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External validation and biomarker assessment of a high-risk, data-driven pediatric sepsis phenotype characterized by
Mihir R Atreya1,2, Tellen D Bennett3, Alon Geva4
1Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Research Foundation, Cincinnati, 45229, OH, USA.
Insights
Identifying children with sepsis-associated multiple organ dysfunction syndrome (MODS) who are at risk for poor outcomes is crucial. The persistent hypoxemia, encephalopathy, and shock (PHES) phenotype accurately identifies these high-risk pediatric patients and correlates with inflammatory biomarkers.
Area of Science:
- Pediatric critical care medicine
- Data-driven phenotyping
- Sepsis research
Background:
- Identifying children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes is challenging.
- Electronic health records (EHR) offer a valuable resource for data-driven phenotyping.
- The 'persistent hypoxemia, encephalopathy, and shock' (PHES) phenotype is a data-driven approach to identify at-risk pediatric patients.
Approach:
- Externally validated the PHES phenotype using a random forest classifier trained on EHR data.
- Assigned PHES phenotype membership in a test set of pediatric septic shock patients.
- Compared biomarker profiles and assessed association with mortality and MODS risk-strata.
Key Points:
- The classifier accurately predicted PHES phenotype membership (AUROC 0.91).
- PHES phenotype was independently associated with complicated course (aOR 4.1) and 28-day mortality (aOR 4.8).
- PHES phenotype patients showed increased systemic inflammation, endothelial activation, and overlapped with high-risk biomarker strata.
Conclusions:
- The PHES phenotype is reproducible in pediatric septic shock patients.
- The PHES phenotype is independently associated with poor clinical outcomes.
- The PHES phenotype aligns with established high-risk biomarker-based strata.
Objective:
Identification of children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes remains a challenge. Data-driven phenotyping approaches that leverage electronic health record (EHR) data hold promise given the widespread availability of EHRs. We sought to externally validate the data-driven 'persistent hypoxemia, encephalopathy, and shock' (PHES) phenotype and determine its association with inflammatory and endothelial biomarkers, as well as biomarker-based pediatric risk-strata.
Design:
We trained and validated a random forest classifier using organ dysfunction subscores in the EHR dataset used to derive the PHES phenotype. We used the classifier to assign phenotype membership in a test set consisting of prospectively enrolled pediatric septic shock patients. We compared biomarker profiles of those with and without the PHES phenotype and determined the association with established biomarker-based mortality and MODS risk-strata.
Setting:
25 pediatric intensive care units (PICU) across the U.S.
Patients:
EHR data from 15,246 critically ill patients sepsis-associated MODS and 1,270 pediatric septic shock patients in the test cohort of whom 615 had biomarker data.
Interventions:
None.
Measurements And Main Results:
The area under the receiver operator characteristic curve (AUROC) of the new classifier to predict PHES phenotype membership was 0.91(95%CI, 0.90-0.92) in the EHR validation set. In the test set, patients with the PHES phenotype were independently associated with both increased odds of complicated course (adjusted odds ratio [aOR] of 4.1, 95%CI: 3.2-5.4) and 28-day mortality (aOR of 4.8, 95%CI: 3.11-7.25) after controlling for age, severity of illness, and immuno-compromised status. Patients belonging to the PHES phenotype were characterized by greater degree of systemic inflammation and endothelial activation, and overlapped with high risk-strata based on PERSEVERE biomarkers predictive of death and persistent MODS.
Conclusions:
The PHES trajectory-based phenotype is reproducible, independently associated with poor clinical outcomes, and overlap with higher risk-strata based on validated biomarker approaches.
