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Prediction of Impending Septic Shock in Children With Sepsis
Ran Liu1,2, Joseph L Greenstein1,2, James C Fackler3
1Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD.
Insights
This study adapted a risk score to predict pediatric septic shock, providing an early warning and stratifying patients into risk groups. Early prediction of pediatric septic shock improves patient outcomes.
Area of Science:
- Pediatric critical care medicine
- Clinical informatics
- Biostatistics
Background:
- Sepsis and septic shock are major causes of in-hospital mortality in children.
- Timely intervention is critical, but treatment delays are common.
- Early prediction of septic shock progression in pediatric patients is needed to improve outcomes.
Purpose of the Study:
- To adapt a previously developed time-evolving risk score for adult patients to predict septic shock in pediatric sepsis patients.
- To determine if these risk scores can stratify patients into distinct temporal evolution groups.
- To enhance the actionable window for interventions in pediatric sepsis.
Main Methods:
- Retrospective cohort study of 6,161 pediatric patients admitted to a pediatric intensive care unit (PICU).
- Development and application of time-evolving risk models to predict transition to septic shock.
- Utilized spectral clustering to stratify patients based on risk score trajectories.
Main Results:
- Achieved an area under the receiver operating curve of 0.90 for early prediction of septic shock.
- Obtained a median early warning time of 8.9 hours.
- Identified two distinct patient clusters with differing septic shock prevalence, mortality, and fluid resuscitation status.
Conclusions:
- The methodology is applicable for early prediction and risk stratification of septic shock in pediatric sepsis.
- Risk score evolution analysis confirmed an abrupt transition preceding septic shock onset in children.
- Patients were successfully stratified into low- and high-risk categories based on risk score trajectories.
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.
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