Validation of the Artificial Intelligence-Based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) Calculator in Emergency General Surgery and Emergency Laparotomy Patients.

Abstract

BACKGROUND:

The Predictive Optimal Trees in Emergency Surgery Risk (POTTER) tool is an artificial intelligence-based calculator for the prediction of 30-day outcomes in patients undergoing emergency operations. In this study, we sought to assess the performance of POTTER in the emergency general surgery (EGS) population in particular.

METHODS:

All patients who underwent EGS in the 2017 American College of Surgeons NSQIP database were included. The performance of POTTER in predicting 30-day postoperative mortality, morbidity, and 18 specific complications was assessed using the c-statistic metric. As a subgroup analysis, the performance of POTTER in predicting the outcomes of patients undergoing emergency laparotomy was assessed.

RESULTS:

A total of 59,955 patients were included. Median age was 50 years and 51.3% were women. POTTER predicted mortality (c-statistic = 0.93) and morbidity (c-statistic = 0.83) extremely well. Among individual complications, POTTER had the highest performance in predicting septic shock (c-statistic = 0.93), respiratory failure requiring mechanical ventilation for 48 hours or longer (c-statistic = 0.92), and acute renal failure (c-statistic = 0.92). Among patients undergoing emergency laparotomy, the c-statistic performances of POTTER in predicting mortality and morbidity were 0.86 and 0.77, respectively.

CONCLUSIONS:

POTTER is an interpretable, accurate, and user-friendly predictor of 30-day outcomes in patients undergoing EGS. POTTER could prove useful for bedside counseling of patients and their families and for benchmarking of EGS care.

• Authors: Majed W El Hechi, Lydia R Maurer, Jordan Levine, Daisy Zhuo, Mohamad El Moheb, George C Velmahos, Jack Dunn, Dimitris Bertsimas, Haytham Ma Kaafarani
• Journal: Journal of the American College of Surgeons (J Am Coll Surg) Vol. 232 Issue 6 Pg. 912-919.e1 (06 2021) ISSN: 1879-1190 [Electronic] United States
• PMID: 33705983 (Publication Type: Journal Article, Validation Study)
• Copyright: Copyright © 2021 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Topics

• Adult
• Aged
• Artificial Intelligence
• Benchmarking (methods, statistics & numerical data)
• Databases, Factual (statistics & numerical data)
• Decision Trees
• Emergency Service, Hospital (statistics & numerical data)
• Emergency Treatment (adverse effects, statistics & numerical data)
• Feasibility Studies
• Female
• Hospital Mortality
• Humans
• Laparotomy (adverse effects, statistics & numerical data)
• Male
• Middle Aged
• Postoperative Complications (epidemiology, etiology)
• Risk Assessment (methods, statistics & numerical data)
• Risk Factors