Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines

• Type of Material: Article
• Authors: Azade Tabaie, Emory University; Evan Orenstein, Emory University; Shamim Nemati, Emory University; Rajit Basu, Emory University; Gari Clifford, Emory University; Rishikesan Kamaleswaran, Emory University
• Language: English
• Date: 2021-09-15
• Publisher: FRONTIERS MEDIA SA
• Publication Version: Final Published Version
• Copyright Statement: © 2021 Tabaie, Orenstein, Nemati, Basu, Clifford and Kamaleswaran.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3389/fped.2021.726870
• Title of Journal or Parent Work: FRONTIERS IN PEDIATRICS
• Volume: 9
• Start Page: 726870
• End Page: 726870
• Grant/Funding Information: This project was supported by an institutional grant provided by the Children's Healthcare of Atlanta, Atlanta, GA through The Pediatric Technology Center, in conjunction with the Health Analytics Council. AT was also funded by the Surgical Critical Care Initiative (SC2i), Department of Defense's Defense Health Program Joint Program Committee 6/Combat Casualty Care (USUHS HT9404-13-1-0032 and HU0001-15-2-0001). All views expressed in this article are the authors' own and do not necessarily reflect the views of the authors' employers and funding bodies.
• Keywords: OUTCOMES; BLOOD-STREAM INFECTIONS; SEPSIS; sepsis; infection; Life Sciences & Biomedicine; UPDATE; PREVENTION; PSI; predictive model; Science & Technology; explainable machine learning; Pediatrics
• Research Categories: Engineering, Biomedical

Abstract

Objective: Predict the onset of presumed serious infection, defined as a positive blood culture drawn and new antibiotic course of at least 4 days (PSI*), among pediatric patients with Central Venous Lines (CVLs). Design: Retrospective cohort study. Setting: Single academic children's hospital. Patients: All hospital encounters from January 2013 to December 2018, excluding the ones without a CVL or with a length-of-stay shorter than 24 h. Measurements and Main Results: Clinical features including demographics, laboratory results, vital signs, characteristics of the CVLs and medications used were extracted retrospectively from electronic medical records. Data were aggregated across all hospitals within a single pediatric health system and used to train a deep learning model to predict the occurrence of PSI* during the next 48 h of hospitalization. The proposed model prediction was compared to prediction of PSI* by a marker of illness severity (PELOD-2). The baseline prevalence of line infections was 0.34% over all segmented 48-h time windows. Events were identified among cases using onset time. All data from admission till the onset was used for cases and among controls we used all data from admission till discharge. The benchmarks were aggregated over all 48 h time windows [N=748,380 associated with 27,137 patient encounters]. The model achieved an area under the receiver operating characteristic curve of 0.993 (95% CI = [0.990, 0.996]), the enriched positive predictive value (PPV) was 23 times greater than the base prevalence. Conversely, prediction by PELOD-2 achieved a lower PPV of 1.5% [0.9%, 2.1%] which was 5 times the baseline prevalence. Conclusion: A deep learning model that employs common clinical features in the electronic health record can help predict the onset of CLABSI in hospitalized children with central venous line 48 hours prior to the time of specimen collection.