Performance Evaluation of Prehospital Sepsis Prediction Models
Abstract
OBJECTIVES: Evaluate prediction models designed or used to identify patients with sepsis in the prehospital setting. DESIGN: Nested case-control study. SETTING: Four emergency departments (EDs) in Utah. PATIENTS: Adult nontrauma patient with available prehospital care records who received ED treatment during 2018 after arrival via ambulance. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: 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…
How strong is the evidence?
Evidence Hub reads this as a case-control, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 16,620 participants or records.
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