医学
统计的
呼吸衰竭
人口
计算器
急诊科
感染性休克
急诊医学
外科
败血症
统计
数学
环境卫生
精神科
计算机科学
操作系统
作者
Lydia R. Maurer,Prahan Chetlur,Ying Daisy Zhuo,Majed El Hechi,George C. Velmahos,Jack Dunn,Dimitris Bertsimas,Haytham M.A. Kaafarani
出处
期刊:Annals of Surgery
[Ovid Technologies (Wolters Kluwer)]
日期:2020-12-23
卷期号:277 (1): e8-e15
被引量:20
标识
DOI:10.1097/sla.0000000000004714
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI