The Composite Health Risk Assessment Model (CHARM) Predicts Risks of Toxicities, Functional and Cognitive Decline Among Survivors of Allogeneic Hematopoietic Cell Transplantation (allo-HCT): A Prospective BMT-CTN Study 1704
Notice bibliographique
Résumé
Introduction: The BMT CTN 1704 study developed and validated the CHARM that stratified risk for non-relapse mortality and overall mortality among older adults, performing better than the HCT-CI alone (Artz A et al, Blood. 2023;142:109) and equally to two machine learning models. CHARM assigns a total score for 7 health variables: increasing age, higher HCT-CI scores, lower albumin, higher C-reactive protein, higher percent of weight loss over the preceding year, lower patient-reported performance status scores, and lower cognitive score per Montreal cognitive assessment (MoCA). A CHARM calculator is available at:https://cibmtr.org/CIBMTR/OffNav/DevSandbox/CHARM-Risk-NRM-Calculator No multi-institutional prospective data exist on functional trajectories and morbidity after HCT in older patients. We now report on the association of CHARM to trajectories of secondary morbidity outcomes among allo-HCT survivors of this large, prospective study. Patients and Methods: Allo-HCT candidates, aged ≥60 years (yrs), were enrolled (n=1226) from 49 centers in the US between 2019 - 2021. The primary analysis includes 1105 patients proceeding to allo-HCT on study and secondary endpoints were assessed at day (D) 100, 180 and 365 except MoCA and organ toxicity were restricted to D100 and frailty had inadequate data for D100. A sequential multiple imputation strategy was implemented to impute endpoints for survivors at each time point with missing data. Analyses on multiply imputed datasets were conducted and the results combined using Rubin's rule. Associations between CHARM scores and secondary outcomes were analyzed using a multivariable Cox, Fine-Gray, Generalized Estimating Equations, and logistic regression model for survival, competing risks, continuous, and binary outcomes, respectively, with latter two focused on surviving patients. Models were adjusted for other variables including conditioning intensity, graft-versus-host disease (GVHD) prophylaxis regimen, disease-risk index, donor-recipient gender match, ethnicity, baseline value of the dependent variable, and visit timepoints. Results: Higher CHARM scores were associated with development of serious organ toxicities by D100 (OR: 2.05, [1.52-2.78], p<0.0001) and ≥2 worsening score on the MoCA (odds ratio (OR) 1.55 [1.16-2.1], p=0.003). Among survivors at all timepoints, higher CHARM scores were associated with greater disability by instrumental activities of daily living (IADL) (Slope -0.640 [-0.433-0.846], p<0.001) and worsening Patient-Reported Outcomes Measurement Reporting System (PROMIS) physical function (Slope -0.981 [-0.057 - -1.904], p=0.037), depression (Slope 0.763 [0.042-1.484], p=0.038) and in a lesser magnitude anxiety (Slope 0.659, p=0.076). Among survivors at D180 and D365, higher CHARM scores were associated with worse frailty (Slope 0.193 [0.081-0.305], p<0.001). CHARM scores were not associated with development of acute GVHD grades 2-4 or 3-4 but were associated with post-GVHD increased mortality (HR: 1.61, [1.25-2.08], p=0.0002). Higher CHARM scores are associated with a lower incidence of chronic GVHD (HR: 0.83, p=0.026), likely due to the effect of CHARM on the competing risk of death leaving fewer patients at risk for chronic GVHD. Conclusions: The novel primary CHARM, originally developed to predict risks of NRM, also predicts worse frailty, disability, cognitive decline, and serious organ toxicities; outcomes that are critically important to older recipients of allo-HCT. CHARM therefore informs risks of transplant morbidity, separate from risks of developing acute GVHD. Results further support adopting CHARM in practice to counsel patients, expedite HCT referrals for lower risk CHARM, and design trials for high CHARM score patients.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».