Emergency medical services (EMSs) face critical situations that require patient risk classification based on analytical and vital signs. We aimed to establish clustering-derived phenotypes based on prehospital analytical and vital signs that allow risk stratification. This was a prospective, multicenter, EMS-delivered, ambulance-based cohort study considering six advanced life support units, 38 basic life support units, and four tertiary hospitals in Spain. Adults with unselected acute diseases managed by the EMS and evacuated with discharge priority to emergency departments were considered between January 1, 2020, and June 30, 2023. Prehospital point-of-care testing and on-scene vital signs were used for the unsupervised machine learning method (clustering) to determine the phenotypes. Then phenotypes were compared with the primary outcome (cumulative mortality (all-cause) at 2, 7, and 30 days). A total of 7909 patients were included. The median (IQR) age was 64 (51–80) years, 41% were women, and 26% were living in rural areas. Three clusters were identified: alpha 16.2% (1281 patients), beta 28.8% (2279), and gamma 55% (4349). The mortality rates for alpha, beta and gamma at 2 days were 18.6%, 4.1%, and 0.8%, respectively; at 7 days, were 24.7%, 6.2%, and 1.7%; and at 30 days, were 33%, 10.2%, and 3.2%, respectively. Based on standard vital signs and blood test biomarkers in the prehospital scenario, three clusters were identified: alpha (high-risk), beta and gamma (medium- and low-risk, respectively). This permits the EMS system to quickly identify patients who are potentially compromised and to proactively implement the necessary interventions.

紧急医疗服务 (EMS) 面临危急情况，需要根据分析和生命体征对患者进行风险分类。我们的目的是根据院前分析和生命体征建立聚类衍生的表型，从而进行风险分层。这是一项前瞻性、多中心、EMS 交付、基于救护车的队列研究，考虑了西班牙的 6 个高级生命支持单位、38 个基本生命支持单位和 4 家三级医院。 2020 年 1 月 1 日至 2023 年 6 月 30 日期间，考虑由 EMS 管理、患有未选择的急性疾病并优先送往急诊科的成人。无人监管的机器使用院前护理点检测和现场生命体征学习方法（聚类）来确定表型。然后将表型与主要结局（2、7 和 30 天的累积死亡率（全因））进行比较。总共纳入了 7909 名患者。中位年龄 (IQR) 为 64 (51-80) 岁，41% 为女性，26% 生活在农村地区。确定了三个簇： α 16.2%（1281 名患者）、 β 28.8%（2279 名患者）和γ 55%（4349 名患者）。 α 、 β和γ的2天死亡率分别为18.6%、4.1%和0.8%； 7 天时，分别为 24.7%、6.2% 和 1.7%； 30 天时，分别为 33%、10.2% 和 3.2%。根据院前场景中的标准生命体征和血液测试生物标志物，确定了三个集群： α （高风险）、 β和γ （分别为中风险和低风险）。这使得 EMS 系统能够快速识别可能受到损害的患者，并主动实施必要的干预措施。