Einstein (Sao Paulo, Brazil) · 2026
Predicting 30-day unplanned readmissions in patients with cancer in a Brazilian hospital: a Machine Learning approach.
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Stabellini Nickolas, Favalesso Marilia Melo, Szlejf Claudia, Miguel Cesar Gomes, Pestana Roberto Carmagnani, Kim Hanna, Ferreira Gabriela de Carvalho, Waite Kristin A, Montero Alberto J, Barnholtz-Sloan Jill S, Amaro Junior Edson, Hamerschlak Nelson
Аннотация
OBJECTIVE: This study aims to develop Machine Learning models to predict 30-day unplanned readmissions in cancer patients treated at a private hospital in Brazil.
METHODS: This retrospective cohort study included admission records of hospitalizations lasting >24 hours, with a corresponding diagnosis of a solid malignancy. The primary outcome was 30-day unplanned readmission. We tested five supervised Machine Learning algorithms to develop predictive models: a "48-hour" model, which used data from the first 48 hours after admission, and a "discharge" model, which used data from the entire hospitalization. The optimal models were selected based on their median performance (area under the ROC curve [AUC], precision, recall, F1, and Fβ-Score). Model explainability was assessed using SHAP scores (SS).
RESULTS: We analyzed 3,728 admissions from 1,573 cancer patients, with an unplanned 30-day readmission rate of 21.13%. The Random Forest algorithm demonstrated the best-balanced performance for the 48-hour model (AUC = 0.70, IQR= 0.68-0.71), whereas LightGBM performed best for the discharge model (AUC = 0.70, IQR= 0.68-0.72). In both models, the top predictor was prior emergency department visits in the past six months (SS=0.074 and 0.053, respectively). The top 10 predictors included: laboratory values (including lymphocyte count, Gamma-glutamyl transferase, Red Cell Distribution Width, hemoglobin, sodium, monocyte count, hematocrit, and urea), vital signs, and emergency index admission in both models, with colon cancer ranking among the top predictors in the 48-hour model.
CONCLUSION: The models underscored the significance of prior emergency department visits, laboratory values, and vital signs as key predictors of 30-day unplanned readmission. The timing of laboratory and vitals influenced their predictive strength. Implementing these models could help providers develop personalized care strategies to reduce readmission risk. External validation is needed.
METHODS: This retrospective cohort study included admission records of hospitalizations lasting >24 hours, with a corresponding diagnosis of a solid malignancy. The primary outcome was 30-day unplanned readmission. We tested five supervised Machine Learning algorithms to develop predictive models: a "48-hour" model, which used data from the first 48 hours after admission, and a "discharge" model, which used data from the entire hospitalization. The optimal models were selected based on their median performance (area under the ROC curve [AUC], precision, recall, F1, and Fβ-Score). Model explainability was assessed using SHAP scores (SS).
RESULTS: We analyzed 3,728 admissions from 1,573 cancer patients, with an unplanned 30-day readmission rate of 21.13%. The Random Forest algorithm demonstrated the best-balanced performance for the 48-hour model (AUC = 0.70, IQR= 0.68-0.71), whereas LightGBM performed best for the discharge model (AUC = 0.70, IQR= 0.68-0.72). In both models, the top predictor was prior emergency department visits in the past six months (SS=0.074 and 0.053, respectively). The top 10 predictors included: laboratory values (including lymphocyte count, Gamma-glutamyl transferase, Red Cell Distribution Width, hemoglobin, sodium, monocyte count, hematocrit, and urea), vital signs, and emergency index admission in both models, with colon cancer ranking among the top predictors in the 48-hour model.
CONCLUSION: The models underscored the significance of prior emergency department visits, laboratory values, and vital signs as key predictors of 30-day unplanned readmission. The timing of laboratory and vitals influenced their predictive strength. Implementing these models could help providers develop personalized care strategies to reduce readmission risk. External validation is needed.