Validation of the Artificial Intelligence-Based Predictive Optimal Trees in Emergency Surgery Risk (POTTER) Calculator in Emergency General Surgery and Emergency Laparotomy Patients.
Adult
Aged
Artificial Intelligence
Benchmarking
/ methods
Databases, Factual
/ statistics & numerical data
Decision Trees
Emergency Service, Hospital
/ statistics & numerical data
Emergency Treatment
/ adverse effects
Feasibility Studies
Female
Hospital Mortality
Humans
Laparotomy
/ adverse effects
Male
Middle Aged
Postoperative Complications
/ epidemiology
Risk Assessment
/ methods
Risk Factors
Journal
Journal of the American College of Surgeons
ISSN: 1879-1190
Titre abrégé: J Am Coll Surg
Pays: United States
ID NLM: 9431305
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
received:
09
12
2020
revised:
19
01
2021
accepted:
10
02
2021
pubmed:
12
3
2021
medline:
9
10
2021
entrez:
11
3
2021
Statut:
ppublish
Résumé
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. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 33705983
pii: S1072-7515(21)00160-5
doi: 10.1016/j.jamcollsurg.2021.02.009
pii:
doi:
Types de publication
Journal Article
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
912-919.e1Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2021 American College of Surgeons. Published by Elsevier Inc. All rights reserved.