Development and Validation of the First FDA Authorized Artificial Intelligence/Machine Learning Diagnostic Tool for the Prediction of Sepsis Risk

Author:

Bhargava Akhil,López-Espina Carlos,Schmalz Lee,Khan Shah,Watson Gregory L.,Urdiales Dennys,Updike Lincoln,Kurtzman Niko,Dagan Alon,Doodlesack Amanda,Stenson Bryan A.,Sarma Deesha,Reseland Eric,Lee John H.,Kravitz Max S.,Antkowiak Peter S.,Shvilkina Tatyana,Espinosa Aimee,Halalau Alexandra,Demarco Carmen,Davila Francisco,Davila Hugo,Sims Matthew,Maddens Nicholas,Berghea Ramona,Smith Scott,Palagiri Ashok V.,Ezekiel Clinton,Sadaka Farid,Iyer Karthik,Crisp Matthew,Azad Saleem,Oke Vikram,Friederich Andrew,Syed Anwaruddin,Gosai Falgun,Chawla Lavneet,Evans Neil,Thomas Kurian,Malkani Roneil,Patel Roshni,Mayer Stockton,Ali Farhan,Raghavakurup Lekshminarayan,Tafa Muleta,Singh Sahib,Raouf Samuel,Zhao Sihai Dave,Zhu Ruoqing,Bashir Rashid,Reddy Bobby,Shapiro Nathan I.

Abstract

AbstractBackgroundSepsis is a life-threatening condition that demands prompt treatment for improved patient outcomes. Its heterogenous presentation makes early detection challenging, highlighting the need for effective risk assessment tools. Artificial Intelligence (AI) models have the potential to accurately identify septic patients, but none have previously been FDA-authorized for commercial use. This study outlines the development and validation of the Sepsis ImmunoScore, the first FDA-authorized AI-based software designed to identify patients at risk of sepsis.MethodIn this prospective study, adult patients (18+) suspected of infection, as indicated by a blood culture order, were enrolled from five U.S. institutions between April 2017 and July 2022. The participants were divided into an algorithm development cohort (n=2,366), an internal validation cohort (n=393), and an external validation cohort (n=698). The primary endpoint was the presence of sepsis (Sepsis-3) within 24 hours of test initiation. Secondary endpoints included hospital length of stay, ICU admission within 24 hours, mechanical ventilation use within 24 hours, vasopressor use within 24 hours, and in-hospital mortality.ResultsThe Sepsis ImmunoScore demonstrated high diagnostic accuracy, with an AUC of 0.85 (0.83–0.87) in the derivation cohort, 0.80 (0.74–0.86) in internal validation, and 0.81 (0.77– 0.86) in external validation. The score was categorized into four risk levels for sepsis with corresponding likelihood ratios: low (0.1), medium (0.5), high (2.1), and very high (8.3). These risk categories also predicted in-hospital mortality: low (0.0%), medium (1.9%), high (8.7%), and very high (18.2%) in the external validation cohort. Similar trends were observed for other metrics, such as hospital length of stay, ICU utilization, mechanical ventilation, and vasopressor use.ConclusionsThe Sepsis ImmunoScore demonstrated high accuracy for identification and prediction of sepsis and critical illness that could enable prompt identification of patients at high risk of sepsis and adverse outcomes, potentially improving clinical decision-making and patient outcomes.DescriptionSepsis is a life-threatening acute condition that requires accurate and rapid identification to guide proper treatment. This study outlines the development and validation of the first FDA-authorized AI-based software to identify patients at risk of having sepsis.

Publisher

Cold Spring Harbor Laboratory

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3