The predictive value of variables measurable in the ambulance and the development of the Predict Sepsis screening tools: a prospective cohort study

Abstract

Background: Despite sepsis being a time critical condition with a high mortality, it is often not identified in a timely fashion. The aim of the current study was to create a screening tool based on bedside measurable variables predictive of sepsis among ambulance patients with infection according to clinical judgment by ambulance personnel.

Methods: Prospective cohort study of 551 adult patients presenting with suspected infection, performed in the ambulance setting of Stockholm during 2017-2018. 18 variables were measured in the ambulance (8 keywords related to medical history, 6 vital signs, 4 point-of-care blood tests, in addition to age, gender, and comorbidity. Logistic regression, area under the curve (AUC) and classification trees were used to study the association with sepsis. The AUC, sensitivity, specificity, predictive values and likelihood ratios were used to evaluate the predictive ability of sepsis screening models.

Results: The six variables with the strongest association with sepsis were: systolic blood pressure ≤ 100 mmHg, temperature > 38.5 °C, GCS < 15, lactate > 4 mmol/L, gastrointestinal symptoms, and a history of acute altered mental status. These were combined into the Predict Sepsis screening tool 1, with a sensitivity of 0.90, specificity 0.41, AUC 0.77; 95% confidence interval [CI] 0.73-0.81, PPV 0.52, and NPV 0.86. Combining a history of acute altered mental status with GCS < 15 and excluding lactate in the Predict Sepsis screening tool 2 did not noticeably affect the AUC. In addition, the AUCs of these models did not differ noticeably when compared to a model including vital signs alone, with novel calculated cut-offs; the Predict Sepsis screening tool 3.

Conclusions: Systolic blood pressure ≤ 100 mmHg, temperature > 38.5 °C, GCS < 15, lactate > 4 mmol/L, gastrointestinal symptoms, and a history of acute altered mental status demonstrated the strongest association with sepsis. We present three screening tools to predict sepsis with similar sensitivity. The results indicated no noticeable increase of predictive ability by including symptom-variables and blood tests to a sepsis screening tool in the current study population.

Trial registration: NCT03249597.

Keywords: Emergency care; Emergency medical services; Prehospital; Screening; Sepsis.

Fig. 1

Flow chart of inclusion and exclusion. ED = Emergency Department, PV=Predictive value, LR = Likelihood ratio, AUC = Area under the receiver operating curve

Fig. 2

Classification tree^α presenting the variables which, at each step, had the strongest association with sepsis*. GCS = Glasgow Coma Scale, Temp = Temperature, SBP=Systolic Blood Pressure, HR = Heart Rate, SpO2 = Saturation of peripheral Oxygen, CHAID = Chi-square Automatic Interaction Detector. ^αThe CHAID algorithm was used to build the tree. *among 551 adult patients with infection according to clinical judgment by ambulance personnel and documentation of to determine whether the patient had sepsis or not. The prevalence of sepsis is bolded and calculated based on the total number of patients in each node (n). Darker blue filling of the box indicates a keyword reflecting medical history. Interpretation; example: “Of the 115 patients with a decreased level of consciousness (GCS<15), 67% had sepsis. If the patients also had fever (Temp>38.0°C), the prevalence of sepsis increased to 86% of the 65 in this group”. All Bonferroni-adjusted values were < 0.05 for all nodes

Fig. 3

ROC curves for models based on variable groups and combinations of these. ROC = Receiver Operating Characteristic, POCs = point-of-care blood tests. Only variables significantly associated with sepsis in the univariable analysis are included in the models

Fig. 4

The Predict Sepsis screening tools. The Predict Sepsis screening tools are developed for adult, non-trauma patients with suspected infection according to clinical judgment by ambulance personnel