Diagnostic Identification of Acute Brain Dysfunction in Pediatric Sepsis and Septic Shock in the Electronic Health Record: A Comparison of Four Definitions in a Reference Dataset.

Abstract

Objectives: Acute brain dysfunction (ABD) in pediatric sepsis has a prevalence of 20%, but can be difficult to identify. Our previously validated ABD computational phenotype (CP ABD ) used variables obtained from the electronic health record indicative of clinician concern for acute neurologic or behavioral change. We tested whether the CP ABD has better diagnostic performance to identify confirmed ABD than other definitions using the Glasgow Coma Scale or delirium scores.

Design: Diagnostic testing in a curated cohort of pediatric sepsis/septic shock patients.

Setting: Quaternary freestanding children's hospital.

Subjects: The test dataset comprised 527 children with sepsis/septic shock managed between 2011 and 2021 with a prevalence (pretest probability) of confirmed ABD of 30% (159/527).

Measurements and main results: CP ABD was based on use of neuroimaging, electroencephalogram, and/or administration of new antipsychotic medication. We compared the performance of the CP ABD with three GCS/delirium-based definitions of ABD-Proulx et al, International Pediatric Sepsis Consensus Conference, and Pediatric Organ Dysfunction Information Update Mandate. The posttest probability of identifying ABD was highest in CP ABD (0.84) compared with other definitions. CP ABD also had the highest sensitivity (83%; 95% CI, 76-89%) and specificity (93%; 95% CI, 90-96%). The false discovery rate was lowest in CP ABD (1-in-6) as was the false omission rate (1-in-14). Finally, the prevalence threshold for the definitions varied, with the CP ABD being the definition closest to 20%.

Conclusions: In our curated dataset of pediatric sepsis/septic shock, CP ABD had favorable characteristics to identify confirmed ABD compared with GCS/delirium-based definitions. The CP ABD can be used to further study the impact of ABD in studies using large electronic health datasets.