Comparing Clinician Estimates versus a Statistical Tool for Predicting Risk of Death within 45 Days of Admission for Cancer Patients

While clinical practice guidelines recommend that oncologists discuss goals of care with patients who have advanced cancer, it is estimated that less than 20% of individuals admitted to the hospital with high-risk cancers have end-of-life discussions with their providers. While there has been interest in developing models for mortality prediction to trigger such discussions, few studies have compared how such models compare with clinical judgment to determine a patient's mortality risk. This study is a prospective analysis of 1,069 solid tumor medical oncology hospital admissions ( = 911 unique patients) from February 7 to June 7, 2022, at Memorial Sloan Kettering Cancer Center. Electronic surveys were sent to hospitalists, advanced practice providers, and medical oncologists the first afternoon following a hospital admission and they were asked to estimate the probability that the patient would die within 45 days. Provider estimates of mortality were compared with those from a predictive model developed using a supervised machine learning methodology, and incorporated routine laboratory, demographic, biometric, and admission data. Area under the receiver operating characteristic curve (AUC), calibration and decision curves were compared between clinician estimates and the model predictions. Within 45 days following hospital admission, 229 (25%) of 911 patients died. The model performed better than the clinician estimates (AUC 0.834 vs. 0.753,