Abstract
Background
Frailty is a dynamic state of vulnerability resulting from progressive functional decline and multimorbidity in patients with interstitial lung disease (ILD). The effect of lung transplantation (LTx) on frailty and its prognostic significance remains insufficiently understood.
Methods
This single-centre cohort study aimed to characterise peri-transplant frailty trajectories, measured by the clinical frailty scale (CFS), and determine their impact on long-term survival in patients with ILD undergoing LTx. CFS was assessed pre-operatively, at 4-months and 5-years post-transplant. Patients were categorised as fit (CFS 1–3), vulnerable (CFS 4) or frail (CFS 5–9). Frailty change (ΔCFS) was classified as improved (ΔCFS ≤−1), unchanged (ΔCFS 0) or worsened (ΔCFS ≥+1). Survival was analysed using Kaplan–Meier estimates and Cox proportional hazards models.
Results
The proportion of fit patients increased from 22.2% before to 87.5% 4 months and 75.0% 5 years post-transplant. Frailty improved in 93.1%, remained unchanged in 2.8% and worsened in 4.1% of patients. Median ΔCFS was −3 in frail, −1 in vulnerable and 0 in pre-LTx fit patients. Prolonged intensive care unit/hospital stay/ventilation was associated with reduced CFS recovery. Each one-point increase in ΔCFS was associated with a 1.76-fold higher mortality hazard (95% CI 1.34–2.31, p<0.001). Compared to those with improvement, patients with worsened frailty had a 43.9-fold higher mortality hazard (95% CI 10.1–173.2, p<0.001).
Conclusions
Peri-transplant CFS trajectory is associated with long-term survival in ILD, underscoring frailty as a modifiable risk factor and highlighting the need for systematic assessment and targeted interventions to optimise outcomes throughout the transplant course.
Shareable abstract
Peri-transplant CFS trajectory predicts long-term survival in ILD, underscoring frailty as a modifiable risk factor and reinforcing the need for routine assessment and targeted interventions to enhance patient outcomes across the transplant course https://bit.ly/4jZNVsh
Introduction
Patients with interstitial lung diseases (ILDs) often experience a progressive decline in pulmonary function due to chronic inflammation and fibrosis of the lung parenchyma, which leads to reduced physical performance, impaired quality of life and a high mortality [1, 2]. In addition to respiratory impairment, physical deconditioning, chronic hypoxaemia and medication-related side-effects, patients with ILD frequently suffer from multimorbidity and are particularly prone to developing frailty [3].
Frailty is a measure of accelerated functional aging and a state of diminished physiological reserves, and increased vulnerability towards physical and social stressors [4]. In ILD, frailty is associated with poorer medication tolerability, higher hospitalisation rates and poorer survival outcomes, contributing to the disease burden beyond lung function decline alone [5]. Several frailty assessment tools have been applied across healthy populations and individuals with chronic diseases, including ILD, with the clinical frailty scale (CFS) emerging as a simple, pragmatic alternative to more complex or research-oriented instruments [5].
Lung transplantation (LTx) is a potentially lifesaving intervention for patients with advanced fibrotic ILD, yet its effectiveness may be influenced by peri-operative risks and long-term complications [6]. Frailty has been observed frequently in this population and may be associated with adverse outcomes, however, its temporal dynamics around the time of transplantation and potential prognostic value remain insufficiently explored.
Study objectives
This study aimed to apply the CFS to characterise dynamic changes in frailty during the peri-transplant period, identify clinical and procedural factors influencing these trajectories, and evaluate their prognostic relevance for long-term survival over a 5-year follow-up period.
Methods
Study design and patient cohort
The study included 72 consecutive patients with ILD, who underwent LTx between March 2005 and October 2023 at the Lung Transplant Center and the Center for Interstitial and Rare Lung Diseases at the University of Giessen and the Marburg Lung Center, Giessen site. Patients were included in the European IPF/ILD Registry (eurIPFreg, eurILDreg) and provided informed consent for participation. The study has been approved by the Ethics Committee of Justus Liebig University Giessen (AZ 111/08) [7–9]. Clinical data were retrieved from medical records and registry questionnaires. ILD diagnoses were established in accordance with current American Thoracic Society/European Respiratory Society/Japanese Respiratory Society/Latin American Thoracic Association guidelines and confirmed through multidisciplinary team discussion [10].
Pre-transplant care included mandatory endurance and resistance training, nutritional assessment with supplementation as needed (with a body mass index (BMI) <17 or >30 kg·m−2 considered temporary contraindications) and strict evaluation of physical mobility, as prolonged immobilisation markedly reduces transplant eligibility. Post-transplant management consist of early mobilisation, structured rehabilitation immediately after discharge, weight control and a standardised triple immunosuppressive regimen with protocol-based monitoring and targeted adjustments only for complications such as post-transplant diabetes, alongside routine anti-infective prophylaxis.
The clinical frailty scale
The CFS is a validated, easy-to-use tool for assessing frailty in clinical practice, scoring patients on a scale from 1 (very fit) to 9 (terminally ill) and allowing stratification into fit (CFS 1–3), vulnerable (CFS 4) and frail (CFS 5–9) categories [11, 12]. The CFS can be assessed by healthcare professionals, care-givers or review of medical records [13–15]. Previous studies have demonstrated prognostic validity of the CFS in a diverse cohort of patients with fibrotic ILD [16, 17].
In this cohort, CFS was assessed within the registry framework at three key time-points, as follows: the last clinical visit before LTx, approximately 4 months post-LTx (first follow-up visit) and either the final available visit or the end of the 5-year period. The CFS was assessed retrospectively by experienced clinicians based on multidisciplinary case conferences and detailed pre- and post-transplant documentation. While inter-rater reliability was not formally evaluated in this retrospective analysis, all assessments were based on standardised criteria and discussed within transplant board meetings to enhance consistency. The peri-transplant change in frailty (ΔCFS) was calculated as the difference between pre- and 4-month post-LTx CFS and stratified as improved (ΔCFS ≤−1), unchanged (ΔCFS=0) or worsened (ΔCFS ≥+1).
Other measurements and outcomes
To comprehensively evaluate patient status before and after LTx, multiple standardised assessments were performed as part of routine clinical care. Donor lung allocation was governed by the lung allocation score, as regulated by Eurotransplant (www.eurotransplant.org) [18]. Post-transplant management followed multidisciplinary protocols, including intensive monitoring in the early post-operative phase and subsequently extended follow-up intervals aimed at optimising graft function and overall outcomes [19].
Demographic and clinical baseline variables included age (in years), sex, BMI (kg·m−2) and the presence or absence of idiopathic pulmonary fibrosis (IPF) and pulmonary hypertension. Haemodynamic parameters such as systolic pulmonary artery pressure (sPAP) (mmHg) and tricuspid annular plane systolic excursion (TAPSE) (mm) were documented.
Pulmonary function tests (PFTs) and the 6-min walk distance (6MWD) (m) were conducted in accordance with internationally standardised protocols [20–22]. Parameters assessed during PFTs were expressed as a percentage of the predicted value and included forced vital capacity (FVC), total lung capacity, intrathoracic gas volume and diffusing capacity of the lung for carbon monoxide (DLCO).
The comorbidity–polypharmacy score (CPS) is a composite measure of overall disease burden and treatment complexity, reflecting both multimorbidity and medication load. CPS was calculated as the sum of chronic comorbidities and the number of concurrently prescribed medications at the time of evaluation; chronic conditions were defined as physician-diagnosed, nontransient medical diagnoses requiring ongoing management, while medications included all long-term prescriptions recorded in the patient's treatment plan, excluding short-term peri-operative treatments [23]. Laboratory analyses were conducted at the certified institutional laboratories of the University of Giessen and the Marburg–Giessen site, using standardised procedures. Biomarkers measured included serum albumin (g·L−1), total serum protein (g·L−1) and C-reactive protein (CRP) (mg·L−1) and were obtained in parallel with key clinical assessments [24].
Further peri-operative data were also collected to capture surgical and recovery-related variables. These included the duration of mechanical ventilation, length of stay in the intensive care unit (ICU) and total hospital stay, all measured in days from the time of LTx. Intra-operative use of extracorporeal membrane oxygenation (ECMO) was recorded as a binary variable and the duration of surgery was documented in hours, from the initial skin incision to final closure.
Statistical analysis
All statistical analyses were performed using R version 4.2.2 (www.r-project.org), employing the following packages: DescTools, Hmisc, ggsankey, lme4, sjPlot, survival, emmeans, finalfit and relevant tidyverse packages for data analysis and visualisation [25].
Descriptive statistics were used to summarise baseline clinical and functional characteristics stratified by pre-transplant frailty status (fit versus vulnerable/frail). Continuous variables were reported as median (interquartile range) and categorical variables as frequencies and percentages. Between-group comparisons were performed using the Wilcoxon test or Fisher's exact test for categorical variables.
Transitions in frailty categories over time were performed using the Kruskal–Wallis test and visualised using Sankey diagrams and violin plots. Peri-transplant frailty change (ΔCFS) was defined as the numeric difference between 4-month and pre-LTx CFS, and categorised as improved (ΔCFS ≤−1), unchanged (ΔCFS=0) or worsened (ΔCFS ≥+1).
Correlation analyses between ΔCFS and clinical or procedural parameters (e.g. age, BMI, PFTs, CRP, albumin, 6MWD, gender–age–physiology (GAP), ventilation duration, ICU and hospital length of stay) were performed using Spearman's rank or Pearson's correlation coefficients, depending on variable distribution. The impact of ΔCFS on peri-operative outcomes (e.g. length of stay) was further assessed using multivariable linear correlation models including baseline CFS as a covariate.
Survival analyses were conducted using Kaplan–Meier estimates and log-rank tests, stratified by ΔCFS trajectory groups. Time-to-event was calculated from the last pre-transplant clinical assessment to death or censoring to 5 years post-LTx. The prognostic impact of ΔCFS on mortality was assessed using univariate and multivariate Cox proportional hazards models, adjusted for age, sex, pre-transplant CFS and IPF. Hazard ratios with 95% confidence intervals and p-values were reported, with statistical significance defined as p<0.05.
Results
Clinical and functional characteristics of the study cohort
The cohort comprised 72 patients, with IPF being the most prevalent diagnosis (33 patients, 45.8%), including one case of combined pulmonary fibrosis and emphysema. The remaining patients had hypersensitivity pneumonitis (12, 16.7%), systemic autoimmune rheumatic disease-associated ILD (8, 11.1%), unclassifiable ILD (7, 9.7%), unclassifiable idiopathic interstitial pneumonia (6, 8.3%) or sarcoidosis (6, 8.3%). Patient characteristics according to pre-transplant CFS are summarised in table 1. Patients categorised as fit (n=15) were generally younger and exhibited more favourable clinical profiles compared to vulnerable or frail patients (n=57). Specifically, fit patients exhibited a lower CPS and a lower prevalence of IPF, higher values FVC, DLCO and 6MWD, lower sPAP, and higher TAPSE. Nutritional and inflammatory markers were also more favourable in fit compared to vulnerable/frail patients, with higher albumin and lower CRP levels. The median pre-transplant CFS was 3.0 in the fit group versus 5.0 among vulnerable/frail patients. The groups were similar in BMI and time from ILD diagnosis to LTx.
TABLE 1.
Clinical and functional characteristics of fit and vulnerable/frail patients pre-transplant
| Variable | Fit (CFS ≤3) (n=15) | Vulnerable/frail (CFS ≥4) (n=57) | Total (N=72) | p-value |
|---|---|---|---|---|
| Age, years | 55.4 (49.9–60.5) | 59.6 (53.3–63.5) | 58.6 (52.6–63.3) | 0.14 |
| Sex, male, n (%) | 10 (67) | 32 (56) | 42 (58) | 0.56 |
| BMI, kg·m−2 | 25.4 (22.1–27.2) | 25.2 (21.2–27.1) | 25.4 (21.6–27.1) | 0.62 |
| Time from diagnosis, months | 69 (52–125) | 82 (48–127) | 79 (49–126) | 0.84 |
| CPS | 16.0 (12.0–21.0) | 17.0 (13.0–20.5) | 16.0 (13.0–20.8) | 0.71 |
| ΔCPS | 13.0 (9.5–15.5) | 13.0 (9.0–17.0) | 13.0 (9.0–16.5) | 0.76 |
| GAP score at LTx time-point | 5.0 (4.0–5.0) | 5.0 (4.0–6.0) | 5.0 (4.0–6.0) | 0.16 |
| GAP score at diagnosis | 2.5 (2.0–3.8) | 3.0 (2.0–4.0) | 3.0 (2.0–4.0) | 0.83 |
| ILD diagnosis: IPF, n/N (%) | 6/15 (40) | 29/57 (51) | 35/72 (49) | 0.57 |
| FVC (% pred) | 38 (30–45) | 33 (26–45) | 34 (27–46) | 0.26 |
| TLC (% pred) | 44 (41–52) | 51 (41–62) | 49 (41–61) | 0.43 |
| ITGV (% pred) | 48.2 (45.3–58.4) | 63.5 (51.1–72.9) | 58.4 (46.5–70.3) | 0.03 |
| DLCO SB (% pred) | 20 (17–24) | 14 (11–20) | 15 (11–21) | 0.11 |
| 6-min walk distance, at LTx time-point, m | 320 (266–360) | 150 (72–200) | 191 (100–284) | <0.001 |
| 6-min walk Borg score, at LTx time-point, points | 4.0 (3.0–5.5) | 7.0 (4.0–9.0) | 6.0 (4.0–9.0) | <0.001 |
| Pulmonary hypertension, n/N (%) | 7/15 (47) | 17/57 (30) | 24/72 (33) | 0.232 |
| sPAP, mmHg | 43 (27–53) | 45 (36–68) | 45 (34–68) | 0.066 |
| TAPSE, mm | 23.0 (22.2–24.8) | 21.0 (19.0–24.0) | 22.0 (20.0–24.0) | 0.064 |
| Albumin, g·L−1 | 42.8 (41.9–45.8) | 40.2 (36.7–43.5) | 41.5 (37.1–43.6) | 0.02 |
| Total protein, g·L−1 | 80 (73–85) | 77 (69–82) | 77 (70–83) | 0.24 |
| CRP, mg·L−1 | 6.5 (4.1–9.4) | 11.8 (4.6–18.5) | 8.6 (4.5–17.2) | 0.035 |
| ICU length of stay, days | 14 (7–32) | 24 (12–42) | 20 (11–42) | 0.085 |
| Hospital length of stay, days | 21 (18–38) | 42 (23–60) | 35 (21–60) | 0.069 |
| Duration of mechanical ventilation, h | 12 (6–240) | 72 (24–504) | 51 (14–432) | 0.055 |
Values are shown as median (interquartile range) or percentage of the study cohort. The Wilcoxon test and Fisher's exact test for count data were used. BMI: body mass index; CFS: clinical frailty scale; CPS: comorbidity–polypharmacy score; CRP: C-reactive protein; DLCO SB: diffusing capacity of the lung for carbon monoxide, single-breath, corrected; FVC: forced vital capacity; GAP: gender–age–physiology; ICU: intensive care unit; ILD: interstitial lung disease; IPF: idiopathic pulmonary fibrosis; ITGV: intrathoracic gas volume; LTx: lung transplantation; sPAP: systolic pulmonary artery pressure; TAPSE: tricuspid annular plane systolic excursion; TLC: total lung capacity; ΔCPS: change in comorbidity–polypharmacy scale.
Peri-transplant changes of the CFS
CFS trajectories over the study period are illustrated in figure 1a–b. Before LTx, 21% of patients were classified as fit, while 38% were vulnerable and 42% were frail. 4 months after LTx, the proportion of fit patients rose markedly to 88%, accompanied by a substantial decline in vulnerability (4%) and frailty (8%). Among those initially frail, 87% improved to fit and 13% remained frail. Of the patients who were vulnerable pre-LTx, 85% transitioned to fit, 11% remained vulnerable and 4% progressed to frail. Among the fit group, 93% maintained their status, while 7% became frail, in three cases CFS data at the end of observational period were missing.
FIGURE 1.
a–b) Trajectories of the clinical frailty scale (CFS) pre- and post-transplant. LTx: lung transplantation.
Determinants of peri-transplant CFS trajectories
Pre-transplant frailty, as measured by the CFS, showed a strong inverse correlation with ΔCFS (r= −0.69, p<0.001), indicating that patients with higher baseline frailty tended to experience the greatest numerical improvements in CFS after LTx. This also resulted in a negative correlation between ΔCFS and the GAP score (r= −0.26, p=0.031) and a positive correlation between ΔCFS and the 6MWD (r=0.35, p=0.004). Other clinical variables, such as age (r=0.14, p=0.241), sex (r= −0.22, p=0.07) and BMI (r=0.14, p=0.258), did not significantly correlate with ΔCFS. Comorbidities (CPS) and PFT parameters showed no significant associations with ΔCFS; similarly, biomarkers (e.g. albumin, total protein) and procedural metrics (ECMO use, ventilation duration, surgery duration, ICU/hospital length of stay) were not significantly associated with ΔCFS on unadjusted analyses (e.g. ECMO use r= −0.22, p=0.06; duration of surgery r= −0.21, p=0.095).
Patients with the average ICU stay had overall a frailty recovery and a peri-transplant decline in CFS of 2.12 points. Multivariable analyses accounting for baseline CFS showed that for each additional 10 days spent in the ICU, this frailty recovery was diminished by 0.17 points (95% CI 0.12–0.21, p<0.001). Similarly, each additional 10 days of hospital stay and each 10 additional days on the ventilator reduced the recovery in CFS by 0.14 (95% CI 0.10–0.19, p<0.001) and 0.14 (95% CI 0.10–0.17, p<0.001) points, respectively.
The association between longer ICU and overall hospital stay with impaired frailty recovery was strongest in patients who were frail before transplantation, with each 10 additional days in ICU and in hospital impacting on CFS recovery by 0.48 (95% CI 0.38–0.58, p<0.001) and 0.41 (95% CI 0.29–0.53, p<0.001), respectively (table 2).
TABLE 2.
Relationship between length of intensive care unit (ICU) and hospital stay and change in clinical frailty scale (ΔCFS) according to pre-transplant frailty category
| Fit (CFS ≤3) | Vulnerable (CFS=4) | Frail (CFS ≥5) | ||||
|---|---|---|---|---|---|---|
| ΔCFS (95% CI) | p-value | ΔCFS (95% CI) | p-value | ΔCFS (95% CI) | p-value | |
| Length of ICU stay per 10 days | 0.10 (0.06–0.13) | <0.001 | 0.21 (0.12–0.30) | <0.001 | 0.48 (0.38–0.58) | <0.001 |
| Length of hospital stay per 10 days | 0.09 (0.05–0.13) | <0.001 | 0.11 (0.03–0.19) | 0.006 | 0.41 (0.29–0.53) | 0.006 |
For every additional 10 days of ICU stay, patients with a pre-lung transplantation (LTx) fit status had 0.1 point higher and patients with a pre-LTX frail status a 0.5 point higher ΔCFS – signifying less frailty recovery.
Impact of peri-transplant CFS trajectories on survival
Outcome analysis stratified by ΔCFS demonstrated that patients in the improved ΔCFS group (n=59) showed survival rates of 96.6% at 1 year, 93.2% at 2 years and 52.5% at 5 years, with 31 patients being alive at 5 years (figure 2). The unchanged ΔCFS group (n=7) exhibited survival rates of 100% at 1 year, 85.7% at 2 years and 71.4% at 5 years, with six patients being alive at 5 years. The worsened ΔCFS group (n=5) showed markedly worse outcomes, with survival rates of to 40.0% at 1 year, 20.0% at 2 years and 0% at 5 years. Peri-transplant ΔCFS was significantly associated with long-term survival on unadjusted analyses and independent of age, sex, IPF diagnosis and pre-transplant CFS. Every one-point increase in ΔCFS was associated with a 1.76-fold higher mortality risk (95% CI 1.34–2.31, p<0.001). Patients with worsening frailty peri-transplant showed a 43.9-fold higher risk of death (95% CI 11.1–173.2, p<0.001) (table 3).
FIGURE 2.
Post-transplant survival stratified by change in clinical frailty scale (CFS) trajectories. Improved signifies a decline in CFS ≥1 and worsened an increase in CFS ≥1. LTx: lung transplantation.
TABLE 3.
Association between peri-transplant frailty trajectory (change in clinical frailty scale (ΔCFS)) and post-transplant mortality risk
| Unadjusted HR (95% CI) | Unadjusted p-value |
Adjusted HR (95% CI)# | Adjusted p-value |
|
|---|---|---|---|---|
| ΔCFS (per one unit increase) | 1.45 (1.14–1.85) | 0.003 | 1.76 (1.34–2.31) | <0.001 |
| Improved/unchanged versus worsened | 3.15 (1.44–6.86) | 0.004 | 3.51 (1.42–8.67) | 0.006 |
| Improved versus unchanged | 1.52 (0.52–4.42) | 0.443 | 1.48 (0.45–4.84) | 0.519 |
| Improved versus worsened | 36.71 (10.05–134.14) | <0.001 | 43.88 (11.11–173.24) | <0.001 |
HR: hazard ratio. #: Adjusted for baseline age, sex, idiopathic pulmonary fibrosis diagnosis and pre-transplant CFS.
Discussion
This study provides the first longitudinal evaluation of frailty trajectories, assessed by the CFS, across the peri-transplant period and 5 years follow-up in patients with advanced fibrotic ILD. Frailty represents a multidimensional loss of physiological reserve and increases susceptibility to otherwise minor stressors, adversely affecting quality of life and increasing hospitalisation risk, and is associated with mortality in patients with ILD and lung transplant candidates, supporting the recommendation for routine frailty assessment in ILD in both clinical practice and research [3]. Among available tools, the CFS is particularly suited to the transplant setting, providing a pragmatic global assessment that can be repeatedly applied at clinically meaningful time points [26]. Although most patients were vulnerable or frail before transplantation, more than 85% transitioned to a fit state after LTx. By 5 years post-transplant, however, approximately one quarter had redeveloped vulnerability or frailty, highlighting frailty as a dynamic process extending beyond the early post-operative phase.
These findings support frailty after LTx as a dynamic, event-driven phenotype with potentially modifiable post-transplant determinants. In advanced fibrotic ILD, frailty appears to be largely driven by pulmonary disease severity, encompassing not only ventilatory limitation and hypoxaemia but also systemic inflammation, metabolic dysregulation and physical deconditioning [27]. The pronounced early post-transplant recovery observed in many frail patients suggests that replacement of the diseased lung alleviates these systemic stressors, rendering a substantial component of frailty at least partially reversible [28]. This framework emphasises differences between pulmonary and extrapulmonary frailty phenotypes rather than frailty severity alone and underscores the need for event-anchored frailty monitoring and validation of trajectory phenotypes in larger cohorts [29].
Peri-transplant exposures emerged as key determinants of frailty recovery [30]. Longer ICU stay, prolonged hospitalisation and extended mechanical ventilation were independently associated with attenuated frailty improvement. While patients with an average ICU stay experienced substantial peri-transplant recovery, each additional 10 days of ICU or hospital exposure significantly reduced this benefit. Stratification by baseline frailty revealed a pronounced vulnerability gradient; fit patients showed only modest impairment, whereas vulnerable and frail patients experienced disproportionate worsening. In pre-transplant frail patients, each additional 10 days of ICU stay was associated with a clinically meaningful increase in CFS, several-fold greater than that observed in fit individuals. These findings show that prolonged ICU exposure encompasses immobility, sedation, delirium, catabolic metabolism, dysphagia, ventilator-associated complications and recurrent infections, all of which accelerate muscle loss and erode functional reserve in patients with limited physiological resilience.
Multiple risk factors for frailty are prevalent in LTx recipients, including physical inactivity, sarcopenia, malnutrition, chronic systemic inflammation, medication side-effects and limited social support [31]. Failure to recover from frailty after transplantation was associated with a markedly increased risk of mortality [32]. Each one-point increase in post-transplant CFS conferred an approximately 1.8-fold higher risk of death, independent of pre-transplant frailty, age, sex or ILD subtype. These findings indicate that post-transplant frailty captures a broader and more clinically relevant vulnerability state than pre-transplant frailty alone, likely reflecting the cumulative impact of residual disease burden, peri-operative injury and new-onset complications. Treatment-related factors, including prolonged corticosteroid exposure, calcineurin inhibitor toxicity and other immunosuppressive side-effects, may further contribute to muscle wasting, functional decline and reduced physiological reserve.
Within our study's frail–fit–frail subgroup, individual-level trajectory analyses were consistent with the hypothesis that recurrent infections and evolving allograft dysfunction contribute to late frailty deterioration through repeated inflammatory insults, hospitalisations, corticosteroid exposure and prolonged inactivity, suggesting that secondary frailty represents a post-transplant phenotype distinct from end-stage lung disease-associated frailty. Worsening frailty after transplantation may therefore also signal ILD-independent vulnerability, including sarcopenia, cardiovascular comorbidity or neurocognitive impairment unmasked or exacerbated in the post-transplant setting, underscoring the importance of longitudinal frailty monitoring beyond the immediate post-operative period.
The clinical implications are substantial. Patients entering transplantation in a frail state are at highest risk of incomplete recovery, supporting the integration of frailty into peri-operative risk stratification and resource planning [33]. Although standardised early mobilisation, structured in-ICU physiotherapy, early enteral nutrition with individualised caloric and protein targets, together with post-discharge pulmonary rehabilitation, are routinely implemented, our findings suggest that uniform peri-operative strategies may be insufficient for patients with baseline frailty [34]. Targeted approaches, including frailty-specific rehabilitation pathways, individualised nutritional supplementation, structured interdisciplinary rehabilitation rounds, prehabilitation prior to transplantation and closer geriatric co-management, warrant prospective evaluation [35]. Importantly, the observation that a subset of initially fit patients deteriorated over time cautions against assuming protection in this group and supports longitudinal frailty surveillance across all transplant recipients.
Our findings should be interpreted in the context of a growing body of literature examining frailty in LTx. Prior studies have consistently demonstrated that frailty at listing or transplantation is associated with increased peri-operative complications, longer hospitalisation and mortality [3, 26, 36, 37]. More recent work has begun to explore frailty as a dynamic construct, with reports showing that frailty may improve after LTx in selected recipients [38, 39]. However, most available studies are limited by short-term follow-up, cross-sectional or binary frailty assessments, heterogeneous transplant indications or reliance on single pre- or early post-transplant time-points.
The present study extends this literature by systematically characterising peri-transplant frailty trajectories in a well-defined cohort of patients with fibrotic ILD, with longitudinal follow-up up to 5 years after transplantation. By focusing on within-patient change rather than baseline frailty status alone, we demonstrate that peri-transplant frailty dynamics provide prognostic information beyond static frailty measures. In particular, our finding that post-transplant frailty, and not pre-transplant frailty per se, is most strongly associated with survival highlights the clinical relevance of longitudinal frailty assessment. This trajectory-based approach complements prior work by Singer et al. [40] and others by identifying recovery, persistence and secondary deterioration as distinct phenotypes with differential prognostic implications, thereby advancing frailty from a descriptive risk marker to a modifiable longitudinal outcome in LTx.
Strengths of this study include its longitudinal design, focus on within-patient change and integration of detailed clinical, functional and procedural data. Limitations include the single-centre setting, limited power for subgroup analyses and reliance on the CFS, which may lack granularity at extreme values and is subject to observer variability. Relevant domains such as sarcopenia extent, cognitive function and psychosocial determinants were not directly assessed. Although follow-up extended beyond 5 years for some patients, survival estimates beyond this timeframe were not interpreted due to limited numbers at risk.
Conclusions
Peri-transplant change in frailty (ΔCFS) provides independent and incremental prognostic value beyond static frailty assessment in ILD patients undergoing LTx. Integrating longitudinal frailty monitoring into routine post-transplant care enables early identification of high-risk trajectories and supports personalised, multidisciplinary strategies to optimise long-term outcomes in this vulnerable population.
Acknowledgments
We gratefully acknowledge RARE-ILD consortium and German Center for Lung Research, as well as participating patients and physicians, who contributed data to the eurIPFreg and eurILDreg and supported the continuation of this effort. During early stages of manuscript preparation, www.deepl.com, a web-based machine translation and language assistance tool, was used in a very limited manner as a language assistance tool to translate short passages originally drafted in German into English. All scientific content, analyses, interpretations, and final wording were created and revised by the authors.
Footnotes
This article has an editorial commentary: https://doi.org/10.1183/23120541.00224-2026
Provenance: Submitted article, peer reviewed.
Ethics statement: The European IPF/ILD Registry and Biobank (eurIPFreg/eurILDreg) operates under full ethical approval from the Ethics Committee of the Justus-Liebig-University Giessen (approval number 111/08). All study procedures comply with the principles of the Declaration of Helsinki and written informed consent was obtained from all participating patients prior to inclusion. Originally established in 2009 within the framework of the European IPF Network, the registry has since expanded to encompass additional interstitial lung diseases, evolving into eurILDreg. Both registries are officially registered (eurIPFreg: ClinicalTrials.gov identifier NCT02951416; eurILDreg: DRKS00028968). All authors confirm that the necessary ethical approvals and patient consents for participation and publication were obtained, and that the study fully adheres to institutional and international standards for biomedical research.
Author contributions: E. Krauss, S. Guler, S. Kuhnert, L. Wilke and A. Guenther analysed and interpreted the patient data and wrote the manuscript. S. Kuhnert was responsible for data extraction. A.C. Windhorst performed the statistics analyses. E. Krauss, L. Wike, S. Tello and A. Guenther recruited ILD patients for the registry and have been involved in drafting or revising the manuscript for important intellectual content. All authors have read and agreed to the published version of the manuscript.
Conflict of interest: All authors report no conflicts of interest.
Support statement: The eurIPFreg/eurILDreg, established under European Union funding and now independently operated by TransMIT and JLU Giessen, is supported by public and industry partners and governed by a Steering Committee chaired by Andreas Guenther.
Data availability
The data supporting the findings of this study originate from the European Registry and Biobank for Interstitial Lung Diseases (eurIPFreg, eurILDreg). Access to anonymised individual-participant data, metadata and biospecimens can be granted to qualified researchers upon reasonable request and following approval by the Registry Steering Committee in accordance with its data governance framework. Information on the registry's data structure, variable catalogue and standardised data elements is publicly available via the Medical Data Models Portal (eurILDreg – Portal für Medizinische Datenmodelle): https://medical-data-models.org. Requests for data access or collaboration should be directed to the corresponding author or submitted through the eurILDreg coordination office.
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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 data supporting the findings of this study originate from the European Registry and Biobank for Interstitial Lung Diseases (eurIPFreg, eurILDreg). Access to anonymised individual-participant data, metadata and biospecimens can be granted to qualified researchers upon reasonable request and following approval by the Registry Steering Committee in accordance with its data governance framework. Information on the registry's data structure, variable catalogue and standardised data elements is publicly available via the Medical Data Models Portal (eurILDreg – Portal für Medizinische Datenmodelle): https://medical-data-models.org. Requests for data access or collaboration should be directed to the corresponding author or submitted through the eurILDreg coordination office.