Development and Validation of a Model to Predict Pediatric Septic Shock Using Data Known 2 Hours After Hospital Arrival
Halden F. Scott, Kathryn Colborn, Carter Sevick, Lalit Bajaj, Sara J. Deakyne Davies, Diane L. Fairclough, Niranjan Kissoon, Allison Kempe
Abstract
Objectives: To use electronic health record data from the first 2 hours of care to derive and validate a model to predict hypotensive septic shock in children with infection. Design: Derivation-validation study using an existing registry. Setting: Six emergency care sites within a regional pediatric healthcare system. Three datasets of unique visits were designated: Patients: Patients in whom clinicians were concerned about serious infection from 60 days to 18 years were included; those with septic shock in the first 2 hours were excluded. There were 2,318 included visits; 197 developed septic shock (8.5%). Interventions: Lasso with 10-fold cross-validation was used for variable selection; logistic regression was then used to construct a model from those variables in the training set. Variables were derived from electronic health record data known in the first 2 hours, including vital signs, medical history, demographics, and laboratory information. Test characteristics at two thresholds were evaluated: 1) optimizing sensitivity and specificity and 2) set to 90% sensitivity. Measurements and Main Results: Septic shock was defined as systolic hypotension and vasoactive use or greater than or equal to 30 mL/kg isotonic crystalloid administration in the first 24 hours. A model was created using 20 predictors, with an area under the receiver operating curve in the training set of 0.85 (0.82–0.88); 0.83 (0.78–0.89) in the temporal test set and 0.83 (0.60–1.00) in the geographic test set. Sensitivity and specificity varied based on cutpoint; when sensitivity in the training set was set to 90% (83–94%), specificity was 62% (60–65%). Conclusions: This model predicted risk of septic shock in children with suspected infection 2 hours after arrival, a critical timepoint for emergent treatment and transfer decisions. Varied cutpoints could be used to customize sensitivity to clinical context.