CHEcking Diagnostic Differential Ability of Real Baseline Variables and Frailty Scores in Tolerance of Anti-Cancer Systemic Therapy in OldEr Patients (CHEDDAR-TOASTIE)

TY - JOUR

T1 - CHEcking Diagnostic Differential Ability of Real Baseline Variables and Frailty Scores in Tolerance of Anti-Cancer Systemic Therapy in OldEr Patients (CHEDDAR-TOASTIE)

AU - Ng, Helen H. L.

AU - Mahmood, Isa

AU - Aggrey, Francis

AU - Dearden, Helen

AU - Baxter, Mark

AU - Zucker, Kieran

N1 - Publisher Copyright: © 2025 by the authors.

PY - 2025/10

Y1 - 2025/10

N2 - BACKGROUND: Despite chemotherapy-related toxicities being more likely in older patients, no routine prediction tool has been validated for the UK population. Previous research within the TOASTIE (tolerance of anti-cancer systemic therapy in the elderly) study found a low predictive performance of the Cancer and Aging Research Group (CARG) score for severe chemotherapy-related toxicities. Building on this, the TOASTIE study dataset was used to assess the viability of developing a predictive model with baseline variables and frailty scores for severe chemotherapy-related toxicities in older patients.METHODS: All patients from the TOASTIE dataset were included, with the inclusion/exclusion criteria detailed in the TOASTIE protocol. Demographic factors, self-assessment scores, Rockwood Clinical Frailty Score and researcher's estimated risks of toxicity were assessed for their association with severe chemotherapy-related toxicities. After data partition into 70:15:15 train/validation/test, models were built on the training dataset using logistic regression (LR), LASSO and random forest (RF). Models were optimized with a validation set with LR and LASSO; cross-validation was used with RF. Model performance was assessed with balanced accuracy, NPV and AUC.RESULTS: Of the 322 patients included, the incidence of severe toxicities was 22% (n = 71). Ten variables were statistically significant, albeit weakly associated with severe toxicities: primarily patient-reported factors, Performance Status and high baseline neutrophil count. LR models gave the best balanced accuracies of 0.6382 (AUC 0.6950, NPV 0.8696) and 0.6469 (AUC 0.6469, NPV 0.4286) with LASSO, and 0.6294 (AUC 0.6557, NPV 0.6557) with RF.CONCLUSIONS: Models lack sufficiently robust results for clinical utility. However, a high NPV in predicting no toxicity could help identify lower-risk patients who may not require dose reductions, potentially improving overall outcomes.

AB - BACKGROUND: Despite chemotherapy-related toxicities being more likely in older patients, no routine prediction tool has been validated for the UK population. Previous research within the TOASTIE (tolerance of anti-cancer systemic therapy in the elderly) study found a low predictive performance of the Cancer and Aging Research Group (CARG) score for severe chemotherapy-related toxicities. Building on this, the TOASTIE study dataset was used to assess the viability of developing a predictive model with baseline variables and frailty scores for severe chemotherapy-related toxicities in older patients.METHODS: All patients from the TOASTIE dataset were included, with the inclusion/exclusion criteria detailed in the TOASTIE protocol. Demographic factors, self-assessment scores, Rockwood Clinical Frailty Score and researcher's estimated risks of toxicity were assessed for their association with severe chemotherapy-related toxicities. After data partition into 70:15:15 train/validation/test, models were built on the training dataset using logistic regression (LR), LASSO and random forest (RF). Models were optimized with a validation set with LR and LASSO; cross-validation was used with RF. Model performance was assessed with balanced accuracy, NPV and AUC.RESULTS: Of the 322 patients included, the incidence of severe toxicities was 22% (n = 71). Ten variables were statistically significant, albeit weakly associated with severe toxicities: primarily patient-reported factors, Performance Status and high baseline neutrophil count. LR models gave the best balanced accuracies of 0.6382 (AUC 0.6950, NPV 0.8696) and 0.6469 (AUC 0.6469, NPV 0.4286) with LASSO, and 0.6294 (AUC 0.6557, NPV 0.6557) with RF.CONCLUSIONS: Models lack sufficiently robust results for clinical utility. However, a high NPV in predicting no toxicity could help identify lower-risk patients who may not require dose reductions, potentially improving overall outcomes.

KW - chemotherapy

KW - chemotherapy-related toxicities

KW - older population

KW - prediction models

UR - https://www.scopus.com/pages/publications/105020939124

U2 - 10.3390/cancers17203303

DO - 10.3390/cancers17203303

M3 - Article

C2 - 41154361

SN - 2072-6694

VL - 17

JO - Cancers

JF - Cancers

IS - 20

M1 - 3303

ER -