Development and Validation of a Model to Predict Pediatric Septic Shock Using Data Known 2 Hours After Hospital Arrival.

Objective:: To use Electronic Health Record (EHR) data from the first two 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: 1. training set (5 sites, 4/1/13-12/31/16); 2. temporal test set (5 sites, 1/1/17-6/30/18); 3. geographic test set (6 site, 4/1/13-6/30/18).

Patients:: Patients in whom clinicians were concerned about serious infection from 60 days-17 years were included; those with septic shock in the first two hours were excluded. There were 2318 included visits; 197 developed septic shock (8.5%).

Interventions:: Lasso with tenfold 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 EHR data known in the first two hours, including vital signs, medical history, demographics, laboratory information. Test characteristics at two thresholds were evaluated: 1) optimizing sensitivity and specificity, 2) set to 90% sensitivity.

Measurements And Main Results:: Septic shock was defined as systolic hypotension and vasoactive use or ≥30 ml/kg isotonic crystalloid administration in the first 24 hours. A model was created using twenty 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; 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.