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

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 hospita...

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Veröffentlicht in:Frontiers in pediatrics Jg. 9; S. 726870
Hauptverfasser: Tabaie, Azade, Orenstein, Evan W., Nemati, Shamim, Basu, Rajit K., Clifford, Gari D., Kamaleswaran, Rishikesan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media S.A 15.09.2021
Schlagworte:
explainable machine learning
infection
Pediatrics
predictive model
PSI
sepsis
infection
predictive model
explainable machine learning
sepsis
PSI
ISSN:2296-2360, 2296-2360
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Zusammenfassung: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.
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Reviewed by: Robert Kelly, Children's Hospital of Orange County, United States; Luc Morin, Université Paris Saclay, France
Edited by: Brenda M. Morrow, University of Cape Town, South Africa
This article was submitted to Pediatric Critical Care, a section of the journal Frontiers in Pediatrics
ISSN:2296-2360
2296-2360
DOI:10.3389/fped.2021.726870