To highlight the advantages of KCCQ in the assessment of heart failure in CKD patients, we compared it with the New York Heart Association (NYHA) classification and the Minnesota Heart Failure Questionnaire (MLHFQ), thereby supporting its practical application in this specific population.
KCCQ and NYHA functional classification
The NYHA functional classification remains widely used for assessing heart failure severity. However, it has several limitations. This classification system lacks standardized symptom assessment and objective scoring mechanisms, primarily relying on healthcare professionals’ subjective judgments based on limited clinical observations, which leads to significant assessment variability and poor reproducibility [38]. Moreover, the boundaries between NYHA classes are ambiguous, resulting in marked overlap, for example, up to 93% of patients in NYHA I and II have similar NT-proBNP levels [39]. Critically, the system shows limited prognostic accuracy: even among NYHA I patients, those with elevated NT-proBNP (≥ 1600 pg/mL) are at significantly increased risk of adverse events (Hazard Ratio [HR] = 3.43) compared to those with lower levels. These findings underscore that NYHA classification alone is insufficient for accurately identifying high-risk patients [39].
In CKD patients, NYHA classification limitations become more pronounced. Fluid retention and electrolyte imbalances in CKD cause symptom variability, while fatigue and dyspnea may overlap with or be masked by renal dysfunction manifestations, potentially leading to misclassification or severity underestimation [11]. In contrast, the repeatable KCCQ may better assess heart failure severity [40]. Studies show CKD patients with lower KCCQ scores face higher heart failure hospitalization risk [37], highlighting KCCQ’s potential value in this population.
Although direct comparisons in CKD populations are limited, several studies in patients with heart failure have demonstrated that the KCCQ provides superior prognostic value and sensitivity to clinical changes compared to the NYHA classification [40,41,42]. For example, a ≥ 5-point improvement in KCCQ score was significantly associated with reduced all-cause mortality (HR = 0.84), whereas changes in NYHA class were not (HR = 0.91) [40]. Furthermore, KCCQ has shown greater sensitivity in capturing symptom fluctuations and substantially overlaps with NYHA classes, challenging the latter’s ability to reflect patient quality of life [42]. Lastly, KCCQ scores may enable clinicians to assess patients’ health status with greater accuracy than the NYHA classification [37]. Substantial overlap in KCCQ scores across NYHA classes (e.g.73.6% between II and III, 88.3% between III and IV) suggests that NYHA class alone may not fully capture differences in quality of life. Therefore, combining KCCQ with NYHA assessment may allow for a more comprehensive evaluation of heart failure severity [40].
In conclusion, the KCCQ shows advantages over NYHA classification in assessing symptom burden and prognosis in heart failure patients. However, combined assessment of both provides a more detailed and precise evaluation of patient health status, leading to improved assessment accuracy.
KCCQ and MLHFQ
The MLHFQ and the KCCQ are two important tools for assessing health-related quality of life in heart failure patients [43]. They differ in terms of assessment content and predictive ability. A meta-analysis in 2023 found that the MLHFQ has limited evaluation of social functioning (such as work, social interactions, family life), and focuses more on assessing patients’ symptoms and emotional functions. In contrast, the KCCQ covers seven domains. Although there is no dedicated module to assess psychological well-being, certain questions can still provide insight into a patient’s mental state, allowing a more comprehensive assessment of their overall health from multiple angles, including symptoms, functional capacity and social functioning [44]. In patients with CKD, this multidimensional assessment is particularly important, as these patients often face complex symptoms and functional limitations [11]. The multidimensional assessment approach of the KCCQ may be able to capture changes in these symptoms more accurately [36].
Recent studies demonstrate that KCCQ outperforms MLHFQ in predicting adverse outcomes. Specifically, in patients with heart failure with reduced ejection fraction (HFrEF), KCCQ showed a significantly higher area under the curve (AUC) compared to MLHFQ for predicting mortality, transplantation, left ventricular assist device (LVAD) implantation, and rehospitalization (0.702 vs. 0.658; p < 0.001) [45]. In contrast, MLHFQ is more influenced by subjective symptom perceptions and less correlated with objective measures, while KCCQ demonstrates stronger associations with both quality of life and prognosis (HR = 2.34 vs. 1.56, P < 0.01) [43, 44]. Although dedicated data in CKD populations remain limited, these advantages suggest the KCCQ may have greater clinical value in this group.
Given the unique advantage of the KCCQ in assessing symptoms in heart failure patients, particularly in those with concurrent CKD, its clinical application becomes especially significant. The following will discuss the specific applications of KCCQ in CKD patients and its potential benefits.
The clinical application of KCCQ in CKD patients
Firstly, the KCCQ can facilitate the early detection and diagnosis of HF in CKD patients. Shlipak et al. [36] used the KCCQ to evaluate heart failure-related symptoms in 2833 CKD patients without diagnosed heart failure, finding that 25% demonstrated a significant symptom burden. In a 4.3-year follow-up study of 3,093 CKD patients, Mishra et al. [37] found those in the lowest quartile of KCCQ scores (≤ 74.5 points) had significantly higher risk of new-onset HF hospitalization compared to the highest quartile (> 99 points) (Odds Ratio [OR] = 3.30, 95% Confidence Interval [CI]: 1.66–6.52). These findings suggest that KCCQ could serve as an effective monitoring and predictive tool for this population. However, it is important to note that while KCCQ assesses general HF symptoms, it lacks specificity in CKD patients. Cardiac biomarkers significantly correlate with KCCQ scores in this population. Cross-sectional analysis revealed associations between KCCQ scores < 75 and elevated growth differentiation factor-15 (GDF-15) (OR = 1.42, 99% CI:1.19–1.68) and Galectin-3 (OR = 1.28, 99% CI:1.12–1.48). Longitudinally, GDF-15 (HR = 1.36, 99% CI:1.12–1.65) and NT-proBNP (HR = 1.30, 99% CI:1.08–1.56) predicted KCCQ decline to < 75 during follow-up [46]. While these biomarkers may detect early symptomatic changes in heart failure, they might also associate with lower KCCQ scores through non-cardiac mechanisms like inflammation, highlighting the need for comprehensive evaluation to distinguish cardiac and non-cardiac contributions to KCCQ scores in CKD patients.
Secondly, the KCCQ can monitor changes in heart failure symptom burden in CKD patients. Walther et al. [35] conducted a 5.5-year prospective study of 3044 CKD patients, identifying five distinct KCCQ trajectory groups: stable high-score (41.7%, average KCCQ 96, mildest symptoms), stable moderate-score (35.6%, average KCCQ 81, relatively mild symptoms), stable low-score (15.6%, average KCCQ 52, significant symptoms), declining score (4.9%, average decrease of 31 points), and improving score (2.2%, average increase of 33 points, highest baseline cardiovascular disease proportion). Clinical outcomes varied significantly among these groups, with incident heart failure rates of 3% in stable high-score, 11% in stable moderate-score, 12% in declining score, 10% in improving score, and 18% in stable low-score groups. These results demonstrate that KCCQ effectively reflects long-term heart failure symptom changes in CKD patients and is associated with prognosis, supporting its application in this population. However, limitations include the single-center design potentially limiting generalizability, and focus solely on KCCQ scores without considering other factors like treatments and comorbidities. Future studies should include larger, diverse populations and examine broader influencing factors.
Thirdly, the KCCQ scores are closely associated with multiple clinical variables in CKD patients. Walther et al. [47] assessed 3,426 CKD patients to investigate relationships between clinical variables and KCCQ scores. Body mass index (BMI) showed the strongest correlation, with scores highest at BMI 24.3 kg/m² and decreasing by up to 5 points when BMI reached 35.7 kg/m². Hemoglobin, blood glucose, and age were also associated with KCCQ scores.
Moreover, research further investigated the impact of CKD on the health status of heart failure patients. Packer et al. [48] studied heart failure with preserved ejection fraction (HFpEF) patients with obesity, finding those with concomitant CKD had more severe heart failure (34.7% vs. 16.2% NYHA class III symptoms and lower KCCQ scores: 51.8 ± 18.7 vs. 56.0 ± 17.7 points). Despite these differences, tripeptide improved KCCQ similarly across renal function groups. These findings demonstrate KCCQ’s effectiveness in assessing symptom burden and treatment outcomes in HFpEF patients with varying kidney function. Yang et al. [49] investigated the differential impact of comorbidities on health status as measured by the KCCQ in 12,172 heart failure patients. In this study, the prevalence of CKD was 33.9% in HFrEF patients and 48.7% in HFpEF patients. Their analysis demonstrated statistically significant reductions in KCCQ scores associated with CKD: a 4.2-point decrease in HFrEF patients and a 3.4-point decrease in HFpEF patients relative to their non-CKD counterparts. Notably, the magnitude of this effect was less pronounced than that observed with other comorbidities such as chronic obstructive pulmonary disease (COPD), angina, and anemia. This finding suggests that the influence of CKD on the KCCQ score may be relatively limited.
Notably, dialysis patients face elevated cardiovascular risks, with heart failure symptoms often masked by dialysis-related discomfort. Traditional biomarkers and imaging show limited sensitivity in ESRD patients, leading to delayed diagnosis [11, 50]. While KCCQ shows utility in CKD patients (Table 1), research in dialysis populations is virtually nonexistent, highlighting the need for multicenter studies to evaluate its effectiveness in this unique group.
Potential clinical applications of KCCQ in CKD: insights from limited evidence
Currently, cardiac function assessment in CKD patients still faces numerous clinical challenges, leading to frequent underestimation or delayed recognition of heart failure in this population, which seriously impacts treatment decisions and prognosis [11]. Against this background, KCCQ, as a heart failure assessment tool, may provide a novel research perspective for cardiac function evaluation in CKD patients. Therefore, although direct evidence is yet to be further accumulated, we draw upon mature guidelines from the heart failure field [51], and by deeply considering the unique pathophysiological characteristics of CKD populations and existing clinical research findings, propose recommendations for heart failure detection in CKD patients.
We recommend routine KCCQ assessment for all CKD patients, with specific considerations according to CKD staging (detailed recommendations are shown in Table 2). Special attention might be warranted for the following high-risk populations: (1) patients with cardiac dysfunction or structural abnormalities, including NYHA functional class III-IV, significantly reduced left ventricular ejection fraction (≤ 35%), cardiac structural abnormalities, and valvular heart disease; (2) patients with cardiovascular event history, including recent hospitalization for heart failure, history of myocardial infarction, and recent cardiac interventional procedures; (3) patients with unexplained decrease in exercise tolerance; (4) patients with comorbidities and metabolic disorders, including moderate to severe renal dysfunction (estimated Glomerular Filtration Rate [eGFR] < 45 mL/min/1.73 m²), hypertension and diabetes; (5) patients with lifestyle-related risk factors, including long-term smoking and obesity (BMI ≥ 30 kg/m²); (6) patients with treatment instability or fluctuating renal function. Consequently, due to their elevated risk of heart failure, these populations warrant closer monitoring and management [52]. It is worth noting that for patients with limited health literacy, we suggest using the simplified version of KCCQ-12 and providing enhanced support [53]. It is not recommended for patients with severe cognitive impairment, communication disorders or terminal illnesses [21].
Available evidence suggests that the value of KCCQ may be maximized when interpreted alongside objective indicators such as NT-proBNP, NYHA classification [11, 40, 46]. The literature indicates that a decrease in the KCCQ score (≥ 5 points) is generally considered clinically significant. This threshold was established in a 14-center study specifically designed to determine the clinical significance of KCCQ score changes, which found that a 0 to 5-point change reflects minimal important clinical changes in patients’ health status [24]. Studies exploring biomarkers in CKD patients have documented that NT-proBNP levels increase substantially as renal function declines. Jafri et al. [54] reported median plasma NT-proBNP concentrations ranging from 208 pg/mL at eGFR > 90 mL/min/1.73 m² to 8377–11,215 pg/mL at eGFR < 15 mL/min/1.73 m². Such data may provide context when interpreting KCCQ scores in CKD patients.
Comprehensive assessment of multiple parameters is conducive to achieving more accurate risk stratification and management optimization [11]. Especially for dialysis patients, it is necessary to jointly interpret the KCCQ score, dry body weight and biochemical indicators to accurately reflect the heart failure status [50]. Tables 2 and 3 outline implementation processes, with Table 2 assessments conducted by medical professionals and Table 3 elaborates on the implementation process of KCCQ in CKD-HF patients, which requires self-assessment by patients under the guidance of healthcare professionals [55]. This integrated approach by multidisciplinary teams aims to enhance risk identification and individualized treatment for CKD-HF patients, potentially improving prognosis and quality of life.