Acute Care Diagnostics Collaboration: Assessment of a Bayesian clinical decision model integrating the Prehospital Sepsis Score and point-of-care lactate

Abstract

Previous research demonstrated that shock index and respiratory rate are highly predictive of intensive care unit admissions. ObjectiveThe objective of the study is to evaluate the integration of the prehospital sepsis project score (PSP-S) and point-of-care lactate in assisting prediction of severity of illness using Bayesian statistical modeling. MethodsThe PSP-S incorporates fever (38°C [100.4°F]) allotted with 1 point, shock index greater than or equal to 0.7 given 2 points, and a respiratory rate greater than or equal to 22 breaths per minute given 1 point for a total maximum score of 4 points. The patient population was stratified based on the PSP-S: 1 point is low risk, 2 points is moderate risk, and 3 to 4 points is high risk. Percentage risk was obtained based on intensive care unit admissions and used as pretest probability. Prehospital lactate pooled data were obtained and used to calculate likelihood ratio (LR). Percentage risk used as pretest probability and LRs for prehospital lactate were charted into the Bayesian nomogram to obtain posttest probabilities. Absolute diagnostic gain (ADG) and relative diagnostic gains (RDG) were then calculated. ResultsPooled data for prehospital point of care lactate demonstrated a positive LR of 1.6 and negative LR of 0.44. Posttest probability for low risk was 16% with an ADG of 6% and RDG of 160%. Moderate risk population yielded a posttest probability of 47%, ADG of 12.5%, and RDG of 136.2%. High-risk population resulted in a posttest probability of 72%, ADG of 12%, and RDG of 120%. ConclusionWe found that PSP-S can be clinically complemented with the use of point-of-care lactate.