Validation of the Al-based Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) Calculator in Patients 65 Years and Older.

Abstract

OBJECTIVE:

We sought to assess the performance of the Predictive OpTimal Trees in Emergency Surgery Risk (POTTER) tool in elderly emergency surgery (ES) patients.

SUMMARY BACKGROUND DATA:

The POTTER tool was derived using a novel Artificial Intelligence (AI)-methodology called optimal classification trees and validated for prediction of ES outcomes. POTTER outperforms all existent risk-prediction models and is available as an interactive smartphone application. Predicting outcomes in elderly patients has been historically challenging and POTTER has not yet been tested in this population.

METHODS:

All patients ≥65 years who underwent ES in the ACS-NSQIP 2017 database were included. POTTER's performance for 30-day mortality and 18 postoperative complications (eg, respiratory or renal failure) was assessed using c-statistic methodology, with planned sub-analyses for patients 65 to 74, 75 to 84, and 85+ years.

RESULTS:

A total of 29,366 patients were included, with mean age 77, 55.8% females, and 62% who underwent emergency general surgery. POTTER predicted mortality accurately in all patients over 65 (c-statistic 0.80). Its best performance was in patients 65 to 74 years (c-statistic 0.84), and its worst in patients ≥85 years (c-statistic 0.71). POTTER had the best discrimination for predicting septic shock (c-statistic 0.90), respiratory failure requiring mechanical ventilation for ≥48 hours (c-statistic 0.86), and acute renal failure (c-statistic 0.85).

CONCLUSIONS:

POTTER is a novel, interpretable, and highly accurate predictor of in-hospital mortality in elderly ES patients up to age 85 years. POTTER could prove useful for bedside counseling and for benchmarking of ES care.

• Authors: Lydia R Maurer, Prahan Chetlur, Daisy Zhuo, Majed El Hechi, George C Velmahos, Jack Dunn, Dimitris Bertsimas, Haytham M A Kaafarani
• Journal: Annals of surgery (Ann Surg) Vol. 277 Issue 1 Pg. e8-e15 (Jan 01 2023) ISSN: 1528-1140 [Electronic] United States
• PMID: 33378309 (Publication Type: Journal Article)
• Copyright: Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Topics

• Female
• Humans
• Aged
• Aged, 80 and over
• Male
• Artificial Intelligence
• Risk Assessment (methods)
• Postoperative Complications (epidemiology)
• Hospital Mortality
• Databases, Factual
• Risk Factors