Abstract
Background
Frailty has recognized prognostic value in cardiovascular disease, but its relationship with health-related quality of life (HRQoL) after heart transplantation remains poorly characterized in middle-income settings. This study evaluated frailty and HRQoL among adult heart transplant survivors.
Methods
We conducted a cross-sectional analysis of adult heart transplant recipients at a national referral center in Colombia. HRQoL was assessed using the EuroQol 5-Dimension 3-Level instrument, and frailty was evaluated using a self-reported FRAIL scale administered by telephone. Participants were classified as non-frail, prefrail, or frail. EQ-5D index scores were compared using Bonferroni-corrected pairwise tests. Multivariable linear regression with robust standard errors was used as an exploratory analysis.
Results
Seventy-three recipients were included: 39 non-frail, 29 prefrail, and 5 frail. EQ-5D index scores decreased across frailty categories. Non-frail participants had higher scores than prefrail and frail participants, and prefrail participants had higher scores than frail participants. In exploratory multivariable analysis, frail participants showed lower HRQoL scores than non-frail participants (β = −0.19; 95% CI, − 0.32 to − 0.06; p = 0.005), whereas prefrail participants showed no clear difference (β = −0.01; 95% CI, − 0.08 to 0.06; p = 0.797). Hypertension also showed an exploratory inverse relationship with EQ-5D index scores (β = −0.11; 95% CI, − 0.19 to − 0.02; p = 0.013).
Conclusions
Among long-term survivors, frail participants showed lower HRQoL scores, whereas prefrail participants showed no clear differences. Given the design, small frail subgroup, and survivorship bias, these findings should be interpreted as exploratory and hypothesis-generating.
Keywords: Heart Transplantation, Frailty, Health-Related Quality of Life, EQ-5D, Patient-Reported Outcomes, Middle-Income Countries
Introduction
Heart transplantation remains the gold standard therapy for selected patients with advanced heart failure, leading to substantial improvements in survival and functional status. Beyond traditional clinical outcomes, health-related quality of life (HRQoL) has become a key endpoint, reflecting patients’ overall well-being, functional capacity, and psychosocial adaptation after transplantation [1, 2]. Although most recipients experience improvements in HRQoL, considerable variability persists, underscoring the need to identify factors related to patient-reported outcomes in this population [3, 4].
Frailty, a multidimensional syndrome characterized by reduced physiological reserve and increased vulnerability to stressors, has gained increasing attention in cardiovascular medicine and transplantation [5]. In patients with advanced heart failure, frailty is highly prevalent and has been consistently associated with adverse outcomes, including mortality, complications, and prolonged hospitalization after heart transplantation [6]. However, despite its prognostic relevance, frailty is not routinely incorporated into post-transplant assessment, and its relationship with patient-reported outcomes beyond traditional clinical outcomes remains insufficiently characterized. Frailty encompasses physical, cognitive, and psychosocial domains that are closely linked to perceived health status. In cardiovascular and transplant populations, it has been associated with worse HRQoL across physical and mental dimensions [4, 7]. Similarly, in heart failure populations, frailty has been linked to poorer HRQoL and may hinder the implementation of optimal therapies, highlighting its relevance for patient-centered outcomes [8, 9].
In heart transplant recipients, frailty appears to be both prevalent and potentially reversible after transplantation [10]. Nevertheless, its relationship with HRQoL in this population remains poorly understood, particularly in real-world settings [7]. Moreover, most available evidence derives from high-income countries, limiting its applicability to middle-income contexts, where differences in healthcare systems and social determinants may influence patient-reported outcomes.
Addressing this gap is essential, as improving HRQoL is a central goal of post-transplant care. Understanding the relationship between frailty and HRQoL may help describe patient-centered vulnerability and generate hypotheses for future post-transplant care strategies [8, 11]. Therefore, this study aimed to describe the relationship between frailty status and HRQoL among long-term adult heart transplant survivors in a middle-income country.
Methods
Study design and population
We conducted a cross-sectional analysis of a historical cohort of adult heart transplant recipients followed at a national cardiovascular referral center in Colombia. The source population included all patients who underwent heart transplantation at the institution between June 6, 2006, and January 22, 2026.
Eligible participants were adult recipients who were alive at the time of contact, under active institutional follow-up, and able to complete a structured telephone interview. Patients who had died before HRQoL assessment, had no updated contact information, did not respond to contact attempts, were pediatric recipients, or had missing frailty data were excluded.
HRQoL and frailty were assessed at a single follow-up point through structured telephone interviews conducted by trained personnel. Of 289 patients who underwent heart transplantation during the study period, 154 had died before assessment. Among the 135 surviving patients, 32 pediatric recipients were excluded. Of the 103 eligible adult survivors, 29 were not contacted or did not respond, and 1 was excluded because of missing frailty data, resulting in a final analytical sample of 73 adult heart transplant recipients. Because only surviving and contactable patients were included, survivorship and selection bias may have occurred, with patients in worse clinical condition potentially underrepresented (Fig. 1).
Fig. 1.
Flowchart of patient selection among heart transplant recipients (2006–2026). Flow diagram illustrating the selection of adult heart transplant recipients included in the study. Of 289 patients who underwent transplantation, 154 were excluded due to death. Among the 135 surviving patients, 32 pediatric patients were excluded. Of the 103 adult recipients, 29 were not contacted or did not respond, and 1 was excluded due to missing frailty data, resulting in a final analytical sample of 73 patients
Data collection
Data were collected through structured telephone interviews conducted by trained personnel using electronic forms implemented in REDCap (Research Electronic Data Capture) [12, 13]. Interview data were complemented with clinical information obtained from the institutional heart transplant database. All data was stored in a secure REDCap platform, ensuring validated data entry, audit trails, and reliable export for statistical analysis. Prior to analysis, the dataset underwent systematic cleaning to assess completeness, internal consistency, and data quality.
Health-related quality of life assessment
HRQoL was evaluated using the EuroQol 5-Dimension 3-Level (EQ-5D-3 L) instrument. previously validated for use in the Colombian population and administered under the corresponding authorization from the EuroQol Research Foundation. This tool assesses five domains: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Health states were converted into a single utility index (EQ-5D index, hereafter referred to as Hindex), ranging from 1 (full health) to 0 (death), with negative values representing health states perceived as worse than death. The Hindex was calculated using the Ecuadorian population-based value set, given the absence of a Colombian tariff, which has been previously applied in comparable Latin American settings [14]. The EQ-5D-3 L also includes a visual analogue scale (EQ-VAS), ranging from 0 to 100, reflecting self-perceived health status [15].
Frailty assessment
Frailty was assessed using the self-reported FRAIL scale during structured telephone interviews, based on the approach described by Morley et al. [16]. Because the assessment was conducted by telephone, performance-based measures such as grip strength and gait speed were not directly measured. Therefore, weakness and ambulation were evaluated using self-reported proxy items rather than objective physical performance tests. The FRAIL scale includes five domains: fatigue, resistance, ambulation, illnesses, and weight loss. For each domain, participants were classified as having or not having the corresponding frailty criterion according to their interview responses. Participants were classified as non-frail (0 criteria), prefrail (1–2 criteria), or frail (≥ 3 criteria). This telephone-based self-reported approach was used to enable assessment in a geographically dispersed post-transplant population; however, it should not be interpreted as equivalent to the performance-based Fried phenotype.
The specific self-reported items used to define the FRAIL domains were: fatigue, self-reported tiredness or exhaustion; resistance, difficulty climbing stairs; ambulation, difficulty walking independently; illnesses, presence of multiple chronic conditions; and weight loss, unintentional weight loss during the preceding period.
Study variables
The primary outcome was the EQ-5D index (Hindex), analyzed as a continuous variable. The main exposure was frailty status. Covariates included sociodemographic variables (age at transplantation and at interview, sex, educational level, marital status, health insurance, area of residence, and socioeconomic status), as well as clinical and transplant-related variables, including time since transplantation, waiting-list duration, etiology of heart failure, recipient clinical status, New York Heart Association (NYHA) functional class, left ventricular ejection fraction classification, transplant type, blood group, and pre-existing comorbidities.
Statistical analysis
Continuous variables were summarized as medians and interquartile ranges (IQR) according to their distribution, which was assessed using the Shapiro–Wilk test. Categorical variables were described as absolute frequencies and percentages. Comparisons across frailty categories (non-frail, prefrail, and frail) were performed using Fisher’s exact test for categorical variables and the Kruskal–Wallis test for continuous variables.
The EQ-5D index (Hindex) was treated as a continuous outcome. Differences in EQ-5D index scores across frailty categories were assessed using pairwise Wilcoxon rank-sum tests with Bonferroni correction to account for multiple comparisons. Multivariable linear regression models were subsequently constructed as exploratory analyses to evaluate the relationship between frailty status and HRQoL, adjusting for a limited number of clinically relevant covariates. Given the very small number of frail participants, these models were not intended to provide confirmatory evidence of an independent association. Regression coefficients, confidence intervals, and p values were interpreted cautiously because of the risk of model instability and overfitting. Results were reported as regression coefficients (β) with corresponding 95% confidence intervals (CI).
Given the non-normal distribution and ceiling effects commonly observed in EQ-5D data, linear regression models with robust standard errors were applied. Due to the limited number of frail patients, models were specified parsimoniously to reduce the risk of overfitting. Model assumptions, including linearity, normality, and homoscedasticity of residuals, were evaluated using graphical methods. Multicollinearity was assessed using variance inflation factors (VIF).
Model performance was evaluated using the coefficient of determination (R²) and information criteria, including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). All statistical tests were two-tailed, and statistical significance was established at p < 0.05. Analyses were conducted using Stata version 16.0 (StataCorp, College Station, TX, USA).
Ethical considerations
The study was conducted in accordance with ethical principles for research involving human subjects and institutional regulations. Data was stored in a secure REDCap database with restricted access to authorized investigators. Interviews were conducted ensuring participants’ privacy and confidentiality. The study protocol was approved by the institutional ethics committee (CEI 2026-11023-2), in accordance with national regulations for observational studies involving human subjects.
Results
A total of 73 adult heart transplant recipients were included: 39 (53.4%) non-frail, 29 (39.7%) prefrail, and 5 (6.8%) frail. Sociodemographic characteristics were comparable across frailty groups, with no statistically significant differences observed. The study was predominantly male across all categories (79.5%, 82.8%, and 80.0%, respectively; p = 0.943). Most patients were affiliated with the contributory insurance regime (48.7%, 55.2%, and 60.0%; p = 0.503). Educational level was similar across groups, with primary and secondary education being the most frequent. A high proportion of participants reported living with a partner (71.8%, 75.9%, and 60.0%; p = 0.753), and the majority resided in urban areas (94.9%, 89.7%, and 100.0%; p = 0.576). Socioeconomic stratum 2 was the most common category across all groups (p = 0.644) (Table 1).
Table 1.
Baseline sociodemographic characteristics of adult heart transplant recipients according to frailty status
| Variable | Non-frail (n = 39) | Prefrail (n = 29) | Frail (n = 5) | P value |
|---|---|---|---|---|
| Age at transplant, y, median (IQR) | 50 (45–55) | 53 (47–61) | 46 (40–51) | 0.112 |
| Sex, n (%) | 0.943 | |||
| Female | 8 (20.5) | 5 (17.2) | 1 (20.0) | |
| Male | 31 (79.5) | 24 (82.8) | 4 (80.0) | |
| Health insurance, n (%) | 0.503 | |||
| Contributory | 19 (48.7) | 16 (55.2) | 3 (60.0) | |
| Subsidized | 17 (43.6) | 8 (27.6) | 2 (40.0) | |
| Other | 3 (7.7) | 5 (17.2) | 0 (0.0) | |
| Educational level, n (%) | 0.412 | |||
| None | 4 (10.3) | 0 (0.0) | 0 (0.0) | |
| Primary school | 12 (30.8) | 12 (41.4) | 3 (60.0) | |
| Secondary school | 14 (35.9) | 13 (44.8) | 1 (20.0) | |
| Technical training | 5 (12.8) | 1 (3.4) | 1 (20.0) | |
| Professional | 4 (10.3) | 3 (10.3) | 0 (0.0) | |
| Marital status, n (%) | 0.753 | |||
| With partner | 28 (71.8) | 22 (75.9) | 3 (60.0) | |
| Without partner | 11 (28.2) | 7 (24.1) | 2 (40.0) | |
| Area of residence, n (%) | 0.576 | |||
| Urban | 37 (94.9) | 26 (89.7) | 5 (100) | |
| Rural | 2 (5.1) | 3 (10.3) | 0 (0.0) | |
| Socioeconomic stratum, n (%) | 0.644 | |||
| 1 | 3 (7.7) | 1 (3.4) | 0 (0.0) | |
| 2 | 28 (71.8) | 17 (58.6) | 4 (80.0) | |
| 3 | 8 (20.5) | 10 (34.5) | 1 (20.0) | |
| 4 | 0 (0.0) | 1 (3.4) | 0 (0.0) |
Values are presented as n (%) or median (interquartile range). Comparisons across frailty groups were performed using the Fisher’s exact test for categorical variables and the Kruskal–Wallis test for continuous variables. A two-sided p value < 0.05 was considered statistically significant
Pre-transplant clinical characteristics were comparable across frailty groups. Most patients were classified as NYHA functional class III–IV, with a similar distribution across categories (p = 0.869). The underlying etiology of heart failure showed some variability (p = 0.086). Chagasic and dilated cardiomyopathies were the most frequent etiologies, particularly among prefrail patients (both 41.4%), whereas ischemic cardiomyopathy appeared more common in non-frail individuals (28.2%). Blood type distribution and comorbidity burden were also similar across frailty categories. Congestive heart failure was the most prevalent comorbidity (51.3–60.0%; p = 0.810) (Table 2).
Table 2.
Clinical and pre-transplant characteristics of adult heart transplant recipients according to frailty status
| Variable | Nonfrail (n = 39) | Prefrail (n = 29) | Frail (n = 5) | P value |
|---|---|---|---|---|
| AHA diagnosis, n (%) | ||||
| Class C selected | 4 (10.3) | 6 (20.7) | 1 (20.0) | 0.468 |
| Class D selected | 4 (10.3) | 4 (13.8) | 1 (20.0) | 0.785 |
| Etiology of heart failure, n (%) | 0.086 | |||
| Chagasic | 12 (30.8) | 12 (41.4) | 1 (20.0) | |
| Dilated | 8 (20.5) | 12 (41.4) | 1 (20.0) | |
| Ischemic | 11 (28.2) | 2 (6.9) | 0 (0.0) | |
| Idiopathic | 2 (5.1) | 1 (3.4) | 2 (40.0) | |
| Congenital | 1 (2.6) | 1 (3.4) | 0 (0.0) | |
| Hypertensive | 1 (2.6) | 1 (3.4) | 0 (0.0) | |
| Valvular | 1 (2.6) | 0 (0.0) | 1 (20.0) | |
| Other | 1 (2.6) | 0 (0.0) | 0 (0.0) | |
| Chemotoxicity | 1 (2.6) | 0 (0.0) | 0 (0.0) | |
| Restrictive | 1 (2.6) | 0 (0.0) | 0 (0.0) | |
| NYHA class‡, n (%) | 0.869 | |||
| II | 0 (0.0) | 1 (4.2) | 0 (0.0) | |
| III | 10 (45.5) | 11 (45.8) | 2 (40.0) | |
| IV | 12 (54.5) | 12 (50.0) | 3 (60.0) | |
| Left ventricular ejection fraction, median (IQR) | 14 (10–23) | 13.5 (10–20) | 10 (10–15) | 0.412 |
| Blood type, n (%) | 0.261 | |||
| A+ | 13 (33.3) | 7 (24.1) | 0 (0.0) | |
| A− | 1 (2.6) | 1 (3.4) | 0 (0.0) | |
| AB+ | 2 (5.1) | 0 (0.0) | 0 (0.0) | |
| B+ | 7 (17.9) | 3 (10.3) | 1 (20.0) | |
| O+ | 16 (41.0) | 17 (58.6) | 3 (60.0) | |
| O− | 0 (0.0) | 1 (3.4) | 1 (20.0) | |
| Medical History, n (%) | ||||
| Congestive heart failure | 20 (51.3) | 17 (58.6) | 3 (60.0) | 0.810 |
| Peripheral vascular disease | 1 (2.6) | 1 (3.4) | 0 (0.0) | 0.905 |
| Cerebrovascular disease | 3 (7.7) | 3 (10.3) | 0 (0.0) | 0.728 |
| Chronic obstructive pulmonary disease | 1 (2.6) | 1 (3.4) | 0 (0.0) | 0.905 |
| Rheumatologic/autoimmune disease | 1 (2.6) | 0 (0.0) | 0 (0.0) | 0.643 |
| Hypertension | 7 (17.9) | 6 (20.7) | 1 (20.0) | 0.959 |
| Diabetes mellitus | 4 (10.3) | 5 (17.2) | 0 (0.0) | 0.471 |
| Dyslipidemia | 2 (5.1) | 1 (3.4) | 0 (0.0) | 0.840 |
| Coronary artery disease | 5 (12.8) | 3 (10.3) | 0 (0.0) | 0.682 |
| Hypothyroidism | 5 (12.8) | 2 (6.9) | 0 (0.0) | 0.537 |
| Family history of coronary disease | 1 (2.6) | 1 (3.4) | 1 (20.0) | 0.176 |
| Smoking history | 3 (7.7) | 3 (10.3) | 0 (0.0) | 0.728 |
Values are presented as n (%). Comparisons across frailty groups were performed using the Fisher’s exact test. A two-sided p value < 0.05 was considered statistically significant
AHA American Heart Association, NYHA New York Heart Association, EF Ejection fraction, COPD Chronic obstructive pulmonary disease
‡ NYHA functional class was available for a subset of patients
Most procedures were orthotopic (> 96% in all categories; p = 0.664), with no relevant differences in transplant type. Post-transplant clinical events were infrequent and showed no clear differences between groups, including infections, post-transplant diabetes mellitus, and de novo malignancy (all p > 0.05). Although not statistically significant, frail patients had a longer time since transplantation, with 80.0% beyond five years compared to 46.2% and 41.4% in non-frail and prefrail groups, respectively (p = 0.122). Consistently, median time since transplantation was longer in frail patients (4826 days [IQR 4621–5212]) compared with prefrail (927 [489–3805]) and non-frail individuals (1683 [917–4512]) (p = 0.062). The left ventricular ejection fraction was severely reduced in all groups, with similar median values (Table 3).
Table 3.
Transplant characteristics and post-transplant outcomes according to frailty status in adult heart transplant recipients
| Variable | Non-frail (n = 39) | Prefrail (n = 29) | Frail (n = 5) | P value |
|---|---|---|---|---|
| Transplant type, n (%) | 0.664 | |||
| Orthotopic | 38 (97.4) | 28 (96.6) | 5 (100) | |
| Heart-kidney | 0 (0.0) | 1 (3.4) | 0 (0.0) | |
| Re Transplantation | 1 (2.6) | 0 (0.0) | 0 (0.0) | |
| Post-transplant events, n (%) | ||||
| Infection | 1 (2.6) | 3 (10.3) | 0 (0.0) | 0.800 |
| Post-transplant diabetes mellitus | 3 (7.7) | 2 (6.9) | 0 (0.0) | 0.562 |
| Post-transplant cancer | 5 (12.8) | 1 (3.4) | 0 (0.0) | 0.100 |
| Time since transplant group, n (%) | 0.122 | |||
| < 1 year | 1 (2.6) | 5 (17.2) | 0 (0.0) | |
| 1–5 years | 20 (51.3) | 12 (41.4) | 1 (20.0) | |
| > 5 years | 18 (46.2) | 12 (41.4) | 4 (80.0) | |
| Waiting-list time, median (IQR) | 21 (10–35) | 16 (1–53.5) | 22 (20–97) | 0.587 |
| Time since transplant, d, median (IQR) | 1683 (917–4512) | 927 (489–3805) | 4826 (4621–5212) | 0.062 |
| Total ischemic time, median (IQR) | 0 (0–236) | 130 (0–232) | 0 (0–0) | 0.275 |
| Age at interview, y, median (IQR) | 57 (52–61) | 61 (55–66) | 61 (59–65) | 0.115 |
Data are presented as n (%) or median (interquartile range), as appropriate. Comparisons across frailty groups were performed using the Fisher’s exact test for categorical variables and the Kruskal–Wallis test for continuous variables. A two-sided p value < 0.05 was considered statistically significant. Post-transplant infection includes any documented infectious complication requiring medical treatment. Post-transplant diabetes mellitus refers to new-onset diabetes after transplantation. Post-transplant cancer includes any de novo malignancy diagnosed after transplantation
Median HIndex values differed across frailty groups, showing a decreasing pattern with increasing frailty. Non-frail patients had the highest values (median 1.00; IQR 1.00–1.00), followed by prefrail individuals (median 0.97; IQR 0.96–1.00), and frail patients (median 0.76; IQR 0.73–0.76). Pairwise comparisons using the Wilcoxon rank-sum test with Bonferroni correction indicated differences between groups. Non-frail patients had higher HIndex values compared with prefrail (adjusted p = 0.024) and frail individuals (adjusted p < 0.001). Additionally, prefrail patients showed higher values than frail individuals (adjusted p = 0.006) (Fig. 2).
Fig. 2.
Distribution of Health-Related Quality of Life (EQ-5D-3 L Index) according to frailty status. Boxplots showing the distribution of EQ-5D-3 L index (HIndex) according to frailty status. Values are expressed as median and interquartile range. Pairwise comparisons were performed using the Wilcoxon rank-sum test with Bonferroni correction
In exploratory multivariable linear regression analysis, frail participants showed lower EQ-5D index scores than non-frail participants. However, because only five participants were classified as frail, these estimates should be interpreted cautiously and should not be considered confirmatory. Compared with non-frail patients, frail individuals had lower EQ-5D index scores (β = −0.190; 95% CI: −0.323 to − 0.058; p = 0.005), whereas prefrail patients showed no significant difference (β = −0.009; 95% CI: −0.077 to 0.060; p = 0.797). Hypertension also showed an exploratory inverse relationship with EQ-5D index scores (β = −0.106; 95% CI: −0.188 to − 0.023; p = 0.013) (Fig. 3). The overall model was statistically significant (F(3,69) = 4.98; p = 0.0035) and explained a modest proportion of the variability in health-related quality of life (R² = 0.178; adjusted R² = 0.142). No evidence of multicollinearity was observed (mean VIF = 1.03). Model fit was supported by information criteria (AIC = − 76.2; BIC = − 67.1).
Fig. 3.
Association between frailty status, hypertension, and health-related quality of life. Exploratory multivariable linear regression analysis of factors related to the EQ-5D index. Data are presented as adjusted regression coefficients (β) and 95% confidence intervals. Frailty groups included non-frail participants (n = 39), prefrail participants (n = 29), and frail participants (n = 5). Because only five participants were classified as frail, estimates involving this group should be interpreted cautiously and should not be considered confirmatory. Data are presented as adjusted regression coefficients (β) and 95% confidence intervals. The model was adjusted for potential confounders, explaining 17.8% of the variance in health-related quality of life (R2 = 0.178; p = 0.003)
Discussion
This cross-sectional study identified an exploratory pattern of lower HRQoL scores among frail long-term adult heart transplant survivors. Frail participants showed lower EQ-5D index scores compared with non-frail participants, whereas prefrail participants did not show clear differences in HRQoL. However, given the small number of frail participants, the cross-sectional design, and the potential for survivorship and selection bias, these findings should be interpreted cautiously and should not be considered confirmatory. They do not establish frailty as an independent determinant of patient-reported outcomes in transplant populations.
Our findings are broadly consistent with prior studies describing lower HRQoL among frail individuals in cardiovascular and transplant populations. In patients with heart failure, frailty has been associated with worse HRQoL, particularly in physical functioning and symptom burden domains [7, 17]. Similarly, studies in solid organ transplant recipients have described an association between frailty and poorer functional recovery and self-perceived health status after transplantation [3, 18–20]. Although our sample size was limited, these findings align with the general direction of the existing literature.
In the FRAIL study (2024) [7], frailty was associated with impairments across multiple HRQoL domains; our results reflect differences in the overall EQ-5D index. This may indicate a more global perception of health status in this population, although this interpretation should be made cautiously. Notably, frail patients in our study had a longer time since transplantation, which may suggest that frailty reflects cumulative processes over time, including aging, long-term exposure to immunosuppressive therapy, and post-transplant complications [21] .
The lack of clear HRQoL differences among prefrail participants in our sample may suggest that early stages of frailty were not clearly reflected in perceived health status. However, this finding should be interpreted with caution, as it may also reflect limited discriminatory ability of the telephone-administered self-reported FRAIL scale to distinguish between non-frail and prefrail states. This limitation may be particularly relevant in our population, which was not predominantly elderly, since subtle impairments in fatigue, resistance, or ambulation may be less easily captured through self-report than through objective performance-based measures such as grip strength or gait speed. This is consistent with the conceptualization of frailty as a dynamic and potentially reversible condition, in which transitions between states may occur before clinically meaningful deterioration becomes evident [22].
Alternatively, this finding may be related to limited statistical power or to clinical factors such as closer follow-up and optimized management in this subgroup. In addition, the high EQ-5D index values observed among non-frail and prefrail participants suggest a possible ceiling effect, which may have further limited the ability to detect small differences in HRQoL between these groups. Therefore, the absence of a statistically significant difference between non-frail and prefrail participants should not be interpreted as evidence that prefrailty is clinically irrelevant after heart transplantation. Evidence from longitudinal studies indicates that transitions from prefrailty to frailty represent a critical threshold associated with functional decline and adverse outcomes [23]. From a clinical perspective, prefrailty may represent a potential area for future research on early supportive strategies, as highlighted in current frameworks for frailty management [24]. In addition, the high EQ-5D index values observed among non-frail and prefrail participants suggest a possible ceiling effect, which may have further limited the ability to detect small differences in HRQoL between these groups. Therefore, the absence of a statistically significant difference between non-frail and prefrail participants should not be interpreted as evidence that prefrailty is clinically irrelevant after heart transplantation.
An additional exploratory finding was that participants with hypertension showed lower EQ-5D index scores. This finding may reflect a greater overall burden of comorbidity or more complex clinical profiles in this subgroup, which could negatively impact perceived health status. Alternatively, it may be influenced by residual confounding or treatment-related factors not fully captured in the analysis. Given the study design and sample size, this result should be interpreted with caution and considered exploratory [25].
Importantly, this study provides data from a middle-income country, contributing to a context that is underrepresented in the literature. Differences in healthcare access, socioeconomic conditions, and follow-up strategies may influence HRQoL outcomes, and our findings offer context-specific insights that complement existing evidence [26–28].
Several limitations should be acknowledged. The cross-sectional design precludes causal inference and limits assessment of temporal relationships. The relatively small sample size, particularly the number of frail patients, may affect statistical power and the stability of multivariable estimates. The single-center design may limit generalizability. HRQoL was assessed through telephone interviews, which may introduce reporting bias. Frailty was also assessed through a telephone-administered, self-reported instrument rather than objective performance-based measures. Therefore, weakness and ambulation were not measured using grip strength or gait speed, which may have introduced misclassification and limits direct comparability with studies using the Fried phenotype or other performance-based frailty assessments. Furthermore, the FRAIL scale was developed and validated primarily in older populations, whereas most participants in this study were not elderly. Its measurement properties and classification thresholds may therefore differ in younger and middle-aged heart transplant recipients. The use of the Ecuadorian EQ-5D value set may not fully reflect local preferences. Importantly, survivorship and selection bias should be considered when interpreting these findings. Of the 289 patients who underwent heart transplantation during the study period, 154 had died before HRQoL assessment, representing 53% of the original cohort. Therefore, the final analytical sample included only adult recipients who survived long enough to be contacted and complete the telephone interview. This survivor subset may represent a clinically healthier group than the overall transplant population. As a result, average HRQoL scores may have been overestimated, while the prevalence and impact of frailty may have been underestimated. Accordingly, our findings should be interpreted as applicable to long-term adult heart transplant survivors rather than to all heart transplant recipients. Residual confounding cannot be excluded, and frailty was assessed at a single time point, precluding evaluation of its trajectory over time.
Conclusions
In this cross-sectional study of long-term adult heart transplant survivors, frail participants showed lower HRQoL scores than non-frail participants, whereas prefrail participants did not show clear differences in patient-reported outcomes. These findings should not be generalized to the overall heart transplant population, because the study included only surviving and contactable adult recipients who were able to complete the HRQoL and frailty assessment. Given the exploratory nature of the study, the small number of frail participants, the cross-sectional design, and the potential for survivorship and selection bias, these results should be interpreted with caution. Further longitudinal studies are needed to clarify whether frailty status and HRQoL are temporally related in this population.
Acknowledgements
Not applicable.
Artificial intelligence use statement
An artificial intelligence tool, ChatGPT by OpenAI, was used solely to support language editing and improve the clarity and readability of the manuscript. All outputs were reviewed, validated, and revised by the authors, who take full responsibility for the final content. No artificial intelligence tools were used for data analysis, interpretation of results, or generation of study findings.
Authors’ contributions
AMJ contributed to data collection, database development, manuscript drafting, and critical revision. MLA, EFMH, and AHO contributed to study conceptualization, methodology, formal analysis, manuscript preparation, and final revision. JAVO contributed to data collection, database development, study conceptualization, manuscript preparation, and final revision. KCCH, AB, and LEE contributed to manuscript drafting and critical revision. All authors read and approved the final manuscript.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Data availability
The datasets generated and/or analyzed during the current study are not publicly available due to institutional and privacy restrictions but are available from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
This study was conducted in accordance with the ethical principles for research involving human participants and applicable institutional regulations. The study protocol was approved by the Institutional Ethics Committee of Fundación Cardiovascular de Colombia / Hospital Internacional de Colombia under approval number CEI 2026-11023-2. All participants provided informed consent prior to the telephone interview. Data were stored in a secure REDCap database with restricted access to authorized investigators, ensuring confidentiality and privacy.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analyzed during the current study are not publicly available due to institutional and privacy restrictions but are available from the corresponding author upon reasonable request.