Le Huy Hoang Nguyen, Thi Ngoc Anh Pham, Cong Tuan Trinh, Ha Ngoc The Than, Ngoc Hoanh My Tien Nguyen, Huy Tung Pham
1School of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Address: 217 Hong Bang Street, Cho Lon Ward, Ho Chi Minh City, Vietnam
2Department of Geriatrics and Palliative Care, University Medical Centre Ho Chi Minh City, Address: 217 Hong Bang Street, Cho Lon Ward, Ho Chi Minh City, Vietnam
3Department of Radiology, Thong Nhat Hospital, Address: 1 Ly Thuong Kiet Street, Tan Son Nhat Ward, Ho Chi Minh City, Vietnam
*Corresponding author: Nguyen Le Huy Hoang, MD, MSc, Position: cardiologist and geriatrician, Institution: School of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Address: 217 Hong Bang Street, Cho Lon Ward, Ho Chi Minh City, Vietnam, Phone number: +84 946 639 943, Fax: not available, Email: lucian.nguyen{at}icloud.com
medRxiv preprint DOI: https://doi.org/10.1101/2025.10.21.25337981
Posted: November 08, 2025, Version 2
Copyright: Email: phamthingocanh798{at}gmail.com, tringcongtuan{at}gmail.com, the.thn{at}umc.edu.vn, tien.nnhm{at}umc.edu.vn, tunghuy516{at}gmail.com
Abstract
In heart failure (HF), management priorities are shifting to include health-related quality of life, measured by patient-reported outcome measures (PROMs) like the Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12). While left ventricular ejection fraction (LVEF) guides therapy, it correlates poorly with patient-reported symptoms (the ‘LVEF paradox’). The contributions of systemic factors, such as New York Heart Association (NYHA) functional class, comorbidity, and frailty, remain poorly quantified against LVEF in older, ambulatory patients. This analytical, cross-sectional, observational study aimed to identify independent predictors of patient-reported health status. We enrolled 150 consecutive patients aged 60 years or older with chronic HF from a single cardiology centre. The primary outcome was the KCCQ-12 overall summary score. A multivariable linear regression model quantified associations of LVEF, NYHA functional class (I-II vs. III-IV), Charlson Comorbidity Index (CCI), frailty (Clinical Frailty Scale 5 or higher), age, and sex with KCCQ-12 scores. The cohort (median age 75 years) had a median KCCQ-12 score of 68, which did not differ significantly across HF phenotypes. The model explained 76.7% of the variance (R-squared = 0.767). Significant independent predictors of poorer health status were: advanced NYHA functional class (Coefficient −15.88), higher comorbidity burden (Coefficient −3.17 per 1-point CCI increase), and frailty (Coefficient −6.82) (all p < 0.001). Male sex predicted higher health status (Coefficient 3.21, p = 0.030). LVEF (p = 0.561) and age (p = 0.909) were not significant predictors. In this cohort, patient-reported health status was independently determined by systemic factors (NYHA class, comorbidity, and frailty), not LVEF. These findings underscore the ‘LVEF paradox’ and support a broader approach to HF assessment incorporating functional, comorbidity, and frailty measures to guide patient-centred management.
Introduction
Heart failure (HF) is a major global health issue, affecting over 64 million people, with its prevalence increasing due to ageing populations.1,2 Consequently, management priorities have expanded from reducing mortality to improving functional capacity and health-related quality of life (HRQoL).3–5 Clinical guidelines now recommend routine assessment of health status using validated patient-reported outcome measures (PROMs).4,5 The Kansas City Cardiomyopathy Questionnaire (KCCQ), including its validated 12-item short form Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12), is the benchmark disease-specific instrument for this purpose.6
Pharmacological therapy for HF is guided by left ventricular ejection fraction (LVEF), which classifies patients into distinct phenotypes.4 However, LVEF correlates poorly with patient symptoms, a phenomenon known as the ‘LVEF paradox’.7,8 Instead, patient-reported health status appears determined by systemic factors.9 These include clinician-assessed functional status, quantified by New York Heart Association (NYHA) functional class, comorbidity burden, and frailty, a multisystem syndrome of decreased physiological reserve, all of which are established predictors of poor outcomes and reduced HRQoL.10
The relative contributions of NYHA functional class, multimorbidity, and frailty to health status, particularly in comparison to LVEF, remain poorly quantified. This uncertainty is pronounced in older, ambulatory patients who are frequently underrepresented in clinical trials.11 However, no equivalent framework exists for predicting patient-reported health status, despite the established role of LVEF in guiding pharmacological therapy.12
This study aimed to identify and quantify the independent predictors of patient-reported health status, measured by the KCCQ-12, in older ambulatory adults with chronic HF. We hypothesised that factors reflecting systemic burden, including NYHA functional class, comorbidity, frailty, age, and sex, would predict KCCQ-12 scores independently of LVEF. The inclusion of sex is supported by established differences in HF pathophysiology and outcomes between men and women.13
Methods
Study design and participants
This analytical observational, cross-sectional study followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for reporting.14 The source population comprised consecutive patients aged 60 years or older with chronic HF attending Thong Nhat Hospital cardiology clinic between August 2024 and March 2025. Patients were excluded if they had acute decompensated heart failure or severe cognitive impairment preventing informed consent or questionnaire completion. Sample size calculations used two approaches: first, estimating mean KCCQ-12 scores with 5-point precision required 97 participants; second, Green’s formula for testing regression coefficients with six predictors required 110 participants. The final cohort of 150 participants exceeded both requirements. Ethical approval was obtained from the University of Medicine and Pharmacy at Ho Chi Minh City (2118/HDDD-DHYD) and Thong Nhat Hospital (121/2024/CN-BVTN-HDDD). The study complied with the Declaration of Helsinki,15 and all participants provided written informed consent before enrolment. Participant flow is shown in Figure 1.
Figure 1.Patient flow diagram
STROBE-style diagram illustrating the screening and enrolment process. Of 300 patients screened, 150 were excluded for reasons including incomplete questionnaire data (n = 51), missing left ventricular ejection fraction measurement (n = 30), acute decompensated heart failure (n = 23), cognitive impairment (n = 23), and declined participation (n = 15), resulting in a final cohort of 150 patients *Other reasons for exclusion included severe non-cardiac illness (n = 5) and planned relocation (n = 3).
Abbreviations: ADHF, acute decompensated heart failure; HFmrEF, heart failure with mildly reduced ejection fraction; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction.
Data collection and measurements
All procedures were completed during a single outpatient visit at Thong Nhat Hospital cardiology clinic. During the visit, trained staff conducted face-to-face interviews in Vietnamese and administered several validated instruments, including the Vietnamese KCCQ-1216,17 and the Clinical Frailty Scale (CFS),18 using structured case report forms. A cardiologist performed clinical assessments and assigned NYHA functional class.19 Data for the Charlson Comorbidity Index (CCI), N-terminal pro–B-type natriuretic peptide (NT-proBNP) levels, and baseline LVEF were abstracted from medical records. Staff received protocol training with competency assessments. Data were entered into an electronic case report form with validation checks. Analyses used anonymised data.
Variable definitions
Primary and secondary outcomes
Participants completed a validated Vietnamese version during a baseline visit before clinical assessment to minimise information bias. Trained staff provided standardised instructions and scored the questionnaires according to the official manual, blinded where feasible to clinical data. Scores for the four domains (physical limitation, symptom frequency, quality of life, and social limitation) were transformed to a 0–100 scale, with higher scores indicating better health. The primary outcome was the overall summary score, calculated as the mean of the available domain scores. The individual domain scores served as secondary outcomes. We prespecified a minimal clinically important difference of 5 or more points for the overall score, which is a conservative estimate consistent with published data.6
Key explanatory variables
LVEF was derived from echocardiograms performed within three months of the study visit. Functional capacity was defined by the NYHA functional class, which we dichotomised for regression into mild (classes I–II) and advanced (classes III–IV) limitation.19 The CCI represented the comorbidity burden.20 While treated as a continuous variable in the primary regression model, for descriptive analyses, scores were stratified into low burden (scores 3–5), medium burden (6–7), and high burden (≥8). This categorisation was based on the distribution of scores within the study cohort rather than pre-specified a priori cut-points. Frailty was defined as a score of 5 or higher on the 9-point CFS. This threshold is widely used and validated for identifying at least mild frailty in older adults and is associated with adverse outcomes.
Other covariates
For descriptive purposes, we classified HF phenotype according to 2021 European Society of Cardiology guidelines based on LVEF: reduced (HFrEF; ≤40%), mildly reduced (HFmrEF; 41–49%), or preserved (HFpEF; ≥50%).4 In the primary model, LVEF was treated as a continuous variable. We recorded demographics (age, sex), lifestyle factors (smoking, alcohol use), and clinical measurements, including body mass index, averaged resting heart rate and blood pressure, and NT-proBNP concentrations. Polypharmacy was defined as using five or more concurrent medications. We also counted the number of prescribed guideline-directed HF drug classes (range 0–4). Functional status was assessed using the Katz Index of Independence in Activities of Daily Living,21 and nutritional status was evaluated with the Mini Nutritional Assessment Short-Form.22
Statistical analysis
All statistical analyses were performed using Stata version 19.5 (StataCorp, College Station, Texas, USA). Categorical variables were summarised as counts and percentages. Continuous variables were assessed for normality using the Shapiro-Wilk test and visual inspection of histograms, then reported as either mean and standard deviation or median and interquartile range as appropriate.
The primary analysis employed a multivariable linear regression model to identify predictors of the KCCQ-12 overall summary score. To disentangle the effects of chronological age from the cumulative comorbidity burden, the model included both age and the CCI as separate continuous predictors. In addition to age and CCI, the model included the other key explanatory variables: LVEF, dichotomised NYHA functional class, and binary frailty status, along with sex. Results were expressed as adjusted regression coefficients with 95% confidence intervals, which were calculated using robust standard errors as a default method to account for potential heteroscedasticity. Model diagnostics involved assessing residual plots for homoscedasticity and normality, whilst variance inflation factors were used to evaluate multicollinearity.
For descriptive analyses, trends across ordered groups were tested using the Jonckheere-Terpstra test for continuous or ordinal outcomes and the Cochran-Armitage test for binary outcomes. KCCQ-12 scores were compared across categories using the Mann-Whitney U test for two-group comparisons and the Jonckheere-Terpstra test for three or more ordered groups.
Exploratory analyses involved stratifying the multivariable regression model by HF phenotype (HFrEF, HFmrEF, HFpEF) to examine whether associations between the predictor variables and KCCQ-12 scores differed across these subgroups. As the study was not powered for formal subgroup analyses, these results require cautious interpretation. Additional exploratory analyses examined whether the associations between predictor variables and health status differed between HFrEF and HFmrEF, with the full results provided in the Supplementary Material.
All analyses used complete-case data, meaning participants with missing data for any variable were excluded from that specific analysis, and the proportions of missing data are reported in the baseline characteristics table. A two-sided p-value below 0.05 was considered statistically significant. No adjustment was made for multiple comparisons in the exploratory analyses.
Results
Cohort and baseline description
The study cohort comprised 150 older adults with HF, who were categorised into HFrEF, HFmrEF, and HFpEF groups (Figure 1). The median age was 75 years, with patients in the HFpEF group being significantly older than those in the HFrEF group.
Geriatric syndromes such as malnutrition, frailty, and limitations in daily activities were prevalent across the cohort. Most patients had a high comorbidity burden and were on multiple medications. While the proportion of patients with a high comorbidity burden appeared to increase from the HFrEF to the HFpEF phenotype, this trend was not statistically significant. Common comorbidities included hypertension, coronary artery disease, and dyslipidaemia. Notably, the prevalence of atrial fibrillation increased significantly from the HFrEF to the HFpEF group (Table 1).
Table 1.Baseline characteristics by heart failure phenotype
Among patients with HFrEF, the prescription of foundational therapies was common. The vast majority of these patients were on combination therapy, with most receiving two or three different drug classes (Supplementary Table 1).
Primary endpoint
The median KCCQ-12 summary score was 68 and did not differ significantly across HF phenotypes. However, health status was significantly poorer in certain subgroups, including patients in advanced NYHA functional classes, those with frailty or an increasing comorbidity burden, and females (Table 2).
Table 2.KCCQ-12 overall summary scores by subgroup
A multivariable linear regression model was developed to identify independent predictors of health status, explaining 76.7% of the variance in KCCQ-12 scores. The analysis identified higher NYHA functional class, increased comorbidity burden, and the presence of frailty as significant predictors of poorer health status. In contrast, male sex was associated with a higher health status score. After adjusting for other variables, age and LVEF were not found to be significant predictors (Table 3 and Figure 2).
Figure 2.Independent predictors of KCCQ-12 overall summary score
The plot shows the adjusted regression coefficients (blue dots) and 95% confidence intervals (horizontal lines) from the primary multivariable linear regression model (n = 150). The dashed vertical line indicates a coefficient of zero, representing no effect. Coefficients and confidence intervals that do not cross this line are statistically significant.
Abbreviations: CCI, Charlson Comorbidity Index; LVEF, left ventricular ejection fraction; NYHA, New York Heart Association.
Table 3.Multivariable linear regression of health status
Diagnostic checks confirmed the validity of the model’s assumptions. There was no evidence of multicollinearity among the predictor variables (Supplementary Table 2). Visual inspection of the quantile-quantile plot confirmed that the residuals were approximately normally distributed (Supplementary Figure 2). Furthermore, a plot of residuals versus fitted values showed a random scatter, satisfying the assumption of homoscedasticity (Supplementary Figure 1).
Secondary endpoints
Consistent with the primary outcome, no significant trend was observed across HF phenotypes for any KCCQ-12 domain scores, including physical limitation, symptom frequency, quality of life, and social limitation. Subgroup analyses revealed consistent patterns of poorer health status, with patients in advanced NYHA functional classes, frail individuals, those with an increasing comorbidity burden, and females reporting significantly lower scores across all four domains (Table 4).
Table 4.KCCQ-12 domain scores by subgroup
Exploratory analysis
Multivariable linear regression models, stratified by HF phenotype, explained a substantial portion of the variance in KCCQ-12 scores for all three groups (Supplementary Table 3).
In both the HFrEF and HFpEF cohorts, advanced NYHA functional class, frailty, and higher comorbidity burden were independent predictors of poorer health status. For patients with HFpEF, male sex also predicted higher scores. In contrast, among patients with HFmrEF, only an increasing comorbidity score independently predicted lower health status.
A significant interaction was observed between comorbidity burden and phenotype when comparing HFrEF and HFmrEF, indicating that health status declined more steeply with increasing comorbidities in the HFmrEF group (Supplementary Table 5, Supplementary Figure 5).
Diagnostic checks for the stratified models confirmed that there was no evidence of multicollinearity and that the assumptions of homoscedasticity and normally distributed residuals were met (Supplementary Table 4, Supplementary Figure 3, Supplementary Figure 4).
Discussion
Principal findings of the study
In this study of 150 older, ambulatory Vietnamese adults with chronic HF, patient-reported health status was determined by systemic and functional factors, not LVEF. The multivariable analysis identified that a higher NYHA functional class, increased comorbidity burden, and frailty were independent predictors of poorer health status, while male sex was associated with a higher health status score. LVEF showed no association with KCCQ-12 scores. These results indicate that, in older adults, patient-reported health status aligns more closely with functional capacity, comorbidity burden, and physiological reserve than with ventricular function alone.
Contextualising health status assessment in geriatric heart failure
HF management increasingly emphasises functional capacity and HRQoL alongside mortality reduction, reflecting demographic changes in ageing populations. Clinical guidelines now recommend the routine use of PROMs like the KCCQ.3–5 These findings support incorporating geriatric assessment domains into routine HF evaluation. The results suggest that assessment of NYHA functional class, comorbidity, and frailty complements LVEF-based pharmacotherapy in addressing patient-centred outcomes recommended by current guidelines.
The left ventricular ejection fraction paradox and phenotype-independent health status
The dissociation between LVEF and patient-reported health status has been widely reported. For instance, a 2020 study that followed patients for 12 months found that the proportions of patients experiencing clinically significant changes in KCCQ scores did not differ between HFpEF and HFrEF groups, which suggests that the trajectory of health status is independent of the underlying phenotype.23 Similarly, research by Pokharel et al. found that the prognostic power of the current KCCQ score for predicting death and hospitalisation holds true for patients irrespective of their ejection fraction, supporting a combined analysis approach.24 LVEF reflects cardiac mechanics rather than systemic physiological reserve or patient-perceived well-being, consistent with previous evidence. The model identified systemic factors as the strongest predictors of health status. These findings suggest that patient-reported symptoms correlate with systemic factors (frailty, comorbidity) rather than with isolated measures of cardiac function.
The primacy of functional class and the ‘discordance’ problem
This finding relates to clinician-patient discordance, a recognised clinical challenge. A 2022 study found that one-third of HF patients experience a discordance between their clinician-assessed functional status and their self-reported health (KCCQ).25 The largest discordant group (27.4% of patients) comprised those with ‘discordant worse KCCQ’.25 These patients were classified as NYHA I/II but reported low KCCQ scores. This discordant group had an 80% higher risk of hospitalisation or death compared to the ‘concordant good’ group (low NYHA, high KCCQ).26 This pattern may be explained by frailty and comorbidity burden, which independently lower KCCQ scores even at lower NYHA functional classes. For instance, a patient assessed as low-risk (e.g., NYHA II) may still report very poor health if they are also frail and have a high comorbidity burden, as these factors independently lower health status scores. These are systemic factors that the NYHA classification alone does not capture. A three-predictor model incorporating NYHA functional class, CCI, and frailty may identify high-risk discordant patients more effectively than NYHA functional class alone.
The independent, dose-dependent impact of comorbidity burden
Health status declined progressively with increasing comorbidity burden, with median KCCQ scores decreasing from 81 in the low-burden group to 58 in the high-burden group. These findings are consistent with Yang et al. (2023), who reported associations between cardiac and non-cardiac comorbidities and reduced health status in HFrEF and HFpEF.27 The present study in a Vietnamese cohort demonstrates similar associations. The exploratory analysis suggested a hypothesis regarding the HFmrEF phenotype. In the HFmrEF group, only comorbidity burden remained a significant predictor, with a larger effect size than observed in other phenotypes. Interaction testing confirmed this difference was statistically significant, suggesting that comorbidity burden may be a particularly important determinant of health status in HFmrEF. The small HFmrEF sample size limits interpretation; however, the finding suggests that non-cardiac disease burden may substantially influence health status in this phenotype and warrants validation in larger studies.
Validating geriatric syndromes as a core determinant
This study demonstrates an association between frailty assessment and patient-reported outcomes (KCCQ). The CFS is a practical bedside assessment tool that has been shown to predict mortality independently in HF patients. Notably, frailty emerged as an independent predictor distinct from other clinical variables. Frailty is sometimes conflated with older age, multimorbidity, or higher NYHA functional class. The multivariable model did not support this assumption. After adjusting for age, comorbidity, and functional class, frailty remained an independent predictor. Frailty may capture loss of physiological reserve, a dimension not reflected in other clinical measures. Frailty assessment may provide prognostic information beyond that obtained from LVEF, NYHA functional class, and comorbidity indices.
Strengths and limitations
This study has several strengths, including its consecutive enrolment of patients from a dedicated ambulatory clinic, focus on older adults, and simultaneous modelling of clinician-assessed function, comorbidity, and frailty against a validated patient-reported outcome measure. The inclusion of a Vietnamese cohort adds valuable, non-Western data to a global literature dominated by North American and European studies. However, several limitations must be acknowledged. The single-centre design limits the generalisability of the findings, and the cross-sectional analysis can only establish association, not causation. The small size of the HFmrEF subgroup renders the finding of the CCI as the sole driver in this phenotype speculative and requires cautious interpretation. Finally, our model did not include other potentially important factors, such as socioeconomic status or psychosocial health.
Future perspectives and clinical implications
These findings have potential clinical implications. Clinically, LVEF alone may be insufficient for assessing, managing, or predicting patient-reported burden. Combined assessment of NYHA functional class, CCI, and CFS can be completed rapidly in clinical practice. This combined assessment may improve risk stratification and support individualised care planning consistent with guideline recommendations. For future research, longitudinal studies are required to determine if changes in frailty and comorbidity burden over time correlate with changes in KCCQ scores. Randomised clinical trials are warranted to test whether interventions targeting these factors improve KCCQ scores as a primary endpoint. The hypothesis that comorbidity burden is a predominant determinant of health status in HFmrEF requires validation in larger, multicentre cohorts.
Conclusion
The findings suggest that patient-reported health status is more closely associated with systemic factors than with cardiac mechanics alone. These findings support a broader approach to HF assessment beyond LVEF alone. Incorporating assessment of functional class, comorbidity, and frailty into routine cardiovascular care may improve identification of high-risk patients and support patient-centred management focused on quality of life.
Supporting information
Supplementary Material[supplements/337981_file02.docx]
Author contributions
All authors approved the final submitted version and any revised versions. Nguyen Le Huy Hoang is the guarantor with full data access and responsibility for accuracy.
Funding
None declared.
Declaration of interest
None declared.
Data availability statement
The data that support the findings of this study are available from the corresponding author, Nguyen Le Huy Hoang, upon reasonable request. The data are not publicly available due to restrictions protecting patient privacy.
Acknowledgements
We thank the patients and their families for participating in this study. We are grateful to the clinicians, echocardiography technologists, and nursing staff at Thong Nhat Hospital for their support with patient recruitment, clinical assessments, and echocardiographic evaluations. We acknowledge the use of the KCCQ-12, licensed from CV Outcomes, Inc. (Kansas City, Missouri, USA).
Footnotes
- Work performed at Thong Nhat Hospital, Ho Chi Minh City, Vietnam.
- This revision includes updates to the abstract, minor clarifications in the methods and discussion sections, and updated supplemental files.
This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/
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