Key result
New prediction models based on standard dispatch questions outperformed the current criteria-based index in prioritizing chest pain patients, achieving an AUROC of 0.79 for high-risk prediction.
Why the study?
Emergency medical dispatch centres needed prediction models with high sensitivity and satisfying specificity to identify high-priority patients and those suitable for non-emergency care among patients presenting with chest pain.
Do new logistic regression-based prediction models improve the accuracy of emergency medical dispatch prioritisation for patients with chest pain compared to the current criteria-based index?
Cohort (n=2,917)
No
Do new logistic regression-based prediction models improve the accuracy of emergency medical dispatch prioritisation for patients with chest pain compared to the current criteria-based index?
New logistic regression-based prediction models using standard dispatch questions significantly improve the accuracy of emergency medical dispatch prioritisation for chest pain compared to current criteria-based indices.
Answers are grounded in this paper and cite their sources.
May refine chest pain dispatch prioritization; hypothesis-generating pending prospective validation.
OBJECTIVES: To develop emergency medical dispatch (EMD) centre prediction models with high sensitivity and satisfying specificity to identify high-priority patients and patients suitable for non-emergency care respectively, when assessing patients with chest pain. METHODS: Observational cohort study of 2917 unselected patients with chest pain who contacted an EMD centre in Sweden due to chest pain during 2018. Multivariate logistic regression was applied to develop models predicting low-risk or high-risk condition, that is, occurrence of time-sensitive diagnosis on hospital discharge. RESULTS: Prediction models were developed for the identification of patients suitable for high- and low-priority dispatch, using 11 and 10 variables respectively. The area under the receiver-operating characteristic curve (AUROC) for the high-risk prediction model was 0.79 and for the low-risk model it was 0.74. When applying the high-risk prediction model, 56% of the EMS missions were given highest priority, compared with 65% with the current standard. When applying the low-risk model, 7% were given the lowest priority compared to 1% for the current standard. The new prediction models outperformed today's dispatch priority accuracy in terms of sensitivity as well as positive and negative predictive value in both high- and low-risk prediction. The low-risk model predicted almost six times as many patients as having low-risk conditions compared with today's standard. This was done without increasing the number of high-risk patients wrongly assessed as low-risk. CONCLUSIONS: By introducing prediction models, based on logistic regression analyses, using variables obtained by standard EMD-questions on age, sex, medical history and symptomology, EMD prioritisation can be improved compared with using current criteria index-based ones. This will allow a more efficient emergency medical services resource allocation.
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Wibring et al. (2022) conducted a cohort in Chest pain (n=2,917). Logistic regression prediction models for emergency medical dispatch vs. Swedish Index for Medical Priority Dispatch (SIMPD) was evaluated on Risk classification group (low- or high-risk condition on hospital discharge) (AUROC 0.79 for high-risk model and 0.74 for low-risk model, p=<0.001). New prediction models based on standard dispatch questions outperformed the current criteria-based index in prioritizing chest pain patients, achieving an AUROC of 0.79 for high-risk prediction.
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