PDF(Pubmed)
Abstract：
OBJECTIVE: The development of predictive models for patients treated by emergency medical services (EMS) is on the rise in the emergency field. However, how these models evolve over time has not been studied. The objective of the present work is to compare the characteristics of patients who present mortality in the short, medium and long term, and to derive and validate a predictive model for each mortality time.
METHODS: A prospective multicenter study was conducted, which included adult patients with unselected acute illness who were treated by EMS. The primary outcome was noncumulative mortality from all causes by time windows including 30-day mortality, 31- to 180-day mortality, and 181- to 365-day mortality. Prehospital predictors included demographic variables, standard vital signs, prehospital laboratory tests, and comorbidities.
RESULTS: A total of 4830 patients were enrolled. The noncumulative mortalities at 30, 180, and 365 days were 10.8%, 6.6%, and 3.5%, respectively. The best predictive value was shown for 30-day mortality (AUC = 0.930; 95% CI: 0.919-0.940), followed by 180-day (AUC = 0.852; 95% CI: 0.832-0.871) and 365-day (AUC = 0.806; 95% CI: 0.778-0.833) mortality.
CONCLUSIONS: Rapid characterization of patients at risk of short-, medium-, or long-term mortality could help EMS to improve the treatment of patients suffering from acute illnesses.
METHODS: A prospective multicenter study was conducted, which included adult patients with unselected acute illness who were treated by EMS. The primary outcome was noncumulative mortality from all causes by time windows including 30-day mortality, 31- to 180-day mortality, and 181- to 365-day mortality. Prehospital predictors included demographic variables, standard vital signs, prehospital laboratory tests, and comorbidities.
RESULTS: A total of 4830 patients were enrolled. The noncumulative mortalities at 30, 180, and 365 days were 10.8%, 6.6%, and 3.5%, respectively. The best predictive value was shown for 30-day mortality (AUC = 0.930; 95% CI: 0.919-0.940), followed by 180-day (AUC = 0.852; 95% CI: 0.832-0.871) and 365-day (AUC = 0.806; 95% CI: 0.778-0.833) mortality.
CONCLUSIONS: Rapid characterization of patients at risk of short-, medium-, or long-term mortality could help EMS to improve the treatment of patients suffering from acute illnesses.
摘要：
目的：在急诊领域，针对急诊医疗服务（EMS）治疗的患者的预测模型的开发正在兴起。然而,这些模型是如何随时间演变的，还没有被研究过。本工作的目的是比较短期内死亡率的患者的特征，中长期,并推导和验证每个死亡时间的预测模型。
方法：进行了一项前瞻性多中心研究，其中包括接受EMS治疗的未经选择的急性疾病的成年患者。主要结局是所有原因的非累积死亡率，包括30天死亡率，31天至180天死亡率，和181至365天的死亡率。院前预测因素包括人口统计学变量，标准生命体征,院前实验室检查，和合并症。
结果：共纳入4830例患者。30、180和365天时的非累积死亡率为10.8%，6.6%,和3.5%，分别。30天死亡率显示最佳预测值（AUC=0.930；95％CI：0.919-0.940），其次是180天(AUC=0.852;95%CI:0.832-0.871)和365天(AUC=0.806;95%CI:0.778-0.833)死亡率。
结论：快速表征处于短期，medium-,或长期死亡率可以帮助EMS改善患有急性疾病的患者的治疗。
方法：进行了一项前瞻性多中心研究，其中包括接受EMS治疗的未经选择的急性疾病的成年患者。主要结局是所有原因的非累积死亡率，包括30天死亡率，31天至180天死亡率，和181至365天的死亡率。院前预测因素包括人口统计学变量，标准生命体征,院前实验室检查，和合并症。
结果：共纳入4830例患者。30、180和365天时的非累积死亡率为10.8%，6.6%,和3.5%，分别。30天死亡率显示最佳预测值（AUC=0.930；95％CI：0.919-0.940），其次是180天(AUC=0.852;95%CI:0.832-0.871)和365天(AUC=0.806;95%CI:0.778-0.833)死亡率。
结论：快速表征处于短期，medium-,或长期死亡率可以帮助EMS改善患有急性疾病的患者的治疗。
