An Early Warning Scoring System to Identify Septic Patients in the Prehospital Setting: The PRESEP Score. (25th June 2015)

• Record Type: Journal Article
• Authors: Bayer, Ole; Schwarzkopf, Daniel; Stumme, Christoph; Stacke, Angelika; Hartog, Christiane S.; Hohenstein, Christian; Kabisch, Björn; Reichel, Jens; Reinhart, Konrad; Winning, Johannes; Zehtabchi, Shahriar
• Is Part Of: Academic emergency medicine. Volume 22:Number 7(2015:Jul.)
• Page Start: 868
• Page End: 871
• Publication Date: 2015-06-25
• Subjects: Emergency medicine -- Periodicals; 616.02505
• DOI: 10.1111/acem.12707
• Languages: English
• ISSNs: 1069-6563

Abstract

<abstract abstract-type="main" id="acem12707-abs-0001"> <title>Abstract</title> <sec id="acem12707-sec-0001" sec-type="section"> <title>Objectives</title> <p>The objective was to develop and evaluate an early sepsis detection score for the prehospital setting.</p> </sec> <sec id="acem12707-sec-0002" sec-type="section"> <title>Methods</title> <p>A retrospective analysis of consecutive patients who were admitted by emergency medical services (EMS) to the emergency department of the Jena University Hospital was performed. Because potential predictors for sepsis should be based on consensus criteria, the following parameters were extracted from the EMS protocol for further analysis: temperature, heart rate (HR), respiratory rate (RR), oxygen saturation (SaO<sub>2</sub>), Glasgow Coma Scale score, blood glucose, and systolic blood pressure (sBP). Potential predictors were stratified based on inspection of Loess graphs. Backward model selection was performed to select risk factors for the final model. The Prehospital Early Sepsis Detection (PRESEP) score was calculated as the sum of simplified regression weights. Its predictive validity was compared to the Modified Early Warning Score (MEWS), the Robson screening tool, and the BAS 90‐30‐90.</p> </sec> <sec id="acem12707-sec-0003" sec-type="section"> <title>Results</title> <p>A total of 375 patients were included in the derivation sample; 93 (24.8%) of these had sepsis, including 60 patients with severe sepsis and 12 patients with septic shock. Backward model selection identified temperature, HR, RR, SaO<sub>2</sub>, and sBP for inclusion in the PRESEP score. Simplified weights were as follows: temperature > 38°C = 4, temperature < 36°C = 1, HR > 90 beats/min<italic> =</italic> 2, RR > 22 breaths/min<italic> =</italic> 1, SaO<sub>2</sub> < 92% = 2, and sBP < 90 mm Hg = 2. The cutoff value for a possible existing septic disease based on maximum Youden's index was ≥4 (sensitivity 0.85, specificity 0.86, positive predictive value [PPV] 0.66, and negative predictive value [NPV] 0.95). The area under the receiver operating characteristic curve (AUC) of the PRESEP score was 0.93 (95% confidence interval [CI] = 0.89 to 0.96) and was larger than the AUC of the MEWS (0.93 vs. 0.77, p < 0.001). The PRESEP score surpassed MEWS and BAS 90‐60‐90 for sensitivity (0.74 and 0.62, respectively), specificity (0.75 and 0.83), PPV (0.45 and 0.51), and NPV (0.91 and 0.89). The Robson screening tool had a higher sensitivity and NPV (0.95 and 0.97), but its specificity and PPV were lower (0.43 and 0.32).</p> </sec> <sec id="acem12707-sec-0004" sec-type="section"> <title>Conclusions</title> <p>The PRESEP score could be a valuable tool for identifying septic patients in the prehospital setting in the case of suspected infection. It should be prospectively validated.</p> </sec> </abstract>