Clinical-Bladder cancer Machine learning decision support model for radical cystectomy discharge planning

Purpose: Timely and appropriate discharge placement for patients who have undergone radical cystectomy (RC) remains challenging. Our objective was to improve the discharge planning process by creating a machine learning model that helps to predict the need for non -home hospital discharge to a higher level of care. Materials and Methods: Patients undergoing elective radical cystectomy for bladder cancer from 2014-2019 were identified in the ACS-NSQIP database. A gradient boosted decision tree was trained on selected predischarge variables to predict discharge location, dichotomized into home and non-home. We used threshold-moving to calibrate model predictions and evaluated model performance on a testing set using receiver operating characteristic and precision recall curves. Model performance was further examined in subgroups of interest. Results and Conclusions: A total of 11,881 patients met inclusion criteria with a mean age of 68.6 years. 10.6% of patients undergoing RC had non-home discharges. Our model predicting non-home discharge achieved an area under the receiver operating characteristic curve of 0.80 and an average precision of 0.33. After threshold-moving, our model had a recall of 0.757 and a precision of 0.211. Top variables by importance were septic shock occurrence, ventilator-use greater than 48 hours, organ space surgical site infection and unplanned intubation. Our model shows strong performance in identifying patients who required non-home discharge to higher levels of care, outperforming commonly used clinical indices and prior work. Modern machine learning techniques may be applied to support more timely and appropriate clinical decision making. (C) 2022 Elsevier Inc. All rights reserved.

Automated summary

The purpose of this study was to improve the discharge planning process by creating a machine learning model that predicts the need for non-home hospital discharge. The model showed strong performance in identifying patients who required higher levels of care, outperforming commonly used clinical indices and prior work.