Prediction of Impending Septic Shock in Children With Sepsis

Ran Liu; Joseph L Greenstein; James C Fackler; Jules Bergmann; Melania M Bembea; Raimond L Winslow

doi:10.1097/CCE.0000000000000442

Prediction of Impending Septic Shock in Children With Sepsis

Crit Care Explor. 2021 Jun 15;3(6):e0442. doi: 10.1097/CCE.0000000000000442. eCollection 2021 Jun.

Authors

Ran Liu^{1

2}, Joseph L Greenstein^{1

2}, James C Fackler³, Jules Bergmann³, Melania M Bembea^{3

4}, Raimond L Winslow^{1

2}

Affiliations

¹ Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD.
² Department of Biomedical Engineering, The Johns Hopkins University School of Medicine & Whiting School of Engineering, Baltimore, MD.
³ Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
⁴ Department of Pediatrics, The Johns Hopkins University School of Medicine, Baltimore, MD.

Abstract

Objectives: Sepsis and septic shock are leading causes of in-hospital mortality. Timely treatment is crucial in improving patient outcome, yet treatment delays remain common. Early prediction of those patients with sepsis who will progress to its most severe form, septic shock, can increase the actionable window for interventions. We aim to extend a time-evolving risk score, previously developed in adult patients, to predict pediatric sepsis patients who are likely to develop septic shock before its onset, and to determine whether or not these risk scores stratify into groups with distinct temporal evolution once this prediction is made.

Design: Retrospective cohort study.

Setting: Academic medical center from July 1, 2016, to December 11, 2020.

Patients: Six-thousand one-hundred sixty-one patients under 18 admitted to the Johns Hopkins Hospital PICU.

Interventions: None.

Measurements and main results: We trained risk models to predict impending transition into septic shock and compute time-evolving risk scores representative of a patient's probability of developing septic shock. We obtain early prediction performance of 0.90 area under the receiver operating curve, 43% overall positive predictive value, patient-specific positive predictive value as high as 62%, and an 8.9-hour median early warning time using Sepsis-3 labels based on age-adjusted Sequential Organ Failure Assessment score. Using spectral clustering, we stratified pediatric sepsis patients into two clusters differing in septic shock prevalence, mortality, and proportion of patients adequately fluid resuscitated.

Conclusions: We demonstrate the applicability of our methodology for early prediction and stratification for risk of septic shock in pediatric sepsis patients. Through analyses of risk score evolution over time, we corroborate our past finding of an abrupt transition preceding onset of septic shock in children and are able to stratify pediatric sepsis patients using their risk score trajectories into low and high-risk categories.

Keywords: cluster analysis; electronic health records; intensive care units; machine learning; pediatric; sepsis; septic; shock.