Comprehensive validation of machine learning models predicting chemotherapy related electrolyte disorders in a multicenter study

Electrolyte abnormalities following chemotherapy are common and clinically significant complications in cancer patients and are often associated with treatment delays and adverse outcomes. This study aims to develop and validate machine learning models to predict eight electrolyte abnormalities in cancer patients receiving chemotherapy. We retrospectively analyze medical records of cancer patients from two tertiary hospitals in Korea (n = 11,227). Four machine learning algorithms are used to predict eight electrolyte abnormalities occurring within 4 weeks of chemotherapy initiation. Model performance is evaluated using a comprehensive validation framework, including internal, external, and temporal validation, and model interpretability is assessed using Shapley additive explanations. Here we show that electrolyte abnormalities occur in 4451 patients (74.0%) in the internal cohort and 4414 patients (84.7%) in the external cohort, with in-hospital mortality rates of 35.9% and 32.8%, respectively. The best-performing models achieve an average area under the receiver operating characteristic curve of 0.798, with an average performance decline of 0.11 during external validation. Model interpretation identifies serum albumin, heart rate, and estimated glomerular filtration rate as the most important predictors across models. Risk stratification demonstrates that patients in the highest-risk quintile have 4.15-fold greater odds of developing electrolyte abnormalities, and a 2.02-fold higher risk of mortality compared with the moderate-risk. These machine learning models provide an effective approach for predicting electrolyte abnormalities in cancer patients receiving chemotherapy and may support risk stratification and monitoring prioritization. Future studies are needed to validate these models in more diverse populations, incorporate additional biomarkers, and explore their integration into clinical decision-support systems. Cho, Jeong et al. develop and validate machine learning models to predict chemotherapy-related electrolyte abnormalities using multicenter real-world clinical data. The models accurately stratify patients by risk, show stable performance across validation settings, and identify individuals at higher risk of electrolyte disturbances and mortality. Cancer patients who receive chemotherapy often develop problems with body salts, such as sodium or potassium imbalances. These problems can delay treatment and increase the risk of serious complications. However, it is difficult to know in advance which patients are most likely to be affected. In this study, we analyzed medical records from over 11,000 cancer patients treated at two large hospitals in Korea. We used computer-based prediction models to estimate the risk of developing electrolyte problems within 4 weeks after starting chemotherapy. The models were carefully tested using data from different time periods and hospitals. We found that the models could identify patients at higher risk of electrolyte problems and death. These findings may help doctors monitor vulnerable patients more closely and prevent complications during cancer treatment.