Validation of a Computational Phenotype to Identify Acute Brain Dysfunction in Pediatric Sepsis

Abstract

To validate a computational phenotype that identifies acute brain dysfunction (ABD) based on clinician concern for neurologic or behavioral changes in pediatric sepsis.DESIGN: Retrospective observational study. SETTING:Single academic children's hospital. PATIENTS:Four thousand two hundred eighty-nine index sepsis episodes. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS:An existing computational phenotype of ABD was optimized to include routinely collected variables indicative of clinician concern for acute neurologic or behavioral change (completion of CT or MRI, electroencephalogram, or new antipsychotic administration). First, the computational phenotype was compared with an ABD reference standard established from chart review of 527 random sepsis episodes to determine criterion validity. Next, the computational phenotype was compared with a separate validation cohort of 3,762 index sepsis episodes to determine content and construct validity. Criterion validity for the final phenotype had sensitivity 83% (95% CI, 76-89%), specificity 93% (90-95%), positive predictive value 84% (77-89%), and negative predictive value 93% (90-96%). In the validation cohort, the computational phenotype identified ABD in 35% (95% CI 33-36%). Content validity was demonstrated as those with the ABD computational phenotype were more likely to have characteristics of neurologic dysfunction and severe illness than those without the ABD phenotype, including nonreactive pupils (15% vs 1%; p < 0.001), Glasgow Coma Scale less than 5 (44% vs 12%; p < 0.001), greater than or equal to two nonneurologic organ dysfunctions (50% vs 25%; p < 0.001), and need for intensive care (81% vs 65%; p < 0.001). Construct validity was demonstrated by higher odds for mortality (odds ratio [OR], 6.9; 95% CI, 5.3-9.1) and discharge to rehabilitation (OR, 11.4;) in patients with, versus without, the ABD computational phenotype. CONCLUSIONS:A computational phenotype of ABD indicative of clinician concern for new neurologic or behavioral change offers a valid retrospective measure to identify episodes of sepsis that involved ABD. This computational phenotype provides a feasible and efficient way to study risk factors for and outcomes from ABD using routinely collected clinical data.

Editor’s Choice Articles for October

“…As a reader of PCCM you may also want to review our recent material about timing of acute neurologic dysfunction in relation to sepsis recognition (13, 14) and the choice of clinical assessment (15). Finally, also consider the computational phenotype of “acute brain dysfunction” regarding database research—based on using neuroimaging or electroencephalography as part of evaluating neurologic change—which had better diagnostic performance than the GCS in sepsis (16).…”

Editor’s Choice Articles for December

“…Alcamo AA, Barren GJ, Becker AE, et al: Validation of a Computational Phenotype to Identify Acute Brain Dysfunction in Pediatric Sepsis (3).…”

Severe Neurological Sequelae in Pediatric Patients with SARS-CoV-2 or MIS-C

“…7,8 These phenotypes show great potential as research tools to identify risk factors for neurological and functional outcomes. Their current utility at the bedside may be limited by reliance on the Glasgow Coma Scale or retrospective detection of clinical interventions (ie, neuroimaging, electroencephalography, and antipsychotic drugs).…”

“…Future studies building on this significant work may benefit from the use of computational phenotypes. In the case of sepsis, such approaches can identify patients with acute brain dysfunction using readily available data from large clinical databases . These phenotypes show great potential as research tools to identify risk factors for neurological and functional outcomes.…”