Prehospital Identification of Patients with Severe Sepsis: Derivation and Validation of a Novel Screening Tool Pubblico

Abstract

Sepsis is a common, life-threatening inflammatory condition that can occur as a consequence of an active infection. In patients with sepsis, early identification and treatment are key components of reducing morbidity and mortality. Unfortunately, there is no standardized way to identify patients with sepsis in the Emergency Medical Services (EMS) setting, potentially delaying identification and live-saving treatment. The goal of this project was to derive and validate a predictive model and clinical risk prediction score for EMS identification of severe sepsis. We performed a retrospective cohort study of sequential, adult, at-risk patients transported by a city-wide Emergency Medical Services (EMS) system to a 900-bed, urban, public hospital between 2011 and 2012. At-risk patients were defined as having all 3 of the following criteria present in the EMS setting: heart rate >=90 beats per minute, 2) respiratory rate >=20 breaths per minute, and 3) systolic blood pressure >=110 mmHg. Among 66,439 EMS encounters, 555 patients were included for analysis, of which 14% (n=75) had severe sepsis. Severe sepsis (including septic shock) was defined by review of clinical documentation. The cohort was randomly divided into derivation (80%) and validation (20%) subgroups, and logistic regression was performed to determine which EMS characteristics were associated with a diagnosis of severe sepsis. The following six risk factors were found to be EMS predictors of severe sepsis: older age, EMS transport from a nursing home, Emergency Medical Dispatch (EMD) 9-1-1 chief complaint category of "Sick Person", hot tactile temperature, low systolic blood pressure, and low oxygen saturation. The final predictive model showed good discrimination in both derivation and validation subgroups (AUC 0.832 and 0.803, respectively). Sensitivity of the final model was 91% in the derivation subgroup and 78% in the validation subgroup; specificity was 34% and 26%, respectively. Finally, the final predictive model was converted into a prehospital severe sepsis (PreSS) risk prediction score. A PreSS score of >=2 points performed with a sensitivity of 86% and a specificity of 47%. Further validation of the PreSS score is needed before determining the potential benefit of its use.

Table of Contents

INTRODUCTION..............1 BACKGROUND................3 METHODS...................6 RESULTS..................11 DISCUSSION...............14 FIGURES AND TABLES.......18 Table 1..........18 Figure 1.........19 Table 2..........20 Table 3..........21 Table 4..........22 Table 5..........23 Table 6..........24 Table 7..........26 Table 8..........28 Figure 2.........29 Table 9..........30 Figure 3.........31 Table 10.........32 REFERENCES...............33

About this Master's Thesis

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School	Laney Graduate School
Department	Clinical Research Curriculum Program
Degree	MS
Submission	Master's Thesis
Language	English
Research Field	Health Sciences, Medicine and Surgery
Parola chiave	emergency medical services sepsis prehospital
Committee Chair / Thesis Advisor	Sevransky, Jonathan , Emory University
Committee Members	Isakov, Alexander, Emory University Martin, Greg, Emory University Shah, Amit Jasvant, Emory University Klein, Mitchel, Emory University

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