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Record W3212444048 · doi:10.1182/blood-2021-153710

Frailty Scale for Outcome Predictions in Hematopoietic Cell Transplanted Adults

2021· article· en· W3212444048 on OpenAlexaff
María Queralt Salas, Eshetu G. Atenafu, Eshrak Al‐Shaibani, Ora Bascom, Leeann Wilson, Carol Chen, Ivan Pašić, Arjun Law, Wilson Lam, Dennis Dong Hwan Kim, Armin Gerbitz, Auro Viswabandya, Jeffrey H. Lipton, Fotios V. Michelis, Jonas Mattsson, Shabbir M.H. Alibhai, Rajat Kumar

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of TorontoUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineCohortHematopoietic cellTransplantationActivities of daily livingHematopoietic stem cell transplantationGerontologyCohort studyProspective cohort studyPhysical therapyInternal medicineStem cellHaematopoiesis

Abstract

fetched live from OpenAlex

Abstract INTRODUCTION Frailty in patients undergoing hematopoietic cell transplantation (HCT) has a negative impact on survival. The inclusion of frailty evaluations before HCT is highly recommended; however, there is no consensus about the best methodology to evaluate this syndrome. In 2018 our center started a Frailty and Functionality program that involves regularly collecting information on the following eight indices in patients referred for allogeneic HCT (Salas et al., 2021): Clinical Frailty Scale (CFS), Instrumental Activities of Daily Living (IADL) test, Timed up and Go Test (TUGT), Grip Strength (GS), Self-Health Rated questionnaire (SHR-Q), Fall test, albumin (Alb), and C - reactive protein (CRP). With the recorded measurements of the eight indices and the corresponding validating process, we propose a HCT Frailty Scale. This scale is specifically designed to identify fit, pre-frail, and frail candidates for alloHCT, from better to worst probability of post-transplant survival. METHODS Between June 2018 and December 2020, 338 adults underwent alloHCT at our Institution. Frailty syndrome was evaluated prospectively in all patients at first consultation, using existing resources, after informed consent. It included measurement of the eight indices. The median time to evaluate was 5-6 minutes. Each index was given a value of 0 if normal or 1 (abnormal). Complete data were available for 298 patients that were finally included in the analysis. With this data the HCT Frailty Scale was elaborated as follows. The study cohort was split in two groups, a training cohort with 2/3 (N=200) of the patients, and a validation cohort of 1/3 (N=98), proportional to death outcomes. With the data from the training group we estimated a multivariable Cox model with overall survival (OS) as dependent variable, and the eight referenced indices as explanatory variables. Any normal result was scored 0, and based on the estimated HR coefficient from the Cox model, a proportional weight score was given to each respective index variable in the calculation of the composite HCT Frailty Scale score. The HCT Frailty index was calculated using the following formula: 1.5 *CFS, +1*IADL, +1*GS, + 1.5*TUGT, + 1*SHR-Q, +1*Falls-Test, + 1.5 *Alb, + 2*CRP. As a result, the HCT Frailty Scale goes from 0 to 10.5. The values of the scale were grouped to determine the following three groups of patients: (a) Fit patent: scale score ≤1; (b) Pre-Frail patient: 1< scale score < 5.5; (c) Frail patient: scale score 5.5 (Figure 1). RESULTS Baseline characteristics of the training and validation cohort are shown in Figure 1. Of the 200 patients included in the training cohort, the median age was 58 years (range 19-76 years); 29 (15.85%) had a KPS between 70-80%; and 56 (30.11%) a HCT-CI >3. The elaborated HCT Frailty Scale classified the 200 patients as: (a) 70 (35%) fit patients with an estimated 1-y OS of 83.7%; (b) 97 (48.5%) pre-frail patients with a predicted 1-y OS of 75.6%; and (c) 33 (16.5%) frail patients with an estimated 1-y OS of 52.8%. Of the 33 frail patients, 54.8% had a KPS between 90-100% and 48.5% had an HCT-CI <3 and of the 70 fit patients, 4.8% had a KPS between 70-80% and 24.2% had an HCT-CI ≥ 3. These differences support the hypothesis that frailty does not necessarily correlate with performance and comorbidities. The predictive ability of the HCT Frailty Scale was validated in 98 patients included in the validation cohort. This scale identified (a) 33 (33.7%) fit patients with an expected 1-y OS of 90.3%, (b) 51 (52%) pre-frail patients with an expected 1-y OS of 69.5%, and (c) 14 (14%) frail patients with an estimated 1y OS of 46.2% (Figure 1). CONCLUSION The HCT Frailty Evaluation Scale has been specifically designed to be applied in routine clinical practice and to patients across all ages. The scale ranges from 0 to 10.5 and the score value is calculated as the weighted sum of values of eight indexes evaluated at first consultation. The proposed scale should be of utility to identify frail and pre-frail patients that may benefit from appropriate counselling pre-transplant and individualized interventions to reverse frailty syndrome prior to alloHCT. Figure 1 Figure 1. Disclosures Law: Novartis: Consultancy; Actinium Pharmaceuticals: Research Funding. Kim: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Paladin: Honoraria, Research Funding; Bristol-Meier Squibb: Research Funding; Pfizer: Honoraria, Research Funding. Lipton: Bristol Myers Squibb, Ariad, Pfizer, Novartis: Consultancy, Research Funding. Mattsson: MattssonAB medical: Current Employment, Current holder of individual stocks in a privately-held company.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.275
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2021
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