Machine Learning Prediction of Surgical Intervention for Small Bowel Obstruction

ABSTRACT Small bowel obstruction (SBO) results in >350,000 operations and >$2 billion annual health care expenditures in the US. Prompt, effective identification of patients at high/low surgery risk could improve survival, lower complication rates, and shorten hospitalization lengths. SBO surgery prediction models were developed based on SBO-related encounters in the Duke University Health System between 2013 and 2017. A total of 3,910 encounters among 3,374 unique patients were identified. Performance was assessed in each hour after admission when predicting whether patients will (a) receive surgery within 24 hours, and (b) receive surgery during the current encounter. Potential benefits of model-based discharge were assessed using the incorrect discharge rate and average reduction in hospital stay. Model-based discharge of low-risk patients was projected to reduce the average length of stay among patients not receiving surgery by >60 hours while maintaining an incorrect discharge rate lower than the observed readmission rate (9.3%). AUROC for the 24-hour prediction task increased from 0.644 to 0.779 at 12 and 72 hours post-admission, respectively. Future work will prospectively explore the benefits of model deployment in an inpatient setting.