HCT Frailty Scale (HCT-FS) for assessing frailty in adult candidates for allogeneic haematopoietic cell transplantation: an international prospective, observational cohort study.

Research in context

Evidence before this study

We searched PubMed, Embase, and Web of Science from database inception to Dec 1, 2024, using the terms “frailty”, “geriatric assessment”, “hematopoietic cell transplantation”, “allogeneic transplant”, and “HCT frailty scale”, limited to English and Spanish language publications. This search yielded 152 studies, of which most focused on heterogeneous geriatric assessments, older adults only, or resource-intensive tools not feasible in routine allogeneic HCT settings. No prior study had validated a frailty scale specifically designed for universal use across adult allo-HCT candidates or assessed its prognostic performance across international cohorts in real-world practice.

Added value of this study

This study provides the first international, prospective, real-world validation of the HCT-FS, a practical frailty scale specifically designed for routine allo-HCT workflows and applicable across all adult age groups. By integrating data from 16 centres across two countries, we demonstrate that HCT-FS reliably stratifies clinical risk, predicts survival and non-relapse mortality independently of age and comorbidity burden, and maintains prognostic accuracy across diverse clinical settings. Importantly, we show that implementation is feasible without external resources, specialised personnel, or dedicated geriatric teams.

Implications of all the available evidence

Accumulating evidence indicates that frailty is a strong and independent determinant of outcomes after allo-HCT, and that practical, standardised approaches such as the HCT-FS can inform clinical decision-making and risk stratification at scale. The current findings support the incorporation of frailty assessment into routine pre-transplant evaluation globally. Future work should evaluate whether targeted prehabilitation or mitigation strategies based on frailty status can improve post-transplant outcomes.

Introduction

Allogeneic haematopoietic cell transplantation (allo-HCT) remains a potentially curative treatment for patients with high-risk haematological malignancies, but still carries substantial risks of morbidity and mortality. 1 , 2 Advances in transplant platforms, donor matching, and supportive care have reduced transplant-related mortality—from approximately 40% in the 1980s to 10–20% today. 3 , 4 These improvements have broadened eligibility, allowing older adults and patients with complex comorbidities to undergo allo-HCT. 5 , 6

In response to the aging transplant population, the American Society of Clinical Oncology (ASCO) recommends comprehensive geriatric assessments for older adults receiving cancer treatment. 7 As older patients increasingly receive allo-HCT with chemo-radiation conditioning, frailty assessment has emerged as a crucial component of pre-transplant evaluation. 8 , 9 Frailty, a multidimensional syndrome characterised by decreased physiological reserve and increased vulnerability to stressors, 10 , 11 and is linked to worse outcomes in allo-HCT candidates— transplant-related complications and lower survival—independent of patient's age and comorbidities.

Although HCT centres have integrated frailty assessments into their practices, methodologies have been heterogeneous, resource-intensive, and often limited to older patients based on arbitrary age thresholds, restricting broader applicability. 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 To address these limitations, the Hans Messner Allo-HCT Program at Princess Margaret Cancer Center (PMCC) in Toronto (ON, Canada) developed the Hematopoietic Cell Transplantation Frailty Scale (HCT-FS), a practical tool designed to be integrated into routine workflows using existing resources. 22 , 23 The HCT-FS categorises patients as fit, pre-frail, or frail and demonstrated strong prognostic value at PMCC, with pre-frail and frail patients showing reduced overall survival (OS), compared with fit patients. The scale was developed using data from 298 consecutive patients assessed for frailty prior to allo-HCT at PMCC between 2018 and 2020. 23 The cohort was randomly divided into a 198-patient training set, in which variable weights were derived from multivariable regression analyses using hazard ratios for overall survival, and a 100-patient internal validation set, in which these weights were subsequently tested. A subsequent multicentre study by the Grupo Español de Trasplante Hematopoyético y Terapia Celular (GETH-TC), involving 15 centres across Spain, validated these findings, supporting the broader utility of HCT-FS. 24

Building on this evidence, the present study integrates all frailty data obtained from all allo-HCT patients evaluated at PMCC and GETH-TC centres in real-world settings, aiming to assess the clinical utility of the HCT-FS and investigate its applicability across diverse age groups and comorbidity profiles.

Methods

Study design and participants

This prospective observational study included all adult patients evaluated for frailty at the PMCC (n = 734, 68.1%) between 2018 and 2024 and at 15 GETH-TC centres (n = 323, 31.9%) between 2022 and 2023, using the HCT-FS. All adults consulted for allo-HCT at the participating institutions were considered eligible to be included in the study and evaluated for frailty after providing informed consent. For the present analysis, only patients who ultimately proceeded to allo-HCT were included in the study ( Flowchart included in Supplementary Material ). Data from non-evaluated or non-transplanted patients at the participant institutions was not collected.

This study was an observational study, meaning that frailty assessments did not influence transplant eligibility, and was conducted in a real-world setting and without external funding. None of the patients included underwent pre-transplant prehabilitation. Data were prospectively updated until February 2025.

Ethics

The study adhered to the Declaration of Helsinki and STOBE guidelines, and was approved by the Ethics Committees of PMCC, Hospital Clínic de Barcelona and GETH-TC (HCB/2021/0766). Written informed consent was obtained from all participants prior to frailty assessment and data collection.

HCT frailty scale

The HCT-FS, summarised in Fig. 1 A, categorises patients as fit, pre-frail, or frail based on a cumulative weighted score derived from eight variables: Clinical Frailty Score, 25 Instrumental Activities of Daily Living test, 26 grip strength score, 27 Timed Up and Go Test, 28 Self-Rated Health question, falls test, together with serum albumin and C-reactive protein levels at time of the evaluation. 29

Developed at PMCC, the HCT-FS was specifically designed to assess candidates for allo-HCT across all age groups and incorporates eight clinically and functionally relevant variables, carefully selected for their applicability in frailty assessment within geriatric oncology and allo-HCT settings ( Supplementary Material ). 30 For the classification of patients into fit, pre-frail and frail categories, an individual frailty score is calculated by summing the weighted contributions of all abnormal findings (normal results score zero), with total scores determining the final classification. 23 Further details are provided in the Supplementary Material and can also be consulted in the QR shown in Fig. 1 ( https://hctfrailtyscale.com ).

During the conduct of the study, frailty assessments were standardised across all centres. Dr. Salas, a member of the original HCT-FS development team at PMCC, helped with the elaboration of training materials (written guides and a video tutorial: https://youtu.be/RPrCnWothlY ), which were shared with GETH-TC centres. Dr. Salas also served as consultant throughout the multicentre implementation. Transplant teams, including haematologists and trained nursing staff, performed the HCT-FS evaluations in approximately 10–15 min per patient, without external personnel or additional funding, ensuring feasibility and scalability in routine practice.

Pre-transplant evaluation and allo-HCT procedures

Each transplant centre followed its institutional protocols for donor selection and transplant platform. Conditioning intensity was generally based on age and comorbidity burden. Most patients aged ≥55 years or those with significant comorbidities (HCT-CI >2) received reduced-intensity conditioning (RIC). Data on transplant complications and post-transplant disease status were recorded locally. GVHD was graded using standard classification systems. 31 , 32 Key definitions are outlined in the Supplementary Material .

Outcomes

The pooled analysis included 1077 adult patients from the Canadian and GETH-TC cohorts. Frailty status, defined by the HCT-FS and categorised as fit, pre-frail, or frail, was analysed as the principal explanatory variable.

The primary outcomes were OS and non-relapse mortality (NRM), which were used to assess the prognostic performance and risk discrimination ability of the HCT-FS across frailty categories.

Additional outcomes included the cumulative incidence of relapse (CIR), acute and chronic graft-vs.-host disease (GVHD), infectious complications, and intensive care unit (ICU) admission. The relationship between frailty categories and these post-transplant complications was evaluated to further characterise the prognostic impact of frailty.

The prognostic accuracy of the HCT-FS was assessed by evaluating its ability to discriminate clinical outcomes across frailty categories in the overall cohort and, subsequently, by testing the consistency of these associations within specific patient subgroups. Subgroup analyses examined the distribution of frailty categories and their relationship with post-transplant outcomes across centres (PMCC and GETH-TC), age groups (18–40, 41–59, and ≥60 years), and comorbidity levels defined by the HCT-CI (≤2 vs. >2). The objective of these analyses was to determine whether the HCT-FS maintains its prognostic validity and discriminatory power across diverse patient populations and clinical settings. All subgroup analyses (by centre, age group, and HCT-CI category) were pre-specified in the study protocol. Interaction testing between frailty and each subgroup variable was added during peer review and is therefore considered a post-hoc analysis.

Statistical analysis

Categorical and discrete variables were summarised as counts and percentages, while continuous variables were expressed as medians with ranges or interquartile ranges (IQR). Associations between baseline characteristics and frailty categories were analysed using multivariate binary logistic regression.

Post-transplant follow-up was measured from the date of allogeneic stem cell infusion to the date of last follow-up or death from any cause. OS was defined as the time from transplantation to death from any cause, with surviving patients censored at the date of last contact. OS was estimated using the Kaplan–Meier method, and survival differences between frailty categories were assessed with the log-rank test. NRM was defined as death without disease relapse or progression and was estimated using the Fine and Gray competing risk model, considering relapse as a competing event. Conversely, the cumulative incidence of relapse (CIR) was defined as the time from transplantation to haematological relapse or disease progression, with death without relapse treated as a competing event. The cumulative incidences of acute and chronic GVHD were estimated using competing risk analyses, considering both death and relapse as competing events. Similarly, the incidences of infectious complications and ICU admission were analysed with death treated as a competing event. All time-to-event outcomes were analysed from the date of stem cell infusion to ensure consistent temporal alignment across endpoints. The impact of frailty on OS and NRM was further evaluated through univariate and multivariate Cox and Fine–Gray regression analyses (UVA and MVA). Subgroup analyses were performed by centre, age group, and HCT-CI category, consistent with the prespecified analytical plan. Sensitivity analyses included repeating all models stratified by centre, age group, and comorbidity burden to assess robustness. Interaction terms were added to multivariable models during peer review to evaluate effect modification; these analyses are therefore labelled as post-hoc. Statistical significance was set at p < 0.05, and all analyses were performed using EZR software version 1.61. 33

Role of the funding source

No funding was received for this work.

Results

The present study combines data from all patients recruited between 2018 and 2024 and evaluated for frailty using the HCT–FS at PMCC and the 15 GETH–TC institutions that participated in this investigation. The scale was implemented using existing human resources available at the participating HCT units. In over 80% of the centres, assessments were performed by haematologists or nurse coordinators with experience in evaluating allo–HCT candidates. Of the total patients consulted for allo–HCT, approximately 5–10% were excluded from the analysis because they missed the frailty assessment, did not proceed to transplantation, or lacked complete data for classification as fit, pre-frail, or frail.

Among the 1077 adult patients included, 734 (68.1%) were from PMCC and 343 (31.9%) from 15 GETH-TC centres ( Fig. 1 B). The median age was 56 years (range 18–76); 411 patients (38.2%) were over 60, and 640 (59.4%) were male. As outlined in Table 1 , the most common underlying diagnoses were acute myeloid leukemia (46.2%) and myelodysplastic syndromes (19.0%). The median time from first consultation to transplant admission was 53 days (IQR: 43–63).

At the first allo-HCT consultation, 360 patients (33.4%) were classified as fit, 579 (53.7%) as pre-frail, and 138 (12.8%) as frail. As shown in Fig. 1 , the proportion of frail patients was similar between PMCC and GETH-TC centres (12.8% vs. 12.5%); however, the percentage of fit patients was higher in PMCC (36.4% vs. 27.1%; p = 0.001).

As detailed in Table 1 , median age did not significantly differ across frailty categories (55, 57, and 56 years for fit, pre-frail, and frail patients; p = 0.285). Frail patients exhibited a higher comorbidity burden (HCT-CI >2: 53.0% vs. 25.2% and 33.2%; p < 0.001), poorer performance status (KPS <90%: 51.4% vs. 12.2% and 26.7%; p < 0.001), and were more frequently classified with high/very high DRI scores (27.8% vs. 18.4% and 23.8%; p = 0.045).

Multivariate logistic model indicated that frailty was significantly associated with higher comorbidity [HCT-CI >2: OR 2.30, p < 0.001] and impaired performance status [KPS <90%: OR 3.54, p < 0.001], but not with age, sex, or DRI. These associations were consistent across PMCC and GETH-TC subgroups ( Supplementary Table S1 ). Additionally, frail patients more frequently received reduced-intensity conditioning regimens (74.6% vs. 58.6% and 62.5%; p = 0.004), reflecting individualised clinical decisions based on comprehensive patient assessments.

As shown in Table 2 , fit patients had shorter median hospital stays (23 vs. 25 and 28 days for pre-frail and frail patients, respectively; p = 0.003) and fewer readmissions than pre-frail and frail ones (≥1 readmission: 31.7% vs. 38.9% and 44.2%; p = 0.016).

Incidences of post-transplant complications such as GVHD, bacterial bloodstream infections and CMV reactivation were similar across frailty categories. However, frail patients had a significantly higher incidence of invasive fungal infections than fit and pre-frail ones (1-year CIF: 10.9% vs. 4.0% and 4.3%, p = 0.004). Notice that, despite similar complication profiles, ICU admissions were more frequent among frail patients than among fit and pre-frail ones (Day +180 CIF: 7.0%, 10.8%, and 20.3% for fit, pre-frail, and frail patients, respectively; p = 0.002).

Disease relapse occurred in 251 (23.3%) patients at a median of 172 days (IQR 107–315). As reported in Fig. 2 and Table 2 , rates of relapse did not differ according to frailty state, and patients classified as fit, pre-frail, and frail showed comparable CIR (2-year CIR: 13.2%, 14.6%, and 14.9%, respectively; p = 0.303).

After a median follow-up of 2.2 years, 369 (34.4%) patients died, mostly from relapse, infection, or GVHD. As shown in Table 2 and Fig. 2 , the likelihood of OS was progressively lower when comparing fit to pre-frail and frail categories of patients. Specifically, 2-year OS was 77.2% for fit patients, 65.7% for pre-frail, and 52.8% for frail patients (p < 0.001). This gradient was largely driven by increasing NRM across frailty levels. Corresponding NRM rates of fit, pre-frail, and frail patients were 11.7%, 19.5%, and 32.2%, respectively (p = 0.001).

As shown in Fig. 2 the negative prognostic impact of frailty was also consistent across geographic regions. Frail patients experienced significantly worse outcomes in both PMCC cohort and GETH-TC centres, despite differences in patient populations ( Supplementary Section 3 ). Importantly, in both settings, frailty was associated lower OS probability and elevated NRM—further reinforcing the utility of the HCT-FS.

To further investigate the impact of frailty on outcomes, UVA and MVA were conducted for OS and NMR. As reported in Table 3 , UVA revealed that pre-frail and frail patients exhibited significantly increased risks of overall mortality [HR 1.53 (p = 0.018); HR 2.47 (p < 0.001)] and NRM [HR 1.69 (p = 0.003); HR 3.25 (p < 0.001)] compared to fit individuals.

In MVA, after adjusting for clinically relevant confounders, frailty remained an independent predictor of adverse outcomes ( Table 3 ). Pre-frail status was associated with inferior OS [HR 1.38, p = 0.012] and higher NRM [HR 1.54, p = 0.017], whereas frailty conferred markedly reduced OS [HR 2.15, p < 0.001] and increased NRM [HR 3.01, p < 0.001]. Additional variables negatively influencing survival included age >65 years, high/very high DRI, and use of 9/10 mismatched unrelated or haploidentical donors. HCT-CI >2 showed a non-significant trend toward reduced OS and increased NRM. Moreover, KPS <90% was not independently associated with outcomes when frailty was incorporated into the model.

One of the primary motivations for combining data from the Canadian and 15-GETH-TC studies was to obtain a sufficiently large sample size to evaluate the prognostic power of frailty—measured by the HCT-FS—across age groups (18–40, 41–59, and ≥60 years) and comorbidity levels (HCT-CI ≤2 vs. >2).

As shown in Fig. 3 , frailty status was significantly associated with both inferior OS and NRM regardless of age or comorbidity burden. While frailty was more common among older patients and among those with higher HCT-CI scores ( Table 1 ), its negative impact on transplant outcomes was consistent across all subgroups ( Fig. 4 ).

Discussion

This study collates data from the prospective and systematic assessment of frailty in allo-HCT using the HCT Frailty Scale (HCT-FS). The HCT-FS was incorporated into the routine evaluation of all adult allo-HCT candidates at participating institutions, first at PMCC and subsequently at 15 GETH-TC centres, without external funding or dedicated infrastructure. The main findings demonstrate that the HCT-FS is a scalable, time-efficient tool for stratifying patients by frailty status using resources already available within transplant programs.

This analysis contains real-world data from 1077 consecutive patients evaluated across 16 institutions and confirms that frailty can be assessed using the HCT-FS in a clinical setting with team coordination. In addition, it confirms that frailty is a robust and independent predictor of outcomes. Consistent with previous inter-inn and single-centre studies from PMCC (n = 298) and GETH-TC (n = 404), 23 , 24 both pre-frail and frail patients had significantly inferior OS and NRM compared to fit individuals. These associations remained significant even after adjusting for age, comorbidities (HCT-CI), and functional status (KPS), underscoring the additive prognostic value of frailty beyond traditional risk models.

Interestingly, although transplant-related event rates were similar across frailty groups, pre-frail—and particularly frail—patients had more ICU admissions and lower OS. These findings support the concept of frailty as a clinical syndrome characterised by reduced physiological reserve and diminished resilience to stressors. 8 , 11 , 34 Frail patients tolerated the demands of allo-HCT—including conditioning toxicity, immunosuppression, and complications such as infections or GVHD—less effectively, leading to impaired recovery and increased vulnerability. This reinforces frailty as both a prognostic marker and a physiological state that magnifies transplant-related risks.

At the first allo-HCT consultation, 12% of patients were frail, with consistent prevalence across geographic regions. This aligns with prior reports showing frailty incidence in allo-HCT candidates ranging from 12% to 33%. 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 Pre-frail patients constituted the most prevalent and clinically heterogeneous group. These results reflect current practice in which eligibility extends beyond strictly fit patients to include pre-frail and selected frail patients in those with disease-related or treatment-related deconditioning. While these patients meet general criteria for allo-HCT, frailty stratification uncovers significant heterogeneity in physiological fitness and identifies subgroups that may benefit from targeted interventions to improve transplant safety. 35 , 36 , 37

Notably, frailty was observed across all age ranges and in patients with and without comorbidities ( Fig. 3 ). TThe HCT-FS demonstrated consistent predictive value for OS across these subgroups, supporting frailty as a multidimensional construct that captures physiological reserve, functional capacity, and biological rather than chronological age. 38 , 39 Again, these findings advocate for systematic frailty assessment in all allo-HCT candidates, irrespective of age or comorbidity profile.

Importantly, incorporating frailty scales alongside established prognostic tools such as the HCT-CI and DRI can provide complementary insights. 40 , 41 Unlike traditional indices, frailty scales evaluate physiological domains—such as strength, physical performance, and systemic inflammation—not captured by standard comorbidity or disease-based metrics. 9 In contrast, the KPS, although widely used, offers a more subjective and narrower perspective focused primarily on functional capacity. 42 Future refined composite scales could integrate frailty domains with variables such as comorbidities, age, or HCT-related variables to enhance the prognostication of adverse events before allo-HCT and guide patient selection. 43

Our findings align with prior evidence linking frailty to adverse outcomes 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ; however, this study stands out for its real-world, multicentre design and the absence of external funding. It represents the first coordinated, multicentre implementation of a standardised frailty assessment tool specifically designed for allo-HCT candidates across diverse transplant centres. The successful adoption of the HCT-FS underscores its scalability and practical value in routine clinical settings.

Moreover, the HCT-FS stands out for its multidimensional structure, easy integration into clinical workflows, and minimal resource requirements. With a median administration time of about 10 min and the flexibility to be performed by either haematologists or trained nurses, it is both practical and sustainable in routine practice. Additionally, the availability of a web-based calculator further supports broad and consistent implementation.

Crucially, frailty is a dynamic and potentially reversible syndrome. 44 Therefore, its identification should probably not be limited to prognostication but also used to guide proactive strategies aimed at mitigating its impact before transplantation. In geriatric medicine, multidisciplinary interventions—particularly those incorporating physical activity—have demonstrated benefits in improving physiological reserve, muscle strength, insulin sensitivity, immune function, and reducing systemic inflammation. 45 , 46 , 47 , 48 Building on this evidence, recent studies have explored the feasibility of prehabilitation programs tailored to pre-frail and frail allo-HCT candidates, aiming to improve physical fitness and enhance transplant tolerance. 18 , 49 , 50

In line with this approach, the PMCC recently completed a prospective phase II study demonstrating the feasibility of a rehabilitation program for allo-HCT candidates, and further interventional studies are in development. 51 Similarly, the Hospital Clínic de Barcelona has launched a prehabilitation initiative incorporating structured exercise, showing early promise in reducing frailty prevalence prior to transplantation. 52 These initiatives highlight the potential of integrating frailty assessment tools—such as the HCT-FS—followed by targeted interventions to optimise transplant readiness and improve patient outcomes.

Despite its strengths, this collaborative study has limitations. The present study only includes transplanted patients and does not contain data from patients who were consulted for allo-HCT and finally not transplanted. In addition, frailty was assessed only at baseline, without follow-up evaluations to capture temporal changes.

The present analysis includes only transplanted patients and does not capture data from individuals who were evaluated for allo-HCT but ultimately did not proceed to transplant. In addition, although the study was observational and prospective, periodic frailty assessments may have enhanced supportive care for pre-frail and frail patients, as evaluating teams might have refined their management strategies over time through accumulated knowledge gained during the study period.

In conclusion, the HCT-FS is a practical, reproducible, and clinically informative tool for identifying fit, pre-frail, and frail allo-HCT candidates. Its integration in busy clinics is feasible using existing staff and infrastructure, and its prognostic value extends beyond conventional risk models. Systematic frailty assessment has the potential to improve clinical decision-making, personalise care, and guide the implementation of prehabilitation strategies. Given its demonstrated relevance and applicability, frailty assessment should become a standard component of pre-HCT workup across transplant centres.

Contributors

RK, MQS, and TAM conceived and designed the study, conducted the statistical analysis, and drafted the paper. MQS, RK and TAM accessed and verified the underlying data. All coinvestigators involved in the study supported the integration of the HCT Frailty Scale into their HCT programs, enrolled patients in the study, and diligently collected the data. IP, MBG, MAG, LF, MMPA, AS, MCQG, ASS, JMSC, VIA, ZAL, JCN, LP, SFL, LPVS, SV, PBM, AE, IIG, AM, EL, JLM, SF, PB, LB, AV, FM, JM, SA, MR, DK and AS contributed significantly to the study, offering valuable feedback, and critically reviewing and approving the manuscript. All authors had full access to the data and accept responsibility for the decision to submit for publication.

Data sharing statement

Anonymised data would be shared only after specific request and internal consideration.

Declaration of interests

Dr. Mussetti has received consulting fees and honoraria from Takeda, BMS, Gilead, Merck and Jazz Pharmaceuticals. Dr. Mattsson has received consulting fees, honoraria, travel support and advisory board compensation from ITB-MED LLC, Sanofi, Takeda, Takeda Canada Inc., Anocca AB, Medexus Pharmaceuticals Inc., Merck & Co., ProTgen Inc. and NEOGAP Therapeutics AB. All other authors report no conflicts of interest.

Acknowledgements

We thank our patients and the multidisciplinary teams involved on the implementation of the HCT-FS in clinical practice. This work has previously been presented as a Late-Breaking Abstract (abstr LB4005) at an Oral Session held June 15, 2025 at European Hematology Association Congress 2025. Artificial intelligence tools were used in the preparation of this work, solely for language editing and grammar correction.

Footnotes

Supplementary data related to this article can be found at https://doi.org/10.1016/j.eclinm.2025.103753 .

Contributor Information

María Queralt Salas, Email: mqsalas@clinic.cat.

Rajat Kumar, Email: Rajat.kumar@uhn.ca.

Supplementary data

Supplementary Sections

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