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. 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. 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. 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.

中文翻译：

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胸痛患者紧急医疗调度优先级的新预测模型的潜力：一项队列研究

开发具有高灵敏度和令人满意的特异性的紧急医疗调度 (EMD) 中心预测模型，以在评估胸痛患者时分别识别高优先级患者和适合非紧急护理的患者。对 2018 年因胸痛联系瑞典 EMD 中心的 2917 例未选择胸痛患者进行的观察性队列研究。应用多变量逻辑回归开发预测低风险或高风险状况的模型，即发生时间敏感出院诊断。为识别适合高优先级和低优先级调度的患者而开发了预测模型，分别使用 11 和 10 个变量。高风险预测模型的受试者工作特征曲线下面积（AUROC）为 0。79，低风险模型为 0.74。在应用高风险预测模型时，56% 的 EMS 任务被给予最高优先级，而当前标准为 65%。在应用低风险模型时，7% 的优先级最低，而当前标准为 1%。新的预测模型在灵敏度以及高风险和低风险预测的正负预测值方面优于当今的调度优先级准确性。与今天的标准相比，低风险模型预测的患者数量几乎是低风险疾病的六倍。这样做并没有增加被错误评估为低风险的高风险患者的数量。通过引入基于逻辑回归分析的预测模型，使用由标准 EMD 问题获得的关于年龄、性别、病史和症状学，EMD 优先级与使用当前基于索引的标准相比可以得到改善。这将允许更有效的紧急医疗服务资源分配。