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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

Pediatric critical care medicine(2024)

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摘要
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 (CPABD) used variables obtained from the electronic health record indicative of clinician concern for acute neurologic or behavioral change. We tested whether the CPABD 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: CPABD was based on use of neuroimaging, electroencephalogram, and/or administration of new antipsychotic medication. We compared the performance of the CPABD 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 CPABD (0.84) compared with other definitions. CPABD also had the highest sensitivity (83%; 95% CI, 76–89%) and specificity (93%; 95% CI, 90–96%). The false discovery rate was lowest in CPABD (1-in-6) as was the false omission rate (1-in-14). Finally, the prevalence threshold for the definitions varied, with the CPABD being the definition closest to 20%. Conclusions: In our curated dataset of pediatric sepsis/septic shock, CPABD had favorable characteristics to identify confirmed ABD compared with GCS/delirium-based definitions. The CPABD can be used to further study the impact of ABD in studies using large electronic health datasets.
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