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Predictive modeling of bacterial infections and antibiotic therapy needs in critically ill adults.

Abstract：

:Unnecessary antibiotic regimens in the intensive care unit (ICU) are associated with adverse patient outcomes and antimicrobial resistance. Bacterial infections (BI) are both common and deadly in ICUs, and as a result, patients with a suspected BI are routinely started on broad-spectrum antibiotics prior to having confirmatory microbiologic culture results or when an occult BI is suspected, a practice known as empiric antibiotic therapy (EAT). However, EAT guidelines lack consensus and existing methods to quantify patient-level BI risk rely largely on clinical judgement and inaccurate biomarkers or expensive diagnostic tests. As a consequence, patients with low risk of BI often are continued on EAT, exposing them to unnecessary side effects. Augmenting current intuition-based practices with data-driven predictions of BI risk could help inform clinical decisions to shorten the duration of unnecessary EAT and improve patient outcomes. We propose a novel framework to identify ICU patients with low risk of BI as candidates for earlier EAT discontinuation. For this study, patients suspected of having a community-acquired BI were identified in the Medical Information Mart for Intensive Care III (MIMIC-III) dataset and categorized based on microbiologic culture results and EAT duration. Using structured longitudinal data collected up to 24-, 48-, and 72-hours after starting EAT, our best models identified patients at low risk of BI with AUROCs up to 0.8 and negative predictive values >93%. Overall, these results demonstrate the feasibility of forecasting BI risk in a critical care setting using patient features found in the electronic health record and call for more extensive research in this promising, yet relatively understudied, area.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Eickelberg G,Sanchez-Pinto LN,Luo Y

doi

10.1016/j.jbi.2020.103540

subject

Has Abstract

pub_date

2020-09-01 00:00:00

pages

103540

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(20)30168-4

journal_volume

109

pub_type

杂志文章

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