Optimizing Chemotherapy Outcomes for Elderly Thai Cancer Patients: Development of a Predictive Tool for Severe Adverse Events
Abstract
Objective: To develop a predictive model for grade 3–5 CAEs in older Thai cancer patients using clinical and geriatric parameters.
Material and Methods: This retro-prospective cohort study enrolled patients aged ≥60 years with solid tumors who were undergoing chemotherapy at Chulabhorn Hospital between January 2023 and June 2024. Clinical data and geriatric assessments were collected at baseline and during each treatment cycle. Predictors were identified using multilevel logistic regression, and model performance was evaluated using AUROC with internal validation.
Results: Ninety-four patients contributed 634 chemotherapy cycles, with 124 grade 3–5 CAEs (19.6%) recorded. Significant predictors included low income, ECOG performance status, white blood cell count, and absolute neutrophil count, while polypharmacy and prolonged Timed Up and Go Test (>20 seconds) showed protective trends, likely reflecting more cautious physician management. The final model achieved an AUROC of 0.72 (95% CI: 0.67–0.77), with a sensitivity of 81.2%, a specificity of 41.7%, a positive predictive value of 25.5%, and a negative predictive value of 90.0%.
Conclusion: A Thai-specific clinical tool incorporating geriatric and clinical parameters can help identify older patients at risk of severe chemotherapy toxicity. Its high sensitivity and negative predictive value support its use as a screening tool in routine oncology care.
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