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Delays in admitting high-risk emergency surgery patients to the intensive care unit result in worse outcomes and increased health care costs. We aimed to use interpretable artificial intelligence technology to create a preoperative predictor for postoperative intensive care unit need in emergency surgery patients.A novel, interpretable artificial intelligence technology called optimal classification trees was leveraged in an 80:20 train:test split of adult emergency surgery patients in the 2007-2017 American College of Surgeons National Surgical Quality Improvement Program database. Demographics, comorbidities, and laboratory values were used to develop, train, and then validate optimal classification tree algorithms to predict the need for postoperative intensive care unit admission. The latter was defined as postoperative death or the development of 1 or more postoperative complications warranting critical care (eg, unplanned intubation, ventilator requirement ≥48 hours, cardiac arrest requiring cardiopulmonary resuscitation, and septic shock). An interactive and user-friendly application was created. C statistics were used to measure performance.A total of 464,861 patients were included. The mean age was 55 years, 48% were male, and 11% developed severe postoperative complications warranting critical care. The Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application was created as the user-friendly interface of the complex optimal classification tree algorithms. The number of questions (ie, tree depths) needed to predict intensive care unit admission ranged from 2 to 11. The Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application had excellent discrimination for predicting the need for intensive care unit admission (C statistics: 0.89 train, 0.88 test).We recommend the Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application as an accurate, artificial intelligence-based tool for predicting severe complications warranting intensive care unit admission after emergency surgery. The Predictive OpTimal Trees in Emergency Surgery Risk Intensive Care Unit application can prove useful to triage patients to the intensive care unit and to potentially decrease failure to rescue in emergency surgery patients.

The Trauma Outcomes Predictor tool was recently derived using a machine learning methodology called optimal classification trees and validated for prediction of outcomes in trauma patients. The Trauma Outcomes Predictor is available as an interactive smartphone application. In this study, we sought to assess the performance of the Trauma Outcomes Predictor in the elderly trauma patient.All patients aged 65 years and older in the American College of Surgeons-Trauma Quality Improvement Program 2017 database were included. The performance of the Trauma Outcomes Predictor in predicting in-hospital mortality and combined and specific morbidity based on incidence of 9 specific in-hospital complications was assessed using the c-statistic methodology, with planned subanalyses for patients 65 to 74, 75 to 84, and 85+ years.A total of 260,505 patients were included. Median age was 77 (71-84) years, 57% were women, and 98.8% had a blunt mechanism of injury. The Trauma Outcomes Predictor accurately predicted mortality in all patients, with excellent performance for penetrating trauma (c-statistic: 0.92) and good performance for blunt trauma (c-statistic: 0.83). Its best performance was in patients 65 to 74 years (c-statistic: blunt 0.86, penetrating 0.93). Among blunt trauma patients, the Trauma Outcomes Predictor had the best discrimination for predicting acute respiratory distress syndrome (c-statistic 0.75) and cardiac arrest requiring cardiopulmonary resuscitation (c-statistic 0.75). Among penetrating trauma patients, the Trauma Outcomes Predictor had the best discrimination for deep and organ space surgical site infections (c-statistics 0.95 and 0.84, respectively).The Trauma Outcomes Predictor is a novel, interpretable, and highly accurate predictor of in-hospital mortality in the elderly trauma patient up to age 85 years. The Trauma Outcomes Predictor could prove useful for bedside counseling of elderly patients and their families and for benchmarking the quality of geriatric trauma care.

Background A minimally invasive step-up approach to necrotizing biliary pancreatitis often requires multiple interventions, delaying cholecystectomy. The risk of gallstone-related complications during this time interval is unknown, as is the feasibility and safety of cholecystectomy after minimally invasive step-up treatment. In this paper, we analyzed both. Methods Necrotizing pancreatitis patients treated with a minimally invasive step-up approach who underwent interval cholecystectomy at 2 tertiary care centers between 2014 and 2019 were included. Gallstone-related complications prior to cholecystectomy were examined, as were surgical approaches to cholecystectomy and complications. Necrotizing pancreatitis patients treated without mechanical intervention were also examined. Results Seven of 31 patients developed gallstone-related complications between minimally invasive step-up treatment initiation and cholecystectomy. One patient developed biliary colic. Six patients developed acute cholecystitis. Two of these patients also developed choledocholithiasis, and 1 developed cholangitis, all requiring endoscopic retrograde cholangiopancreatography. Cholecystectomy was performed laparoscopically in 27 of 31 patients. One patient required open conversion, and 3 patients underwent planned cholecystectomy during another open operation. Four patients developed postoperative complications. Two of 14 necrotizing pancreatitis patients treated without mechanical intervention developed recurrent pancreatitis while awaiting cholecystectomy. Conclusion Over 20% of necrotizing pancreatitis patients treated by a minimally invasive step-up approach developed gallstone-related complications while awaiting cholecystectomy. Laparoscopic cholecystectomy is feasible and safe in the great majority of necrotizing pancreatitis patients treated by a minimally invasive step-up approach.

Setting patient and family expectations for postoperative outcomes is an important aspect of care, a cornerstone of which is accurate, personalized, and explainable risk estimation. Modern machine learning offers a plethora of models that can effectively capture the complex, nonlinear contributions of preoperative risk factors to the surgical patient's overall risk. However, most of these models produce risk estimates that are not interpretable, which compromises trust in these systems, renders them unaccountable, and limits their widespread adoption. Recent developments in machine learning have been successful at creating risk calculators that address this gap, producing explainable risk estimates without compromising accuracy. In this work, we describe how the state of the art in postoperative risk estimation addresses the shortcomings of older methods, and how they have been applied in a clinical setting. We conclude with a discussion of the potential of machine learning models to be systematically integrated in health care more broadly and future prospects beyond passive risk prediction.