作者
Heather M Giannini, Jennifer C Ginestra, Corey Chivers, Michael Draugelis, Asaf Hanish, William D Schweickert, Barry D Fuchs, Laurie Meadows, Michael Lynch, Patrick J Donnelly, Kimberly Pavan, Neil O Fishman, C William Hanson III, Craig A Umscheid
发表日期
2019/11/1
期刊
Critical care medicine
卷号
47
期号
11
页码范围
1485-1492
出版商
LWW
简介
Objectives:
Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice and patient outcomes.
Design:
Retrospective cohort for algorithm derivation and validation, pre-post impact evaluation.
Setting:
Tertiary teaching hospital system in Philadelphia, PA.
Patients:
All non-ICU admissions; algorithm derivation July 2011 to June 2014 (n= 162,212); algorithm validation October to December 2015 (n= 10,448); silent versus alert comparison January 2016 to February 2017 (silent n= 22,280; alert n= 32,184).
Interventions:
A random-forest classifier, derived and validated using electronic health record data, was deployed both silently and later with an alert to notify clinical teams of sepsis prediction.
Measurement and Main Result:
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