Abstract
Background
Frailty predicts mortality in older adults with heart failure, but its association with recurrent hospitalization, and its value relative to left ventricular ejection fraction (LVEF), remain poorly characterized in Asian populations.
Methods
We followed 150 consecutive Vietnamese outpatients aged 60 years or older with chronic heart failure for 12 months for recurrent all-cause hospitalization. Frailty was defined as a Clinical Frailty Scale score of 5 or higher. The primary analysis used the Andersen–Gill recurrent-event model; a four-level frailty–comorbidity composite was derived from the Charlson Comorbidity Index, and discrimination was compared by Harrell's C-statistic.
Results
Of 150 participants (median age 75 years; 59.3% male), 54 (36.0%) were frail and 79 (52.7%) were hospitalized at least once. Frailty was associated with recurrent hospitalization after adjustment for age and sex (adjusted hazard ratio 1.73, 95% confidence interval [CI] 1.06–2.83; p=0.028). The frailty–comorbidity composite showed a graded increase in hospitalization burden that did not persist after adjustment. Discrimination was modest across all models (C-statistics near 0.60) and did not differ significantly between frailty (0.581) and LVEF (0.568); combining both gave 0.610 (95% CI 0.560–0.661).
Conclusion
In older Vietnamese adults with chronic heart failure, frailty was associated with recurrent all-cause hospitalization after adjustment for age and sex, with modest discrimination similar to LVEF-based stratification. Baseline frailty assessment may help identify higher-risk patients and warrants prospective evaluation.
Keywords: Frailty, Heart failure, Patient readmission, Comorbidity, Risk assessment, Aged
INTRODUCTION
Heart failure is a leading cause of hospitalization worldwide, with 30-day all-cause readmission rates exceeding 20% in many settings.1) In the Association of Southeast Asian Nations (ASEAN) region, demographic aging and epidemiological transition have increased the cardiovascular disease burden, and rehospitalization for heart failure represents a major and growing component of that burden, particularly among patients with preserved ejection fraction.2-4)
Current risk stratification in heart failure relies predominantly on left ventricular ejection fraction (LVEF) to guide prognosis and therapy. However, systematic reviews of readmission prediction models demonstrate that LVEF-based classification has poor discriminative performance for hospitalization endpoints across heart failure phenotypes.5) Comorbidity indices similarly show limited predictive value for readmission in acute decompensated heart failure,6,7) and these limitations have been observed in Southeast Asian cohorts where the evidence base for hospitalization predictors remains sparse.8)
Frailty, a state of reduced physiological reserve and heightened vulnerability to stressors, is prevalent among older adults with heart failure and independently predicts mortality after adjustment for LVEF, New York Heart Association (NYHA) functional class, and comorbidity burden.9) Most prior studies have examined frailty in relation to time-to-first-event or composite endpoints, which underestimate cumulative hospitalization burden; the recurrent-event dimension of frailty-associated risk has not been systematically evaluated. A prior analysis of this cohort demonstrated that frailty, comorbidity burden, and functional limitation determine patient-reported health status independently of LVEF10); whether these factors predict longitudinal hospitalization events is a distinct and clinically important question.
To our knowledge, no prospective study has evaluated whether frailty predicts recurrent all-cause hospitalization in older Southeast Asian adults with chronic heart failure, nor whether a frailty–comorbidity composite outperforms ejection-fraction-based stratification for this outcome. We conducted a prospective cohort study with three aims: to test whether a Clinical Frailty Scale (CFS) score of 5 or higher is associated with recurrent hospitalization over 12 months; to evaluate whether a frailty–comorbidity composite improves risk stratification beyond either factor alone; and to compare the discriminative performance of frailty-based versus ejection-fraction-based prognostic models.
MATERIALS AND METHODS
Study Design, Participants, and Ethics
This was a single-center, prospective cohort study conducted at the outpatient cardiology clinic of Thong Nhat Hospital between August 2024 and March 2025, with 12-month follow-up, reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Participants were enrolled by consecutive sampling. Eligible patients were aged 60 years or older with chronic heart failure diagnosed according to the 2021 European Society of Cardiology guidelines11) for at least three months. Patients were excluded if they had acute decompensated heart failure, severe cognitive impairment preventing informed consent or questionnaire completion, terminal non-cardiac illness, or inability to complete 12-month follow-up.
The sample size was fixed at 150 by the study design, as previously described10); a comparison of the present prospective cohort and the companion cross-sectional study is provided in Supplementary Table S1. Post-hoc power calculations confirmed adequate statistical power for the primary frailty analysis given the observed frailty prevalence of 36%, a non-frail group hospitalization rate of approximately 0.44 events per person-year, a minimum detectable hazard ratio of 1.8, two-sided α of 0.05, and 80% power. Power for subgroup analyses was limited, as acknowledged in the Limitations section.
The study was conducted in accordance with the Declaration of Helsinki. This study was approved by the Institutional Review Board of the University of Medicine and Pharmacy at Ho Chi Minh City (2118/HDDD-DHYD) and by the Institutional Review Board of Thong Nhat Hospital (121/2024/CN-BVTN-HDDD), with extension for 12-month follow-up (26/2026/CN-HDDD-BVTN). Written informed consent was obtained from all participants.
Data Collection
The primary outcome was recurrent all-cause hospitalization, defined as any unplanned overnight hospital admission during the 12-month follow-up period. Hospitalizations were ascertained through scheduled clinic visits, structured telephone follow-up, and medical record review; each event was recorded with its date of admission. Death was recorded as a terminal event.
The primary predictor was frailty status, assessed using the CFS12) and defined as a score of 5 or higher on the scale. We selected the CFS rather than the Fried physical phenotype13) because the CFS is a clinician-rated judgment that requires no specialized equipment (in particular, no grip dynamometer or gait-speed timing), takes under one minute to complete in routine outpatient practice, and has been extensively validated in heart failure cohorts. Comorbidity burden was quantified using the Charlson Comorbidity Index (CCI). Heart failure phenotype was classified by LVEF as reduced (40% or less), mildly reduced (41%–49%), or preserved (50% or greater) according to echocardiographic measurement. N-terminal pro-B-type natriuretic peptide (NT-proBNP) values were extracted from the medical record, selecting the most recent measurement within three months prior to the enrollment visit.
Covariates collected at baseline included age, sex, body mass index (BMI), NYHA functional class, activities of daily living (ADL) and instrumental activities of daily living (IADL) limitation, Mini Nutritional Assessment–Short Form (MNA-SF) score, polypharmacy, and guideline-directed medical therapy. Kansas City Cardiomyopathy Questionnaire-12 (KCCQ-12) scores, which were examined in a prior analysis of this cohort,10) were available from the baseline assessment but are not analyzed here. The detailed baseline assessment protocol has been described previously.10)
Statistical Analysis
All analyses were performed using R version 4.6.0 (R Foundation for Statistical Computing, Vienna, Austria). To verify reproducibility, the two authors independently re-implemented the entire analytic pipeline in separate software environments (R 4.6.0 and Python 3.12); both implementations produced concordant estimates to three decimal places. Continuous variables were summarized as mean±standard deviation when normally distributed or median (interquartile range [IQR]) when skewed; categorical variables were reported as frequency and percentage. Group differences at baseline were compared using the Mann–Whitney U test or independent t-test for continuous variables and the chi-square or Fisher exact test for categorical variables, as appropriate. NT-proBNP was log-transformed for use as a covariate. A four-level frailty–comorbidity composite variable was constructed by cross-classifying frailty status with high versus low CCI, defined by the cohort median CCI as the cutpoint, yielding four groups: non-frail/low CCI, non-frail/high CCI, frail/low CCI, and frail/high CCI.
The primary analysis evaluated the association between frailty and recurrent all-cause hospitalization using two complementary approaches. The Andersen–Gill extension of the Cox proportional hazards model was applied to the counting-process dataset, with robust standard errors to account for within-patient event correlation, yielding hazard ratios (HRs) with 95% confidence intervals (CIs). Negative binomial regression with the natural logarithm of follow-up time as an offset was used to model total hospitalization counts per patient, yielding incidence rate ratios (IRRs). Both models were fitted unadjusted and adjusted for age and sex. The proportional hazards assumption was assessed using Schoenfeld residuals. Death was treated as informative terminal censoring in both frameworks.
Secondary analyses tested the frailty–comorbidity composite variable in both model types, with the non-frail/low CCI group as the reference. Five parallel model specifications (frailty alone, LVEF classification alone, frailty plus LVEF, the frailty–comorbidity composite alone, and the composite plus LVEF) were compared using Harrell’s C-statistic with 95% confidence intervals (Andersen–Gill models), Akaike and Bayesian information criteria (AIC and BIC), and likelihood ratio tests for nested model pairs. Harrell’s C-statistic was computed from each Andersen–Gill counting-process fit using patient-clustered robust standard errors. These model comparisons were exploratory; no internal validation (bootstrap optimism correction or split-sample validation) was performed.
No missing data were present; multiple imputation was therefore not required. Three pre-specified sensitivity analyses were conducted to assess the robustness of primary findings: (1) additional adjustment for log-transformed NT-proBNP to evaluate confounding by hemodynamic severity; (2) inclusion of a frailty-by-BMI interaction term in both the Andersen–Gill and negative binomial models to test the obesity paradox hypothesis; and (3) application of the Fine–Gray subdistribution hazard model to formally account for the competing risk of death prior to any hospitalization. Statistical significance was defined as a two-sided p-value less than 0.05 throughout.
RESULTS
Study Participants and Baseline Characteristics
Between August 2024 and March 2025, 300 patients were screened and 150 enrolled (Fig. 1). Of these, 96 (64.0%) were non-frail (CFS <5) and 54 (36.0%) were frail (CFS ≥5). The median age was 75 years (IQR 66–84), and 89 (59.3%) were male. Frail patients were significantly older (median 80 vs. 69 years; p<0.001) and more symptomatic, with 61.1% in NYHA class III–IV versus 10.4% of non-frail patients (p<0.001). Mean CCI was markedly higher in the frail group (8.74±1.79 vs. 5.84±1.53; p < 0.001). LVEF and NT-proBNP did not differ significantly between groups (p=0.793 and p=0.828, respectively). Guideline-directed medical therapy was prescribed less frequently in frail patients across all classes—beta-blocker (31.5% vs. 50.0%, p=0.028), renin–angiotensin system inhibitor (53.7% vs. 69.8%, p=0.049), mineralocorticoid receptor antagonist (48.1% vs. 67.7%, p=0.019), sodium–glucose cotransporter-2 inhibitor (50.0% vs. 74.0%, p=0.003), and median number of drug classes (2 vs. 3, p<0.001) (Table 1).
Fig. 1.
Screening, enrollment, and follow-up. Of 300 consecutive patients with chronic heart failure aged 60 years or older screened at the outpatient cardiology clinic of Thong Nhat Hospital between August 2024 and March 2025, 150 were excluded for the reasons shown. All 150 enrolled participants completed 12-month prospective follow-up and were classified by frailty status at baseline: 96 non-frail (CFS score <5) and 54 frail (CFS score ≥5). Baseline clinical and functional characteristics of this cohort have been reported in the companion cross-sectional study. CFS, Clinical Frailty Scale.
Table 1.
Baseline characteristics of the study patients, according to frailty status
| Characteristic | Total (n=150) | Non-frail (n=96) | Frail (n=54) | p-value |
|---|---|---|---|---|
| Demographics and anthropometry | ||||
| Age (y) | 75 (66–84) | 69 (66–78) | 80 (75–87) | <0.001 |
| ≥75 | 94 (62.7) | 47 (49.0) | 47 (87.0) | <0.001 |
| Male sex | 89 (59.3) | 62 (64.6) | 27 (50.0) | 0.081 |
| Current smoker | 51 (34.0) | 40 (41.7) | 11 (20.4) | 0.008 |
| Current drinker | 57 (38.0) | 36 (37.5) | 21 (38.9) | 0.866 |
| BMI (kg/m²) | 21.6±2.2 | 21.7±2.1 | 21.4±2.5 | 0.542 |
| Underweight | 15 (10.0) | 9 (9.4) | 6 (11.1) | |
| Normal weight | 102 (68.0) | 66 (68.8) | 36 (66.7) | |
| Overweight | 26 (17.3) | 18 (18.8) | 8 (14.8) | |
| Obese | 7 (4.7) | 3 (3.1) | 4 (7.4) | |
| Cardiac status | ||||
| LVEF (%) | 49 (37–66) | 48 (37–67) | 50 (40–64) | 0.793 |
| HFrEF | 43 (28.7) | 30 (31.3) | 13 (24.1) | |
| HFmrEF | 34 (22.7) | 23 (24.0) | 11 (20.4) | |
| HFpEF | 73 (48.7) | 43 (44.8) | 30 (55.6) | |
| NT-proBNP (pg/mL) | 604 (155–2122) | 604 (153–2122) | 604 (176–2011) | 0.828 |
| SBP (mmHg) | 139±12 | 140±11 | 139±13 | 0.653 |
| NYHA class III–IV | 43 (28.7) | 10 (10.4) | 33 (61.1) | <0.001 |
| Geriatric syndromes | ||||
| ADL limitation | 38 (25.3) | 1 (1.0) | 37 (68.5) | <0.001 |
| IADL limitation | 54 (36.0) | 0 (0.0) | 54 (100.0) | <0.001 |
| MNA-SF score | 6.8±3.4 | 7.1±3.3 | 6.3±3.6 | 0.181 |
| Polypharmacy | 132 (88.0) | 84 (87.5) | 48 (88.9) | 0.802 |
| Comorbidities | ||||
| CCI | 6.89±2.14 | 5.84±1.53 | 8.74±1.79 | <0.001 |
| Hypertension | 137 (91.3) | 87 (90.6) | 50 (92.6) | 0.681 |
| Atrial fibrillation | 43 (28.7) | 28 (29.2) | 15 (27.8) | 0.857 |
| Coronary artery disease | 116 (77.3) | 72 (75.0) | 44 (81.5) | 0.363 |
| Diabetes | 72 (48.0) | 48 (50.0) | 24 (44.4) | 0.513 |
| Dyslipidemia | 115 (76.7) | 72 (75.0) | 43 (79.6) | 0.520 |
| CKD | 43 (28.7) | 26 (27.1) | 17 (31.5) | 0.567 |
| Stroke | 9 (6.0) | 4 (4.2) | 5 (9.3) | 0.207 |
| Guideline-directed medical therapy | ||||
| Beta-blocker | 65 (43.3) | 48 (50.0) | 17 (31.5) | 0.028 |
| ACEi/ARB/ARNI | 96 (64.0) | 67 (69.8) | 29 (53.7) | 0.049 |
| MRA | 91 (60.7) | 65 (67.7) | 26 (48.1) | 0.019 |
| SGLT2 inhibitor | 98 (65.3) | 71 (74.0) | 27 (50.0) | 0.003 |
| GDMT drug classes (0–4) | 2 (2–3) | 3 (2–3) | 2 (1–3) | <0.001 |
| Health status (KCCQ-12) | ||||
| Overall summary score | 67.8 (57.1–81.8) | 79.2 (64.6–85.4) | 54.7 (42.2–62.0) | <0.001 |
| Physical limitation score | 66.7 (41.7–83.3) | 75.0 (58.3–83.3) | 41.7 (33.3–58.3) | <0.001 |
| Symptom frequency score | 72.9 (64.6–85.4) | 79.2 (70.0–91.7) | 60.4 (43.8–70.0) | <0.001 |
| Quality of life score | 50.0 (50.0–75.0) | 75.0 (50.0–87.5) | 50.0 (50.0–50.0) | <0.001 |
| Social limitation score | 75.0 (66.7–83.3) | 75.0 (66.7–91.7) | 62.5 (50.0–66.7) | <0.001 |
| Clinical events during follow-up | ||||
| All-cause mortality | 9 (6.0) | 3 (3.1) | 6 (11.1) | 0.048 |
| Patients with ≥ 1 hospitalization | 79 (52.7) | 42 (43.8) | 37 (68.5) | 0.004 |
| Total hospitalizations | 1 (0–2) | 0 (0–1) | 1 (0–2) | 0.001 |
Values are presented as median (IQR), mean±standard deviation, or n (%).
ACEi, angiotensin-converting enzyme inhibitor; ADL, activities of daily living; ARB, angiotensin receptor blocker; ARNI, angiotensin receptor–neprilysin inhibitor; BMI, body mass index; CCI, Charlson Comorbidity Index; CKD, chronic kidney disease; GDMT, guideline-directed medical therapy; HFmrEF, heart failure with mildly reduced ejection fraction; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; IADL, instrumental activities of daily living; IQR, interquartile range; KCCQ-12, Kansas City Cardiomyopathy Questionnaire-12; LVEF, left ventricular ejection fraction; MNA-SF, Mini Nutritional Assessment–Short Form; MRA, mineralocorticoid receptor antagonist; NT-proBNP, N-terminal pro-B-type natriuretic peptide; NYHA, New York Heart Association; SBP, systolic blood pressure; SD, standard deviation; SGLT2, sodium–glucose cotransporter 2.
p-values from Mann–Whitney U, independent t-test, or chi-square/Fisher exact test as appropriate.
Baseline health status, as measured by the KCCQ-12, was poorer in frail patients and is summarized in Table 1 for cohort characterization; the full determinants analysis has been reported previously.10) Nine patients (6.0%) died during follow-up, all during a hospitalization. A total of 79 patients (52.7%) experienced at least one hospitalization, with a higher proportion among frail than non-frail patients (68.5% vs. 43.8%, p=0.004) (Fig. 2, Table 1).
Fig. 2.
Distribution of all-cause hospitalizations over 12 months according to frailty status. (A) shows the proportion of patients in each hospitalization-count category (0, 1, 2, 3, 4, or ≥5 events) stratified by frailty status. (B) shows the total number of all-cause hospitalizations per patient, presented as a beeswarm plot overlaid on a box plot (median, interquartile range, and 1.5×interquartile range whiskers); individual observations are jittered to reduce overplotting. The p-value was derived from the Mann–Whitney U test. Frail patients were defined as those with a Clinical Frailty Scale score of 5 or higher.
Frailty and Recurrent Hospitalization
Frail patients accumulated a greater burden of recurrent hospitalizations over 12 months, as illustrated by the mean cumulative hospitalization curves (Supplementary Fig. S2). In unadjusted Andersen–Gill analysis, frailty was associated with approximately twice the rate of all-cause hospitalization (HR 1.94, 95% CI 1.28–2.95; p=0.002). After adjustment for age and sex, frailty remained significantly associated with recurrent hospitalization (adjusted HR 1.73, 95% CI 1.06–2.83; p=0.028) (Table 2, Supplementary Fig. S1).
Table 2.
Hazard and incidence-rate ratios for recurrent all-cause hospitalization, according to frailty status and the frailty–comorbidity composite
| Predictor | Unadjusted HR/IRR (95% CI) | p-value | Adjusted HR/IRR (95% CI) | p-value |
|---|---|---|---|---|
| Model 1: Recurrent event risk (Andersen–Gill) | ||||
| Non-frail (CFS < 5) | Reference | Reference | ||
| Frail (CFS ≥ 5) | 1.94 (1.28–2.95) | 0.002 | 1.73 (1.06–2.83) | 0.028 |
| Model 2: Hospitalization burden (negative binomial) | ||||
| Non-frail (CFS < 5) | Reference | Reference | ||
| Frail (CFS ≥ 5) | 2.15 (1.36–3.43) | <0.001 | 1.82 (1.11–3.01) | 0.017 |
| Model 3: Frailty–comorbidity composite (Andersen–Gill) | ||||
| Non-frail, Low CCI | Reference | Reference | ||
| Non-frail, High CCI | 0.74 (0.39–1.39) | 0.346 | 0.66 (0.34–1.26) | 0.208 |
| Frail, Low CCI | 1.33 (0.70–2.54) | 0.388 | 1.18 (0.58–2.41) | 0.642 |
| Frail, High CCI | 1.87 (1.16–3.01) | 0.010 | 1.54 (0.85–2.82) | 0.156 |
| Model 4: Frailty–comorbidity composite (negative binomial) | ||||
| Non-frail, Low CCI | Reference | Reference | ||
| Non-frail, High CCI | 0.71 (0.35–1.40) | 0.313 | 0.62 (0.30–1.23) | 0.173 |
| Frail, Low CCI | 1.48 (0.54–4.30) | 0.452 | 1.29 (0.46–3.77) | 0.626 |
| Frail, High CCI | 2.05 (1.23–3.42) | 0.005 | 1.59 (0.90–2.81) | 0.110 |
Adjusted models include age and sex. HR, hazard ratio from the Andersen–Gill extension of the Cox model. IRR, incidence rate ratio from negative binomial regression with follow-up time as offset. Death treated as informative terminal censoring.
CCI, Charlson Comorbidity Index; CFS, Clinical Frailty Scale; CI, confidence interval.
The negative binomial model yielded concordant findings: the unadjusted incidence rate ratio for frailty was 2.15 (95% CI 1.36–3.43; p<0.001), and the adjusted estimate was 1.82 (95% CI 1.11–3.01; p=0.017). The proportional hazards assumption was satisfied (global Schoenfeld test χ²=5.57, p=0.134; per-covariate p-values all >0.05) (Supplementary Table S7). All nine deaths occurred during hospitalization, supporting the treatment of death as informative terminal censoring.
Frailty–Comorbidity Composite and Prognostic Model Comparison
The frailty–comorbidity composite groups showed increasing hospitalization burden from non-frail/low CCI to frail/high CCI (Supplementary Table S3, Fig. S2). In the Andersen–Gill model, the frail/high CCI group had the highest unadjusted hospitalization risk relative to the non-frail/low CCI reference (HR 1.87, 95% CI 1.16–3.01; p=0.010), though this estimate was attenuated and no longer statistically significant after adjustment (HR 1.54, 95% CI 0.85–2.82; p=0.156). Neither the frail/low CCI nor the non-frail/high CCI group differed significantly from the reference in either model (Table 2). In a pre-specified subgroup analysis by CCI tertile, event rates rose monotonically from 101.8 per 100 person-years in the lowest tertile to 140.2 per 100 person-years in the highest tertile (Supplementary Table S6); however, after adjustment for age and sex, neither the middle nor the highest tertile differed significantly from the lowest (Supplementary Table S5, Fig. S3).
In exploratory prognostic model comparisons, discrimination was modest across all specifications, with C-statistics near 0.60. Frailty alone (C-statistic 0.581, 95% CI 0.529–0.633) and LVEF classification alone (0.568, 95% CI 0.518–0.619) had almost completely overlapping confidence intervals, so no superiority of one over the other can be inferred, although the AIC was marginally lower for frailty (1508.26 vs. 1516.56). Adding LVEF to frailty was associated with a modest numerical increase (0.610, 95% CI 0.560–0.661), while the frailty–comorbidity composite did not improve fit over binary frailty (0.594, 95% CI 0.540–0.648; AIC 1510.04). These comparisons were not internally validated and should be regarded as exploratory (Fig. 3, Supplementary Table S4).
Fig. 3.
Discriminative performance of prognostic models for recurrent all-cause hospitalization. (A) shows Harrell's C-statistic with 95% confidence intervals from Andersen–Gill recurrent event models for five predictor specifications: frailty alone, left ventricular ejection fraction (LVEF) classification alone, frailty plus LVEF, the frailty–Charlson Comorbidity Index (CCI) composite alone, and the composite plus LVEF. The dashed vertical line denotes chance discrimination (C-statistic=0.50). (B) shows the Akaike information criterion (lower values indicate better model fit) for the same five specifications estimated separately by the Andersen–Gill (red) and negative binomial (blue) estimators. Frailty was defined as a Clinical Frailty Scale score of 5 or higher; LVEF was classified as reduced (≤40%), mildly reduced (41%–49%), or preserved (≥50%); the frailty–CCI composite was constructed by cross-classifying frailty status with high versus low CCI, defined by the cohort median.
Sensitivity Analyses
Three pre-specified sensitivity analyses tested the robustness of the primary findings (Supplementary Table S2). Additional adjustment for log-transformed NT-proBNP barely attenuated the frailty association, which remained statistically significant (IRR 1.80, 95% CI 1.10–2.98; p=0.019), indicating that frailty captures prognostic information distinct from hemodynamic severity reflected in natriuretic peptide levels. No significant frailty-by-BMI interaction was identified (interaction IRR 0.93, 95% CI 0.76–1.14; p=0.485), providing no support for an obesity paradox effect. The Fine–Gray competing risks model yielded a subdistribution hazard ratio for frailty consistent with the primary estimate (HR 1.72, 95% CI 1.06–2.81; p=0.029), as expected because all deaths occurred during hospitalization, precluding a substantive competing risks effect.
DISCUSSION
Principal Findings
Over 12 months of follow-up, frailty defined by a CFS score of 5 or higher was associated with a 73% higher rate of recurrent all-cause hospitalization after adjustment for age and sex. The frailty–comorbidity composite showed a graded increase in hospitalization burden that was attenuated and no longer significant after adjustment and did not improve discrimination over frailty alone. Discrimination was modest and did not differ significantly between frailty-based and LVEF-based models, and combining the two produced only a small numerical increase. These findings identify frailty as a clinically relevant correlate of recurrent hospitalization that may capture prognostic information beyond traditional cardiac indices, although residual confounding cannot be excluded (see Limitations section).
Mechanisms Linking Frailty to Recurrent Hospitalization
Several biological mechanisms explain why frailty amplifies recurrent hospitalization risk beyond what ejection fraction or comorbidity burden alone captures. Sarcopenia, highly prevalent in older adults with heart failure,14,15) reduces peak skeletal-muscle oxygen extraction and limits cardiac output reserve during metabolic stress, accelerating decompensation and prolonging recovery after each admission.15) Chronic low-grade inflammation (inflamm-aging) promotes vascular remodeling and myocardial fibrosis,15) while impaired hepatic and renal reserve compromises tolerance of guideline-directed pharmacotherapy, heightening the risk of adverse drug events that may precipitate worsening heart failure.15) Nutritional depletion compounds this vulnerability: low BMI is independently associated with higher mortality risk in older heart failure patients.16) Together, these pathways explain why frailty and ejection fraction capture complementary prognostic domains.
Most prior studies of frailty and hospitalization in heart failure relied on time-to-first-event designs, which underestimate cumulative burden in patients who experience multiple admissions. The Andersen–Gill framework accounts for the informative nature of prior events; its advantage over Cox regression has been demonstrated in the CHARM-Preserved and DAPA-HF recurrent-event analyses, where time-to-first-event methods meaningfully underestimated treatment benefit.17-20) Our finding that frailty was associated with recurrent hospitalization extends the observations of Leong et al.,9) who reported that frailty predicted first hospitalization or death but did not examine recurrent events; it also aligns with Sokoreli et al.,21) who demonstrated frailty was independently associated with both first and recurrent hospitalizations in the OPERA-HF cohort, and addresses the gap left by the FRAGILE-HF registry, where Jujo et al.,22) used composite first-event endpoints only.
Frailty versus Ejection Fraction for Hospitalization Prediction
Frailty-based and ejection-fraction-based models showed similarly modest discrimination for recurrent hospitalization (C-statistic 0.581 vs. 0.568, with overlapping confidence intervals). Although neither approach discriminated well, the observation that a frailty-based model performed at least as well as ejection fraction suggests that ejection fraction alone may be insufficient for hospitalization prognostication in this population. Talha et al.15) confirmed in their review that frailty confers a 1.5- to 2-fold higher risk of all-cause death and hospitalization irrespective of comorbid conditions, yet frailty remains absent from most clinical risk scores. Van Grootven et al.,5) in a systematic review of 81 readmission prediction models in heart disease, found that the C-statistic fell below 0.70 in 72 models, highlighting the inadequacy of conventional predictors, including ejection fraction, for discriminating readmission risk.
These observations align with Parikh et al.,23) who demonstrated in a large real-world cohort that model performance and leading predictors for worsening heart failure events were consistent across the full ejection fraction spectrum, suggesting ejection fraction itself adds limited incremental discriminatory value. In a prior analysis of this cohort, ejection fraction did not independently predict patient-reported health status.10) Together, these findings suggest that ejection fraction provides only modest discrimination for subjective health perception and hospitalization frequency. The modest numerical improvement when combining frailty with ejection fraction (C-statistic 0.610) is consistent with these parameters capturing complementary domains, systemic resilience and cardiac reserve, a possibility that warrants evaluation in larger, internally validated studies.22)
The Frailty–Comorbidity Interface in Geriatric Heart Failure
The frailty–comorbidity composite showed a gradient in unadjusted hospitalization burden, with the frail/high CCI group demonstrating the highest event rates; however, this association did not remain significant after adjustment for age and sex and did not improve discrimination over frailty alone, so the composite should be regarded as exploratory. McAlister et al.24) established that the Hospital Frailty Risk Score and CCI are only weakly correlated (r=0.35), confirming they capture distinct phenomena, organ-specific disease burden versus erosion of integrative reserve, although in their cohort adding the frailty score to standard predictors yielded only modest incremental discrimination. Wei et al.25) further identified a nonlinear relationship between CCI and readmission in heart failure, with prognostic saturation near a threshold of 2.97, suggesting threshold effects in comorbidity-only stratification.
The absence of excess hospitalization risk in the non-frail/high CCI group aligns with Marcus et al.,6) who found that common comorbidities perform poorly as individual-level readmission predictors in acute decompensated heart failure, supporting the need for models that integrate functional factors. Elevated event rates in the small frail/low CCI subgroup reinforce that frailty operates through mechanisms distinct from organ-specific disease accumulation, consistent with Aidoud et al.,26) who argue that frailty and multimorbidity must be assessed jointly to characterize risk in older adults with cardiovascular disease.
Frailty and Hospitalization in Southeast Asia
This study contributes data from a population underrepresented in the frailty and heart failure literature. The frailty prevalence of 36% in our cohort is consistent with estimates reported in Asian registry studies, where prevalence varies widely depending on the assessment tool, though direct comparisons remain constrained by instrument heterogeneity.9,27) The high comorbidity burden (mean CCI 6.89) mirrors the ASIAN-HF registry, which documented a high concurrence of hypertension, diabetes, and chronic kidney disease across 11 Asian regions, comorbidity patterns shaped by the ongoing epidemiological transition in Southeast Asia.2,28)
Unlike findings from a prior analysis of this cohort, in which male sex predicted better patient-reported health status,10) no significant sex-based differences in hospitalization patterns were observed. This is consistent with evidence that sex-related mechanisms influencing subjective health perception differ from those governing acute care utilization.29) The high proportion of heart failure with preserved ejection fraction (48.7%) mirrors regional registry data and underscores the importance of frailty evaluation in this phenotype, where pharmacological options remain limited.4,30)
Implementation in Heart Failure Care Pathways
The CFS is designed for routine clinical use: it requires no specialized equipment, takes under 1 minute for a clinician or trained nurse to complete at patient intake, and is available free of charge. In practice, a baseline CFS assessment could be embedded at the point of vital-sign measurement during an outpatient heart failure visit without disrupting clinic workflow. Because our data are observational, they cannot establish that frailty-guided care reduces hospitalization; whether a CFS score of 5 or higher should prompt structured geriatric review, medication reconciliation to address the substantial perceived medication burden reported by older adults with heart failure,31) or referral to a supervised exercise rehabilitation program requires evaluation in prospective, ideally randomized, studies. Kitzman et al.32) demonstrated in a multicenter randomized trial that early exercise rehabilitation significantly improved physical function in frail older patients hospitalized for heart failure, supporting the feasibility of rehabilitation-based interventions in this population. Evidence from inpatient settings corroborates the prognostic utility of frailty-based triage: Chew et al.33) found that a CFS score of 4–6 was independently associated with 30-day readmission in acutely admitted older adults, and Shin et al.34) demonstrated that geriatric syndrome screening within 48 hours of admission similarly identified patients at heightened readmission risk. In lower-middle-income settings across Southeast Asia, where geriatric cardiology infrastructure is still developing, the CFS offers a pragmatic, resource-efficient first step toward frailty-integrated care pathways that do not require specialized equipment or dedicated geriatric wards.
Future Directions
Adequately powered multicenter studies should evaluate subgroup effects of the frailty–comorbidity composite. Randomized trials of frailty-targeted interventions, including exercise rehabilitation, nutritional supplementation, and deprescribing protocols, should adopt recurrent hospitalization as the primary endpoint rather than time-to-first-event. Longitudinal studies examining trajectories of frailty change and their dynamic relationship with hospitalization risk are needed. Integration of patient-reported outcomes with clinical event endpoints into composite measures may provide more comprehensive assessments of treatment benefit in older adults with heart failure.
Strengths and Limitations
This study has several strengths. We enrolled a consecutive prospective cohort with systematic 12-month follow-up and applied recurrent-event methodology (Andersen–Gill and negative-binomial models) that captures cumulative hospitalization burden rather than time-to-first-event alone; we also conducted a direct head-to-head comparison of frailty-based and ejection-fraction-based prognostic models and quantified the incremental discrimination obtained by combining them. The cohort comprises older Southeast Asian adults with chronic heart failure, a population underrepresented in the frailty literature and in registry data informing current guidelines.
Several limitations warrant consideration. The fixed sample size of 150 and single tertiary-center design limit precision for subgroup contrasts and restrict generalizability to community, rural, and primary-care settings across diverse Asian populations. Because the number of events was limited, the multivariable models adjusted only for age and sex; frail patients had substantially higher NYHA class, greater comorbidity burden, more functional dependency, and received less guideline-directed medical therapy across all drug classes (Table 1), each of which could confound the frailty–hospitalization association. Our estimates therefore reflect association rather than an independent or causal effect, and a fully adjusted model incorporating heart failure severity and treatment could not be reliably estimated in this sample. Hospitalizations were ascertained as all-cause events, and admissions at external facilities may have been undercounted. Baseline data on socioeconomic status, depressive symptoms, and caregiver support were also unavailable; residual confounding from these factors may bias the frailty–hospitalization estimate upward, and the true effect may be smaller than reported. Multicenter validation in Asian populations is warranted before routine adoption.
Conclusion
In this single-center prospective cohort of older Vietnamese adults with chronic heart failure, frailty defined by a CFS score of 5 or higher was associated with recurrent all-cause hospitalization over 12 months of follow-up, with frail patients experiencing a 73% higher rate of recurrent events after adjustment for age and sex. Frailty-based and LVEF-based prognostic models showed similarly modest discrimination, and combining the two produced only a small numerical increase. Baseline CFS assessment offers a rapid, resource-efficient means of identifying patients at higher risk of recurrent hospitalization; whether frailty-guided interventions such as exercise rehabilitation, deprescribing review, or structured geriatric consultation reduce hospitalization was not tested here and requires evaluation in prospective trials.
Footnotes
The authors gratefully acknowledge the contributions of Anh Thi Ngoc Pham, MD, MSc and Uyen Thi Hoang Nguyen, MD, MSc (Department of Geriatrics and Gerontology, School of Medicine, University of Medicine and Pharmacy at Ho Chi Minh City, Vietnam), Tung Huy Pham, MD, MSc (Department of Radiology, Thong Nhat Hospital, Ho Chi Minh City, Vietnam), Tien Ngoc Hoanh My Nguyen, MD, MSc, and The Ha Ngoc Than, MD, PhD (Department of Geriatrics and Palliative Care, University Medical Centre Ho Chi Minh City, Vietnam) for their assistance with data collection and participant recruitment during the original cross-sectional phase of this study.
CONFLICT OF INTEREST
The researchers claim no conflicts of interest.
FUNDING
None.
AUTHOR CONTRIBUTIONS
Conceptualization, HLHN; Data curation, HLHN, TCT; Formal analysis, HLHN; Investigation, HLHN, TCT; Methodology, HLHN; Project administration, HLHN; Supervision, TCT; Writing–original draft, HLHN; Writing–review & editing, HLHN, TCT.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are not publicly available due to privacy and ethical restrictions but are available from the corresponding author upon reasonable request.
SUPPLEMENTARY MATERIALS
Supplementary materials can be found via https://doi.org/10.4235/agmr.26.0049.
SUPPLEMENTARY METHODS
Table S1. Comparison of the present prospective cohort and the companion cross-sectional study
Table S2. Sensitivity analyses of the association between frailty and recurrent all-cause hospitalization
Table S3. Baseline characteristics of the study patients, according to the frailty–comorbidity composite group
Table S4. Discrimination and fit of prognostic models for recurrent all-cause hospitalization, according to frailty- and LVEF-based stratification
Table S5. Prespecified subgroup analyses, according to CCI tertile
Table S6. All-cause hospitalization event rates, according to CCI tertile
Table S7. Schoenfeld residual proportional-hazards diagnostic for the primary adjusted Andersen–Gill model
Fig. S1. Adjusted Hazard Ratios and Incidence-Rate Ratios for Recurrent All-Cause Hospitalization. (A) shows adjusted hazard ratios from the Andersen-Gill model; (B) shows adjusted incidence rate ratios from the negative binomial model. All estimates are adjusted for age and sex. The primary predictor, frailty status (blue), is presented alongside secondary predictors including frailty-comorbidity composite groups (red) and LVEF categories (green). Error bars represent 95% confidence intervals. CI, confidence interval; CCI, Charlson Comorbidity Index; HFmrEF, heart failure with mildly reduced ejection fraction; HFpEF, heart failure with preserved ejection fraction; HFrEF, heart failure with reduced ejection fraction; LVEF, left ventricular ejection fraction.
Fig. S2. Mean cumulative number of all-cause hospitalizations, according to the frailty–comorbidity composite group. Mean cumulative function (Nelson–Aalen estimator) for recurrent hospitalizations per patient over 12 months, stratified by frailty–comorbidity composite group. CCI, Charlson Comorbidity Index.
Fig. S3. Adjusted hazard ratios for recurrent all-cause hospitalization, according to Charlson Comorbidity Index (CCI) tertile. Adjusted hazard ratios (Andersen–Gill model, adjusted for age and sex) for recurrent all-cause hospitalization across CCI tertiles; Tertile 1 is the reference. Estimates are from Supplementary Table S5; crude event rates are in Supplementary Table S6.
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