Performance Evaluation of Prehospital Sepsis Prediction Models.

Abstract

Evaluate prediction models designed or used to identify patients with sepsis in the prehospital setting. Nested case-control study. Four emergency departments (EDs) in Utah. Adult nontrauma patient with available prehospital care records who received ED treatment during 2018 after arrival via ambulance. None. Of 16,620 patients arriving to a study ED via ambulance, 1,037 (6.2%) met Sepsis-3 criteria in the ED. Complete prehospital care data was available for 434 case patients with sepsis and 434 control patients without sepsis. Model discrimination for the outcome of meeting Sepsis-3 criteria in the ED was quantified using the area under the precision-recall curve (AUPRC), which yields a value equal to outcome prevalence for a noninformative model. Of 21 evaluated prediction models, only the Prehospital Early Sepsis Detection (PRESEP) model (AUPRC, 0.33 [95% CI, 0.27-0.41) outperformed unaided infection assessment by emergency medical services (EMS) personnel (AUPRC, 0.17 [95% CI, 0.13-0.23]) for prehospital prediction of patients who would meet Sepsis-3 criteria in the ED ( p < 0.001). PRESEP also outperformed the quick Sequential Organ Failure Assessment score (AUPRC, 0.13 [95% CI, 0.11-0.16]; p < 0.001). Among 28 evaluated dichotomous predictors of ED sepsis, sensitivity ranged from 6% to 91% and positive predictive value 8-100%. PRESEP exhibited modest sensitivity (60%) and positive predictive value (20%). PRESEP was the only evaluated prediction model that demonstrated better discrimination than unaided EMS infection assessment for the identification of ambulance-transported adult patients who met Sepsis-3 criteria in the ED.