An AI Model to Predict Acute Hospitalization after Radiation Therapy

Abstract

<h3>Purpose/Objective(s)</h3> Acute hospitalization during or after cancer treatment negatively impacts quality of care and causes significant patient morbidity. In patients receiving radiation therapy (RT) for any malignancy, we hypothesized that a machine learning approach would enable prediction of hospitalizations during or shortly after RT. <h3>Materials/Methods</h3> We analyzed 35,810 courses of RT provided to treat a cancer diagnosis at a large multisite academic department in a major metropolitan environment (all cancer patients treated with RT between 8/1999—1/2022 regardless of disease site or treatment intent). Approximately 150 clinical/treatment variables were extracted and processed into analytic format by a proprietary oncology analytics platform connected to the Electronic Medical Record System (inpatient and outpatient), Oncology Information System, and PACS. Such variables included past medical history, recent laboratory data, prior systemic cancer therapies, prior RT, recent hospitalization, and RT details. A patient was labeled as having suffered an acute hospitalization if they had an encounter classified as inpatient or emergency during RT or in the 30 days following completion of RT. A machine learning model was trained on a subset (75%) of the data reserved for training (26857 cases). Five-fold cross-validation was used to select model type and hyperparameters, using area under the ROC curve (AUC) to measure performance. The final model was evaluated for accuracy, AUC, precision, recall, and F1 score on an independent test set (25%) excluded from the training process (8953 cases). Model calibration was assessed by visual inspection of calibration plots. An AUC>0.70 was considered clinically valid. <h3>Results</h3> Among the 35,810 courses of RT, the incidence of acute hospitalization was 9.1% (9.2% training set; 9.0% test set). Model performance on the test set is shown in Table 1. Variables deemed to be significant predictors for hospitalization included recent lab values (PT INR, sodium, potassium) recent hospitalization (within 30 days prior to RT start), and patient age <h3>Conclusion</h3> In cancer patients undergoing RT, a machine learning model identified patients at risk of 30-day hospitalization. Predictive analytics may be a key tool to help providers identify high-risk patients and optimize interventions, while improving quality and value of care.