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Recommendations in NICE’s type 2 diabetes guideline update published today will enhance the use of SGLT-2 inhibitors in primary care and address these health inequalities.

NICE analysis conducted in 2024 revealed significant disparities in SGLT-2 inhibitor prescriptions for type 2 diabetes patients in England. The study, examining records of almost 590,000 people, found these medications, which protect the heart and kidneys whilst lowering blood sugar levels, are particularly under-prescribed to women, older people and Black or Black British individuals.

Key findings show only one in five people with atherosclerotic cardiovascular disease (ASCVD) received SGLT-2 prescriptions. Age-related disparities were stark – 32% of people aged 50 to 59 with ASCVD received prescriptions compared to just 13% of those aged 80-89. Gender differences were also notable, with men (35%) more likely to receive prescriptions than women (23%) among people with heart failure.

Several factors contribute to these inequalities. For older people, concerns about medication interactions may limit prescriptions. Women’s lower prescription rates might relate to urinary tract infection (UTI) risks and pregnancy considerations, though given the age distribution of the eligible population the former factor is likely to be much more relevant. Black or Black British people with ASCVD and those in deprived areas with ASCVD had 22% and 15% lower odds of receiving prescriptions respectively.

We have updated our guidelines to recommend SGLT-2 inhibitors as first-line treatment for all diabetes patients, not just those with heart disease. The updated guidance also calls for research to better understand and address these prescribing disparities.

Benefit of increased use

NICE findings from the research (detailed below) suggest that a uniform and safe increase in SGLT-2 inhibitors’ uptake would bring the greatest benefits for people in deprived areas and ethnic minorities, where type 2 diabetes is more prevalent.

 “There is some urgency to find ways to increase the uptake of SGLT-2 inhibitors because if we were to achieve perfect uptake the nation would be significantly healthier,” said Dr Waqaar Shah, a GP partner at Chatfield Health Care in London and committee chair for the newly published type 2 diabetes guideline update. “That has benefits primarily for the patient but also for the healthcare system, potentially reducing the use of resources associated with health complications.”

Diabetes – a condition where someone has too much glucose (sugar) in their blood – costs the NHS £1 million per hour, about 10% of its entire budget. According to Diabetes UK, every week diabetes leads to more than 930 strokes, 660 heart attacks, and almost 2,990 cases of heart failure in the UK. It is estimated that 60% of NHS spending on diabetes goes towards treating complications. 

What are SGLT-2 inhibitors?

Sodium-glucose co-transporter-2 (SGLT-2) inhibitors are once-a-day tablets to treat type 2 diabetes. They reduce blood sugar levels by helping the kidneys remove excess sugar from the body. They can also protect against heart failure and heart attacks.

The 2022 type 2 diabetes NICE guideline recommended SGLT-2 inhibitors for adults with type 2 diabetes and co-existing chronic heart failure (CHF) or ASCVD. It advised doctors consider the medicine for people at high risk of developing cardiovascular disease (CVD).

Low uptake

The analysis of primary care data found that the proportion of people prescribed SGLT-2 inhibitors was low: Only one in five people with comorbid ASCVD or at high risk of CVD had a current prescription for one of these medicines, compared to about one in three patients with CHF.

The updated guidance recommending SGLT-2 inhibitors as a first-line treatment for everyone with diabetes will raise awareness of the treatment among healthcare professionals and increase uptake for all patient groups.

Inequality of access

Older age groups were less likely to have a current SGLT-2 inhibitor prescription.

Figure 1: Proportion of type 2 diabetes patients with an SGLT-2 inhibitor prescription in England, by age group and comorbidity

Note: Data as of 1 September 2023, 18 months after the recommendations were published. Includes people aged 18 or over. Source: NICE analysis of Clinical Practice Research Datalink (CPRD) data.

For people with ASCVD, 32% of patients aged 50-59 had a current prescription, while only 13% of those aged 80-89 had one. Similar age-related trends were seen in people with heart failure or high CVD risk.

“As people age, they are more likely to be on a larger number of medications than before. There may be a concern of side effects or interactions of the new drug with their stable portfolio of medicines. Patients may also resist switching to a new medication if the current diabetic treatment is working,” said Dr Shah.

Health inequalities were also observed by gender: About one in four women (23%) with comorbid heart failure had a prescription, compared to approximately one in three men (35%). A similar pattern was seen in women with ASCVD or high CVD risk.

Figure 2: Proportion of type 2 diabetes patients with an SGLT-2 inhibitor prescription in England, by gender and comorbidity

Note: Data as of 1 September 2023, 18 months after the recommendations were published. Includes people aged 18 or over. Source: NICE analysis of Clinical Practice Research Datalink (CPRD) data.

UTIs and thrush are common side effects of this medication, which may contribute to the lower use in women.

“SGLT-2 inhibitors are not approved for use in pregnant or breastfeeding patients, which can hinder prescribing for women,” said Dr Rahul Mohan, a GP at West Bridgford Medical Centre in Nottingham and Secretary of the Primary Care Diabetes Society.

A lower percentage of Black or Black British people than White people had a prescription. This was the case across all groups (ASCVD, CHF or high CVD risk) but the differences were smaller than by age or gender.

Among patients with high CVD risk, 16% of Black or Black British people were prescribed the medicines, compared with 21% of White people.

Figure 3: Proportion of type 2 diabetes patients with an SGLT-2 inhibitor prescription in England, by ethnicity and comorbidity

Note: Data as of 1 September 2023, 18 months after the recommendations were published. Includes people aged 18 or over. Source: NICE analysis of Clinical Practice Research Datalink (CPRD) data.

Additional modelling found that people aged 70-79 with type 2 diabetes and ASCVD had less than half the odds of a current prescription compared to those aged 18-39, and women had 2/3rds the odds of men. Black and Black British people had 22% lower odds of having a current prescription than White people. In addition, this analysis found that patients living in the most deprived areas had about 15% lower odds of a prescription than those from the least deprived areas. Similar results were observed in the model for patients with type 2 diabetes and CHF.

“People living in areas of higher deprivation and certain ethnic minority groups are what we call ‘seldom heard’ groups. They tend to visit their doctor or attend their appointments less often, which could explain the disparities,” said Dr Soon Song, Consultant Physician and Diabetologist at Sheffield Teaching Hospitals NHS Foundation Trust and committee member for the type 2 diabetes guideline update.

“We need to devise a more innovative way of providing services to reach these groups, educating and empowering patients to help them follow their treatment plan,” he added.

Encouraging appropriate prescribing

The findings informed the medicines update of the type 2 diabetes guideline, which was published today.

Dr Shah said: “We [NICE’s independent committee] have made a research recommendation because we want to understand with some level of granularity exactly the reasons for this inequality. I think that research will generate answers that we can then use to inform future recommendations.”

The evidence from our analysis is clear. There are prescribing gaps that need to be addressed. The guideline update published today will help to increase equitable uptake of SGLT-2 inhibitors, which we know can prevent serious health complications. NICE used this real-world evidence to inform these research recommendations, which will result in powerful data to help further reduce health inequalities.

Professor Jonathan Benger, chief medical officer at NICE

Several initiatives at a local level aim to encourage appropriate prescribing of SGLT-2 inhibitors.

Dr Mohan has led training sessions in different areas of his Integrated Care Board (ICB), across Nottingham and Nottinghamshire: “The audience is a mixture of GPs, practice nurses, healthcare assistants, and other primary care professionals. Some improvement in the SGLT-2 inhibitor prescribing has happened, but much more needs to be achieved.”

Primary care learning events on this topic have also taken place in other regions, including South Yorkshire and London.

Experts also highlighted the importance of using SGLT-2 inhibitors safely. They are associated with a risk of diabetic ketoacidosis (DKA), a condition that is preventable and can be fatal.

Dr Song said: “We need to use SGLT-2 inhibitors more, but we must do so safely to minimise the risk of diabetic ketoacidosis. People on a low-calorie diet or those going for surgery should avoid this medication. Patients with any acute illness should stop taking it until they are fully recovered.”

Conjunctivitis Outbreak Sweeps Malakand, Over 1,600 Cases Reported in Batkhela

Locals report that the outbreak has not only disrupted attendance in schools and offices but has also caused anxiety as the infection spreads from one family member to another within households.

The district of Malakand is witnessing a rapid spread of conjunctivitis (commonly known as pink eye), with hundreds of people already affected. Medical experts have warned that this viral infection is highly contagious, transferring quickly from one person to another, and the situation is becoming increasingly alarming.

According to health officials, Batkhela and Thana Baizai tehsils are among the most affected areas, where dozens of patients are visiting hospitals and private clinics daily for treatment. 

Locals report that the outbreak has not only disrupted attendance in schools and offices but has also caused anxiety as the infection spreads from one family member to another within households. Healthcare workers are advising citizens to strictly follow preventive measures to contain the outbreak.

Iqbal Hussain, Media Coordinator at DHQ Hospital Batkhela, stated that 1,633 patients have so far been brought to the hospital for conjunctivitis treatment. 

Hospital records indicate that the highest number of cases has been reported from Batkhela, while significant numbers are emerging from Aman Dara, Allah Dhand, Thana Tota Kan, Peeran, Agra, and Badwan. Iqbal Hussain confirmed that eye care facilities are available at the hospital and patients are continuously being instructed on precautionary measures to curb the further spread of the virus.

Also Read: ‘I Could Be Next’: Mahira Khan Reveals Fear During KP Floods, Stands with Victims

Sharing his ordeal, a patient named Peer Rawad Shah said, “My younger brother was the first to get infected, but within a few days, the disease spread throughout our household. Now, our entire family is suffering from burning eyes, constant tearing, and swelling, which has disrupted our daily life and created a lot of stress. Even performing daily chores has become difficult, and educational activities have been severely affected.”

He added that the most painful moments occur when bending down. “Especially during prayers, the pain becomes unbearable in the prostration position, making it hard to perform religious obligations,” he said.

District Eye Specialist Dr. Jamal Azeem explained that conjunctivitis is a viral disease that can easily spread from one person to another. While there is no need to panic, he emphasized the importance of precautionary measures. He elaborated that the disease is mainly transmitted through physical contact such as handshakes, close interactions in crowds or gatherings, or sharing drinking glasses with an infected person.

Dr. Azeem further stated that conjunctivitis is essentially a viral infection and recovery time varies from case to case. Some patients recover within a week to 10 days, while in other cases, symptoms may persist for up to 20 to 25 days. He advised patients not to rub their eyes and to maintain physical distance from others to prevent further transmission.

Environmental Sciences lecturer Tahir Ali attributed the spread to recent floods, heavy rains, and poor sanitation conditions. “Unhygienic environments, open garbage dumps, and poor drainage systems accelerate the spread of eye infections. Therefore, maintaining cleanliness should be a top priority,” he said.

Zaid Bacha, a teacher at Government Primary School Jala Wanam, noted that the outbreak has affected academic activities as many students have contracted the infection, causing concern among parents and teachers. However, he added that since schools are already closed until August 25 due to flood and rain concerns, this temporary break might help reduce the chances of the disease spreading further.

Introduction

Out-of-hospital cardiac arrest (OHCA) is a life-threatening medical emergency that requires rapid and efficient resuscitation. Evidence strongly supports the role of a multidisciplinary team in providing thorough post-resuscitation care after the return of spontaneous circulation (ROSC).¹ Despite advancements in treatment protocols, mortality rates remain elevated, and the majority of patients are discharged with unfavorable neurological outcomes.²

OHCA survivors frequently develop multiple organ failure resulting from ischemia-reperfusion injury, a condition collectively termed post-cardiac arrest syndrome (PCAS). This syndrome considerably impacts mortality rates.³ PCAS progresses through distinct phases, with the early and intermediate phases, spanning up to 72 h after ROSC,⁴ representing a critical period for intensive care monitoring and intervention. Previous studies have largely concentrated on the pathophysiology of PCAS with respect to the heart and brain, leaving the respiratory system relatively underexplored. Current guidelines emphasize general respiratory care measures, such as maintaining normal carbon dioxide levels and oxygen saturation levels above 94%.¹ Emerging evidence has begun to shed light on the potential role of respiratory dynamics in post-cardiac arrest outcomes. A study involving unconscious OHCA patients who underwent mechanical ventilation and temperature management in the first 72 h identified respiratory rate and driving pressure as key predictors of 6-month mortality.⁵ Similarly, animal models of post-cardiac arrest have demonstrated an elevated respiratory drive but reduced tidal volumes compared to non-arrested controls.⁶ This suggests that a mismatch between respiratory drive and ventilation, referred to as ventilation-drive coupling, potentially contributes to alveolar injury.

Modern ventilators can automatically evaluate the respiratory drive using measurements such as P0.1, which reflects the airway pressure recorded during the first 100 ms of an end-expiratory occlusion. P01 is considered a reliable metric because of its independence from respiratory mechanics and its minimal influence on patient responses during brief occlusions. For patients with critical illness, a P0.1 range of 1.5–3.5 cmH₂O is generally considered acceptable.⁷ However, the optimal range of P0.1 in patients following OHCA remains unclear, and its potential utility as a prognostic tool in this population remains unclear. This study aimed to investigate respiratory drive variations in comatose patients following OHCA and to examine their association with survival.

Materials and Methods

Study Design

This prospective cohort study included patients with OHCA admitted to Songklanagarind Hospital between October 2022 and October 2024. Enrollment was conducted within 24 h of hospital admission, and participants were followed up until discharge.

Adults aged 18 years or older diagnosed with OHCA and achieving ROSC for at least 20 min were included. Eligibility required unconsciousness with a Glasgow Coma Scale (GCS) score below 8 and admission to the medical intensive care unit (MICU) or cardiac care unit (CCU) within 24 h of ROSC. Additionally, patients were required to be on a ventilator capable of measuring P0.1 and were expected to require mechanical ventilation for over 24 h.

The following patients were excluded: terminally ill patients receiving palliative care, individuals on extracorporeal membrane oxygenation, pregnant patients, and those in a pre-cardiac arrest vegetative state or coma.

This research acquired approval from the Ethics Committee, Faculty of Medicine, Prince of Songkla University (REC 65–310-14-4) and adhered to the guidelines established in the Declaration of Helsinki. Informed consent was provided by the patient’s nearest relative or designated individual promptly and subsequently by the patient upon regaining capacity. This study was registered in the Thai Clinical Trials Registry (TCTR20221010003).

Setting

Songklanagarind Hospital is an 850-bed tertiary referral center in Thailand. It includes a 12-bed MICU and 6-bed CCU staffed by 5 full-time intensivists and 4 cardiac interventionists available throughout the week. Patients who experienced cardiac arrest were treated following local policy guidelines, which aligned with the American Heart Association 2020 recommendations.¹ For comatose patients, targeted temperature management (TTM) was implemented based on physician discretion. Coronary angiography (CAG) and percutaneous coronary intervention (PCI) were performed by cardiac interventionists when considered clinically appropriate. Neuroprognostication utilizes a multimodal approach, with brain-death declarations made by a team of neurologists.

Data Collection

Electronic medical records provided information on baseline clinical variables, including sex, age, height, weight, underlying diseases, admission date, etiology of cardiac arrest, no-flow time, cardiopulmonary resuscitation (CPR) duration (from documented start to stop times), time to ROSC, and initial cardiac rhythm. If no height record was available in the medical chart, the height was estimated from the photograph of the patient’s Thai national identity card, which includes a height scale. The predicted body weight (PBW) was then calculated using the standard formula: 50 + 0.91 × (height in centimeters – 152.4) for males and 45.5 + 0.91 × (height in centimeters – 152.4) for females.⁸ The height used for PBW calculation was primarily obtained from medical records; when unavailable, height from the identity card was used as an alternative.

Physiological and laboratory parameters were documented to compute the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, utilizing the worst values recorded within the initial 24 h following admission.⁹ Additional data included vital signs, respiratory and ventilator parameters, arterial blood gas results, sedation, and continuous neuromuscular blocking agent (NMBA) use, Richmond Agitation Sedation Scale (RASS), GCS at 24, 48, and 72 h post-admission, and the Cerebral Performance Category (CPC) score at the time of hospital discharge. Circulatory shock was defined as a systolic blood pressure < 90 mmHg for at least 30 min or the need for supportive measures to maintain a systolic pressure ≥ 90 mmHg, accompanied by signs of end-organ hypoperfusion, such as cool extremities, urine output < 30 mL/h, and a heart rate > 60 beats/min.¹⁰ The RASS, ranging from +4 (combative) to −5 (unarousable), assigns a score of 0 for patients who are alert and calm.¹¹ For patients on mechanical ventilation, a GCS verbal score of 1 is assigned.¹² The CPC scale ranges from 1, indicating good cerebral performance, to 5, representing brain death.¹³

P0.1 values and respiratory parameters, including arterial blood gas measurements, were obtained 24, 48, and 72 h post-admission, with a permissible variation of up to 3 h. P0.1 was measured with a Dräger Evita XL ventilator (Dräger Medical, Lübeck, Germany) through an automated maneuver performed four times at 1-min intervals, and the average value was calculated.¹⁴ At the time of measurement, all patients received full ventilatory support in either pressure-controlled or volume-controlled mode. The ratio of minute ventilation (VE) to P0.1 was used to evaluate the ventilation-drive coupling.^15,16

Primary Outcome

The primary outcome was the in-hospital mortality rate. Non-survivors were classified by the cause of death into one of the following categories: withdrawal of care attributed to neurological status, withdrawal of care due to comorbid conditions, refractory hemodynamic shock, refractory respiratory failure, or sudden cardiac death.¹⁷

Statistical Analyses

In a study by Rittayamai et al,¹⁸ the mean P0.1 value for critically ill patients, including those who had experienced cardiac arrest, was 2.6 ± 1.7 cmH₂O. For non-survivors, P0.1 value was estimated to be 1 cmH₂O lower than the reported mean value of 2.6 cmH₂O, resulting in an estimated P0.1 of 1.6 cmH₂O. This estimation, along with a standard deviation of 1.70, alpha level of 0.05, and statistical power of 80%, indicated that a sample size of 23 was required to test the population mean.¹⁹ To account for a potential 30% dropout rate, the adjusted sample size was calculated to be 30 participants.

The Shapiro–Wilk test was used to assess the normality of the continuous data. Continuous variables are reported as medians with interquartile ranges (IQR), and the Mann–Whitney U-test was used for comparisons. Categorical variables are summarized as frequencies and percentages, and comparisons were performed using Fisher’s exact test. Repeated measurements over time were analyzed using generalized estimating equations.

The odds ratio of in-hospital mortality for P0.1 values considered acceptable in mechanically ventilated patients—which were 1.5–3.5 cmH₂O at 24, 48, and 72 h—was assessed using univariable logistic regression. Multivariable analysis was adjusted for established survival predictors, including witnessed arrest, bystander CPR, shockable rhythms, and prehospital ROSC.²⁰

The association between P0.1 and variables such as VE, tidal volume, compliance, and RASS over time were analyzed using linear mixed models. Variance explained by fixed effects was measured through Marginal R², while Conditional R² captured the combined contribution of fixed and random effects.^21,22 A significance level of P < 0.05 was applied to all statistical analyses. Data were analyzed using STATA version 16 (StataCorp LP, College Station, TX, USA).

Results

Among 59 post-cardiac arrest patients, 29 were excluded, and 30 patients were prospectively evaluated (Figure 1). Of the 16 patients who died, the causes of death included withdrawal of care due to neurological status (8/16, 50%), withdrawal of care due to comorbid conditions (3/16, 18.8%), refractory respiratory failure (2/16, 12.5%), sudden cardiac death (2/16, 12.5%), and refractory hemodynamic shock (1/16, 6.2%).

The baseline characteristics of patients, categorized by their survival status at hospital discharge, are presented in Table 1. Most patients were male (80%), with a median age of 61 years. While 86.7% experienced witnessed arrest, only 53.3% received bystander CPR. The most common initial cardiac rhythm was shockable rhythm. The median no-flow time was 12 min. Most surviving patients had poor neurological outcomes at discharge, with a median CPC score of 4.

Table 1 Baseline Characteristics of the Patients

Regarding respiratory drive and ventilator parameters, the P0.1 values in the survival group were higher than those in the non-survival group (1.34 vs 0.56, 1.63 vs 0.16, and 1.28 vs 0.93 cmH₂O), but these differences did not achieve statistical significance (Table 2 and Figure 2). The tidal volume per predicted body weight was significantly lower in the survival group during the first 24 h after admission (P = 0.034). Ventilation-drive coupling (VE/P0.1) was generally lower in the survival group than in the non-survival group, although this difference was not statistically significant (Table 2).

Table 2 Comparison of Respiratory Drive, Respiratory Parameters, and Sedation Depth Between Survivors and Non-Survivors Over a 72-h Period

Figure 2 P0.1 trend in the survival and non-survival groups during the 72-h period post-admission. Abbreviations: Q, quartile.

To evaluate the relationship between P.01 values and mortality, patients with P0.1 values of 1.5–3.5 cmH₂O were analyzed. Measurements taken 24 h post-admission revealed an independent association with reduced in-hospital mortality (adjusted OR 0.043, 95% CI, 0.003–0.588; P = 0.018) (Table 3). When plotting P0.1 values over time for the survival and non-survival groups, patients in the survival group predominantly had P0.1 values within the range of 1.5–3.5 cmH₂O. In contrast, patients in the non-survival group mostly had values below 1.5 cmH₂O, falling outside the acceptable range (Figure 3).

Table 3 Odds Ratio of in-Hospital Mortality for P0.1 Values (1.5–3.5 cmH2O)

Figure 3 P0.1 values for surviving patients (A) and non-surviving patients (B).

Linear mixed model analysis showed no statistically significant associations between P0.1 and VE, tidal volume, lung compliance, or RASS over time (Table 4). These findings indicated that changes in P0.1 were not strongly correlated with these parameters during the study period.

Table 4 Linear Mixed Model Association Between P0.1 and Ventilatory Parameters Over Time

Discussion

This study investigated the alteration of the respiratory drive, particularly P0.1, in comatose patients with OHCA and its correlation with sedation levels, ventilation parameters, and patient outcomes. The findings revealed a trend toward higher P0.1 values in survivors than in non-survivors. Additionally, survivors had significantly lower tidal volumes per predicted body weight than non-survivors. Notably, P0.1 levels within the optimum range of 1.5–3.5 cmH₂O were significantly associated with reduced in-hospital mortality.

To the best of our knowledge, this study is the first to investigate the respiratory drive and its association with outcomes in post-cardiac arrest patients. P0.1 serves as a reliable, non-invasive indicator of respiratory drive, reflecting the neural effort involved in breathing. Previous studies have identified a normal range of 1.5–3.5 cmH₂O for P0.1 in mechanically ventilated critically ill patients, with deviations indicating abnormal respiratory drive.²³ Our study supports these findings by demonstrating an association between P0.1 values within this range and reduced in-hospital mortality, underscoring its potential as a prognostic tool in this patient population.

The concept of ventilation–drive coupling, which examines the relationship between respiratory drive and delivered ventilation, holds particular significance in critically ill patients. Post-cardiac arrest patients frequently exhibit a mismatch between the signals from the respiratory center and the mechanical ventilation administered, resulting in suboptimal ventilation strategies. For instance, animal studies, such as the porcine model by Yang et al,⁶ have shown that post-arrest animals require a higher respiratory drive to achieve the same level of ventilation as their non-arrested counterparts. This uncoupling can contribute to alveolar injury and adverse outcomes. In our study, the survivors demonstrated higher P0.1 values, reflecting a stronger respiratory drive, although the differences were not statistically significant. This discrepancy might stem from variations in the study techniques and settings. Nonetheless, the observed trend suggests that robust respiratory effort may be beneficial in the post-arrest setting.

The lower tidal volumes observed in survivors, relative to the predicted body weight, align with the principles of lung-protective ventilation aimed at reducing ventilator-induced lung injury. This strategy, commonly used to manage acute respiratory distress syndrome, may also benefit post-cardiac arrest patients. By limiting tidal volumes, the risks of barotrauma and volutrauma can be minimized, potentially reducing mortality.^24,25 Our findings suggest that survivors may have received more effective lung-protective ventilation, as evidenced by their lower tidal volumes. Therefore, P0.1 could serve as a critical metric for optimizing ventilator settings by ensuring that respiratory drive is adequately supported without over- or under-assisting the patient’s respiratory efforts, thereby improving outcomes.

This study also critically examined the relationship between sedation levels, as assessed by the RASS and P0.1. Most patients were deeply sedated, with RASS scores between −5 and −4, indicating unresponsiveness. Despite this, we observed higher P0.1 values among survivors, suggesting a stronger respiratory drive, even under deep sedation. This finding is consistent with previous research conducted by Dzierba et al,²⁶ which demonstrated minimal influence of deep sedation on P0.1 in critically ill patients. The lack of a statistically significant association between RASS scores and P0.1 in our study highlights the reliability of P0.1 as a measure of respiratory drive, even in deeply sedated patients.

The interaction between sedation and the respiratory drive is complex. While deep sedation can suppress spontaneous respiratory effort, P0.1 captures the neural drive to breathe, which may persist despite sedation. In our study, the higher P0.1 values observed in survivors may indicate better overall physiological recovery, particularly in the brainstem, which regulates autonomic breathing functions. This observation may explain why survivors exhibited a higher respiratory drive than non-survivors, even though their sedation levels were similar to those of non-survivors. It is also plausible that elevated respiratory drive reflects a compensatory response to underlying respiratory or metabolic derangements.²⁷ These findings suggest that P0.1 serves as a reliable prognostic tool independent of sedation depth, offering important insights into a patient’s respiratory status and recovery potential.

In addition to sedation, the use of NMBAs may also influence the interpretation of P0.1. P0.1 reflects neural respiratory drive and requires intact neuromuscular transmission for accurate measurement. Continuous infusion of NMBAs, commonly used in post-cardiac arrest patients to suppress shivering during targeted temperature management and manage ventilator asynchrony, can reduce inspiratory muscle activity and may eventually lead to low or absent P0.1 values despite preserved central respiratory drive.¹⁴ Therefore, caution is warranted when interpreting low P0.1 values in patients receiving neuromuscular blockade.

We explored various ventilatory parameters, including VE, lung compliance, and tidal volume, to evaluate their relationships with P0.1. Although no statistically significant associations were identified, the findings suggested that P0.1 remained relatively independent of respiratory mechanics. This independence can be attributed to three factors: (1) P0.1 was assessed at end-expiratory lung volume, making the airway pressure drop unaffected by lung or chest wall recoil; (2) maneuver was performed without airflow, eliminating the influence of flow resistance on the measurement; and (3) lung volume was preserved during occlusion, minimizing the impact of vagal volume-related reflexes and the force-velocity relationships of respiratory muscles.¹⁴

Various ventilatory parameters, such as driving pressure, respiratory rate, and mechanical power, have been studied in relation to outcomes in post-cardiac arrest patients.²⁸ Among these parameters, mechanical power has emerged as a particularly noteworthy metric, as it integrates multiple aspects of ventilator-induced stress. Mechanical power has been associated with clinical outcomes in post-cardiac arrest populations; however, its calculation requires specific inputs, such as driving pressure and respiratory rate, and its clinical interpretation can be complex. To date, no randomized controlled trials have established a definitive cutoff value for mechanical power in this specific patient group.²⁸

The characteristics of our patients also reflect the typical cardiac arrest population in Thailand. Although 86.7% of arrests were witnessed, only 53.3% of patients received bystander CPR, which is consistent with previous national surveys.²⁹ Interestingly, the proportion of patients with prehospital ROSC was lower in the survival group (7.1%) than in the non-survival group (31.2%). While this finding appears to contradict the established literature,²⁰ it likely reflects a selection bias and the limitations of our small sample size. Additionally, differences in the quality and timeliness of pre-hospital emergency care in our setting may have contributed to this unexpected finding.

This study has some limitations. First, this was a single-center observational study with a limited sample size, which restricted its ability to establish causal inferences. Although advanced methods such as marginal structural models³⁰ may help address time-varying confounding in future research, our sample size was insufficient for causal modeling. Second, most post-arrest patients experienced prolonged no-flow times, with approximately 53% receiving bystander CPR, both of which could potentially influence neurological outcomes. Third, our focus was exclusively on patients with OHCA; the applicability of P0.1 in in-hospital cardiac arrest warrants further exploration, as these cases differ in characteristics and prognosis. Fourth, approximately one-quarter of patients received neuromuscular blockade, which may suppress inspiratory effort and lead to falsely low P0.1 values. Lastly, while we used P0.1, measured automatically by a specific ventilator, studies suggest that measurements from different machines are interchangeable.³¹

Conclusion

This study indicated a trend of higher respiratory drive in survivors than in non-survivors. A P0.1 value between 1.5 and 3.5 cmH₂O within the first 24 h was independently linked to reduced mortality. Survivors also demonstrated lower tidal volumes than non-survivors did. Given its simple measurement, P0.1 may serve as a neuroprognostic tool in patients following cardiac arrest. Further studies are needed to confirm these findings.

Data Sharing Statement

The data used to support the findings of this study are available from the corresponding author upon request.

Ethics Approval and Consent to Participate

This research was authorized by the Human Ethics Committee, Faculty of Medicine, Prince of Songkhla University, and was carried out in line with the Declaration of Helsinki (REC 65-310-14-4). Informed consent was provided by the patient’s nearest relative or designated individual promptly and subsequently from the patient upon regaining capacity.

Acknowledgments

We extend our sincere gratitude to the MICU and CCU nurses for their invaluable support and assistance in facilitating this research. We would like to thank Editage (www.editage.com) for English language editing.

The abstract of this paper was presented at the European Resuscitation Council Congress – Resuscitation 2024 as a poster presentation with interim findings. The abstract was published in the Poster Presentation section of Resuscitation (DOI: 10.1016/S0300-9572(24)00691-9).

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

The Faculty of Medicine, Prince of Songkla University provided research funding to support this study (grant number: 65-310-14-4).

Disclosure

The authors declare that they have no competing interests in this work.

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