarding kidney cancer, although the ASDR is generally decreasing, the rate of decline is relatively modest. Sri Lanka leads in the reduction trend with an EAPC of –0.79 (95% CI: –0.87 to –0.67) (Figure 1C). As for bladder cancer, some countries show substantially faster declines in ASDR than others, with Mongolia, Mauritius, and Egypt ranking in the top three (Figure 1D). Overall, China stands out globally for achieving substantial reductions in the ASDR across all four major urologic diseases in women, with an EAPC of –0.60 (95% CI: –0.76 to –0.25) for urinary tract infections, –0.66 (95% CI: –0.76 to –0.40) for urolithiasis, –0.52 (95% CI: –0.67 to –0.32) for kidney cancer, and –0.33 (95% CI: –0.55 to –0.05) for bladder cancer.

Figure 1 Global and regional variations in the EAPC of ASDR for urologic diseases in women. (A) Urinary tract infections. (B) Urolithiasis. (C) Kidney cancer. (D) Bladder cancer.

Correlation Among EAPC, ASR, and HDI

In the correlation analysis between the ASR and the EAPC from 1990 to 2021 for urologic diseases in women, a notable negative correlation was observed between the ASDR of UTIs and the corresponding EAPC in 1990 (cor=−0.3184, p<0.0001), while a positive correlation emerged by 2021 (cor=0.2299, p=0.0009) (Figure 2A). A similar trend was found for urolithiasis, with a negative correlation in 1990 (cor=−0.3376, p<0.0001) and a positive correlation in 2021 (cor=0.2236, p=0.0013) (Figure 2B). In contrast, for urologic cancers, including kidney and bladder cancer, significant negative correlations were noted in 1990 between ASIR/ASDR and EAPC, but no significant correlations were found in 2021 (Figure 2C and D). Regarding the association between EAPC and the Human Development Index (HDI) in 2021, a positive correlation was observed between the ASDR and EAPC for UTI (cor = 0.2546, p = 0.0013), and a negative correlation for bladder cancer (cor = –0.1810, p = 0.0233). No statistically significant associations were identified for other diseases (Figure 2E–H).

Figure 2 Correlations of EAPC with ASR and HDI for urologic diseases in women. Panels (A–D) show the correlation between EAPC and ASRs in 1990 for urinary tract infections (A), urolithiasis (B), kidney cancer (C), and bladder cancer (D). Panels (E–H) show the correlation between EAPC and HDI in 2021 for the same diseases (E–H, respectively).

Current Age-Specific Burden of Urologic Diseases in Women

Figure 3 illustrates the global age-specific distribution of incidence and DALYs for four major urologic diseases in women in 2021. Non-neoplastic diseases displayed pronounced differences in age patterns. The incidence of UTIs peaked between ages 30–34, with approximately 37 million new cases. Conversely, urolithiasis peaked later, around ages 55–59, reaching nearly 50 million cases. Regarding incidence rates, UTIs demonstrated a bimodal distribution, with the first peak in middle-aged adults (25–54 years) and a second sharp increase among individuals older than 85, exceeding 10,000 per 100,000 population. The incidence rate pattern for urolithiasis mirrored its case distribution, peaking similarly in the 55–59 age group (Figure 3A).

Figure 3 Global incidence and DALY counts and rates for urologic diseases in women by age group. (A) Incidence of non-neoplastic diseases. (B) DALYs of non-neoplastic diseases. (C) Incidence of neoplastic diseases. (D) DALYs of neoplastic diseases.

The age distribution of DALYs for UTIs followed a bimodal trend, with a pronounced peak in the 15–24 age group, followed by a decline and then a second rise, reaching the highest burden in the 70–74 age group. In contrast, the DALYs burden for urolithiasis steadily increased until 55–59 years, then gradually declined. In terms of DALY rates, both UTIs and urolithiasis showed a general increase with age, with UTIs displaying a marked surge after age 85 (Figure 3B).

Due to the life-threatening nature of kidney and bladder cancers, both diseases exhibited similar age-related patterns in incidence and DALYs. Peaks were observed in the 65–79 age range, with the burden consistently increasing with age. Notably, kidney cancer showed a minor uptick in incidence between ages 2–10, and the corresponding DALYs among individuals aged 2–19 showed a negative correlation with age (Figure 3C and D).

Figure 4 presents the EAPC in age-specific DALY rates across different regions from 1990 to 2021. For UTIs, China experienced declines in all age groups, while many other regions, particularly high-middle SDI areas, showed a pattern of decreasing burden in younger groups and increasing burden in the oldest age groups, peaking at an EAPC of 2.48 in individuals aged 95 and older (Figure 4A). Urolithiasis showed an overall decreasing trend in most age groups globally, especially in China. However, an increasing trend in DALY rates after age 35 was observed in North Africa and the Middle East (Figure 4B). For kidney cancer, most age groups in low, low-middle, and middle SDI regions demonstrated an increasing trend in DALY rates (Figure 4C). In contrast, bladder cancer presented a more favorable picture: DALY rates declined across nearly all regions and age groups, except among individuals older than 95, where a slight increase was noted (Figure 4D).

Figure 4 EAPC in DALY rates for urologic diseases in women by age group and region, 1990–2021. (A) Urinary tract infections. (B) Urolithiasis. (C) Kidney cancer. (D) Bladder cancer.

Composition of Incident Cases and Risk-Attributable DALYs for Urologic Diseases in Women

The composition of incident cases and risk-attributable DALYs for major urologic diseases in women in 1990 and 2021 was analyzed (Figure 5). In 2021, among non-neoplastic urologic diseases in women, urinary tract infections (UTIs) accounted for a significantly higher proportion of global incident cases compared to urolithiasis (91.1% vs 8.9%). However, in China, urolithiasis contributed a relatively higher proportion than the global average, with UTIs and urolithiasis accounting for 75.7% and 24.3% of cases, respectively (Figure 5A). For urologic cancers, kidney cancer represented a slightly greater share of incident cases globally than bladder cancer (52.5% vs 47.5%). This disparity was particularly pronounced in North Africa and the Middle East (60.5% vs 39.5%). In contrast, low Socio-demographic Index (SDI) regions demonstrated the opposite pattern, with bladder cancer comprising a higher proportion (59.6% vs 40.4%) (Figure 5B). Longitudinal trends from 1990 to 2021 indicate that the global proportion of UTIs among non-neoplastic urologic diseases has continued to rise. Meanwhile, the proportion of kidney cancer among urologic malignancies has increased across all SDI regions.

Figure 5 The proportion of incident cases and DALYs attributable to risk factors for urologic diseases in women, 1990–2021. (A) Proportional distribution of incident cases among non-neoplastic urologic diseases. (B) Proportional distribution of incident cases among neoplastic urologic diseases. (C) Proportion of DALYs attributable to specific risk factors for kidney cancer. (D) Proportion of DALYs attributable to specific risk factors for bladder cancer.

In 2021, the leading attributable risk factor for kidney cancer was high body mass index (BMI), accounting for 85.0% of the DALYs, followed by smoking (14.8%) and occupational carcinogens (0.2%) (Figure 5C). The contribution of smoking was highest in high-SDI and Western European countries (23.3% and 23.2%, respectively). Risk factors for bladder cancer showed marked regional variation: in low-SDI and North Africa/Middle East regions, high fasting plasma glucose was the predominant risk factor (62.8% in North Africa and the Middle East), whereas in high-SDI and Western Europe, smoking was the leading contributor (69.1% and 71.9%, respectively) (Figure 5D). Compared to 1990, the contribution of high BMI to kidney cancer burden increased in 2021, while the role of high fasting plasma glucose as a risk factor for bladder cancer also rose. Consequently, the proportion of urologic cancer-related DALYs attributable to smoking among women has declined.

Projections of Global Incidence and DALY Rates of Urologic Diseases in Women

We projected the trends in ASIR and ASDR for four major urologic diseases in women worldwide from 2021 to 2046 (Figure 6). For UTIs, both the ASIR and ASDR are expected to remain relatively stable over the next decade, with a modest upward trend anticipated after 2032 (Figure 6A). In the case of urolithiasis, projections suggest that both ASIR and ASDR will remain stable throughout the forecast period, without significant fluctuation (Figure 6B). For malignant urologic conditions, the predicted trajectories for kidney and bladder cancers show a slight initial decline in both ASIR and ASDR, followed by a mild increase in subsequent years. However, the magnitude of these changes is relatively small, indicating a generally stable burden over time (Figure 6C and D).

Figure 6 Predicted trends in incidence and DALY rates for urologic diseases in women from 2021 to 2046. (A) Projected age-standardized incidence rates for non-neoplastic urologic diseases. (B) Projected age-standardized DALY rates for non-neoplastic urologic diseases. (C) Projected age-standardized incidence rates for neoplastic urologic diseases. (D) Projected age-standardized DALY rates for neoplastic urologic diseases.

Discussion

In recent years, women’s urologic health has gained increasing global attention due to its growing prevalence and associated healthcare burden. These diseases pose substantial challenges to public health systems and call for urgent, coordinated responses.²⁸ Using data from the Global Burden of Disease Study 2021, we systematically assessed the incidence and DALYs for UTIs, urolithiasis, kidney cancer, and bladder cancer in women across global, regional, and national levels from 1990 to 2021.

These four urologic diseases display two distinct epidemiological patterns—non-malignant conditions like UTIs and urolithiasis, and malignant ones like kidney and bladder cancers. UTIs remain a major public health concern among women due to their high prevalence and potential complications.²⁹ Our findings indicate that although the ASDR for UTIs has remained stable in most regions, the absolute number of cases has risen significantly, likely driven by population growth, aging, and the heightened susceptibility of elderly women.³⁰ This is consistent with the findings of Yang et al, who reported a rising incidence of UTIs associated with aging populations.³¹ Cognitive impairment, incontinence, and diminished functional capacity—common among older women—are established risk factors for UTIs.^32,33 Notably, several South American countries experienced a marked rise in UTI-related ASDR, possibly due to the increased prevalence of multidrug-resistant infections.³⁴ Correlation analyses further revealed shifting trends in burden disparities. In 1990, a negative association was observed between baseline ASDR and EAPC, suggesting convergence across countries. However, by 2021, this relationship reversed, possibly reflecting inequities in healthcare access. A similar trend was observed in urolithiasis, whereas it is less pronounced in kidney and bladder cancers. Additionally, UTI-related ASDRs positively correlated with HDI, potentially due to the higher prevalence of resistant pathogens in high-income settings.³⁵

Although the overall burden of urolithiasis appears stable or declining, an upward trend is evident in tropical and hot-climate regions, possibly linked to dehydration, dietary factors, and environmental exposures.^36–38 This finding aligns with Wang et al’ s findings on climate-related risk for stone formation.³⁹ The highest burden was noted among women aged 50–59, suggesting a possible link to menopause, which may increase urinary calcium excretion and thereby the risk of stone formation as suggested by Prochaska et al^40,41 Future projections indicate a relatively stable burden, likely supported by advances in surgical and minimally invasive treatment options.⁴²

Urologic cancers show distinct epidemiological trajectories. Kidney cancer has surpassed bladder cancer as the leading malignant urologic disease in women in regions such as North Africa and the Middle East. ASDRs for both kidney and bladder cancers were negatively associated with HDI, underscoring the disproportionate burden in low-resource settings due to delayed diagnosis and limited treatment access.⁴³ The long-term cancer control successes observed in North America, Oceania, and parts of Europe emphasize the importance of early detection and effective treatment.⁴⁴ While these cancers primarily affect older populations, kidney cancer also contributes substantially to DALYs in children, likely due to nephroblastoma and early-onset clear cell carcinoma.⁴⁵

Among modifiable risk factors, smoking remains the predominant contributor to DALYs from female bladder and kidney cancers. Despite a global decline in smoking prevalence since 1990, it continued to account for the largest share of bladder cancer-related DALYs in women throughout the study period. This highlights the persistent need for robust tobacco control policies, especially targeting youth and secondhand smoke exposure.^46,47 Although men are generally at higher risk for bladder cancer, women tend to be diagnosed at more advanced stages.^48,49 Sex differences in tumor detection may contribute to these disparities, with men more likely to receive early diagnosis.⁵⁰ Emerging evidence also suggests that sex hormones and their receptors may influence tumorigenesis and progression.^51–53 These findings underscore the necessity of gender-specific prevention and treatment strategies to reduce sex-based disparities in cancer outcomes. In addition, obesity, particularly abdominal obesity, is a well-documented risk factor for kidney cancer, with obese individuals showing a 1.32-fold higher risk than their non-obese counterparts.^54,55 We also observed an increasing contribution of elevated fasting plasma glucose to bladder cancer DALYs, pointing to the growing global burden of metabolic syndrome.⁵⁶ Strong evidence supports the role of lifestyle interventions, such as physical activity and balanced diets, in mitigating cancer risk.^57,58 Therefore, alongside anti-smoking measures, strategies to enhance metabolic health including diabetes management and nutritional guidance should be prioritized in future cancer control efforts targeting women.

While our study offers the most recent GBD-based estimates on the global burden of four common urologic diseases in women, it is subject to several limitations. First, like all GBD studies, the quality and completeness of data vary across countries, particularly in low- and middle-income settings where robust epidemiological data are often lacking. Biases in diagnostic criteria and data reporting in primary studies also affect accuracy.^18–20,24 Second, the impact of the COVID-19 pandemic introduces uncertainty in mortality estimates, especially in heavily affected regions. Third, our focus was limited to UTIs, urolithiasis, kidney cancer, and bladder cancer, excluding other urologic conditions that may be significant. Fourth, definitional constraints in the GBD database may lead to underestimation of disease burden. Fifth, differences in diagnostic practices across countries and over time could limit comparability. These limitations necessitate a cautious interpretation of global burden trends and call for improved data collection, harmonized diagnostic criteria, and complementary analytical approaches to validate our findings. Lastly, the GBD risk analysis is literature-based and may not account for all disease-specific risk factors.

Conclusion

Urologic diseases in women pose a growing global health challenge. The burden of UTIs and kidney cancer continues to rise with aging populations, while urolithiasis and bladder cancer are declining. Disparities in healthcare access and prevention have led to a polarized disease burden across countries. The rising impact of metabolically related cancers highlights the need for better metabolic health management. Strengthening global collaboration to develop effective screening and targeted, gender-sensitive strategies is essential to reduce the burden of these diseases.

Abbreviations

DALYs, disability-adjusted life-years; ASR, age-standardized rate; ASIR, age-standardized incidence rate; ASDR, age-standardized DALYs rate; EAPC, estimated annual percentage change; UI, uncertainty interval; SDI, socio-demographic index; HDI, human development index.

Ethics Approval and Consent to Participate

The study got an exemption from the Ethical Review Committee of the Fourth Affiliated Hospital of School of Medicine, Zhejiang University, because it used publicly available and deidentified data from GBD database.

Disclosure

The authors report no conflicts of interest in this work.

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