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

## TL;DR

This study found that current models for predicting chemotherapy side effects in older patients are not accurate enough for clinical use, despite some promising results in identifying low-risk patients.

## Contribution

The study evaluates the predictive performance of baseline variables and frailty scores for chemotherapy toxicity in older UK patients, revealing limitations in current models.

## Key findings

- 22% of 322 patients experienced severe toxicities from chemotherapy.
- Logistic regression models achieved the highest balanced accuracy of 0.6382 for predicting toxicity.
- Models showed low–moderate accuracy, insufficient for clinical implementation.

## Abstract

Older adults are more prone to severe side effects (toxicities) from chemotherapy. The initial observational study found that scoring systems used to predict toxicities in a 65+ UK population receiving first-line chemotherapy performed poorly. This subsequent study aims to explore whether additional frailty and baseline health data can improve the performance of toxicity prediction models. Data from the observational study were used: factors such as age, sex, weight, a patient’s own assessment of health (AHQ), and number of comorbidities were analyzed for their predictive performance. Then, predictive models were built using various statistical and machine learning methods. Among 322 patients, 22% had toxicities. Ten factors were weakly linked to toxicities, including AHQ and a high baseline neutrophil count. The best performance predictive models had only low–moderate accuracies, insufficient for clinical use in predicting toxicities. Further research is needed to develop a more robust predictive scoring system.

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.