Crowe Remle P, Bourn Scott S, Fernandez Antonio R, Myers J Brent
ESO, Inc, Austin, Texas (RPC, SB, ARF, JBM); Department of Emergency Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina (ARF).
Prehosp Emerg Care. 2022 Jan-Feb;26(1):55-65. doi: 10.1080/10903127.2020.1862944. Epub 2021 Jan 25.
A standardized objective measure of prehospital patient risk of hospitalization or death is needed. The Rapid Emergency Medicine Score (REMS), a validated risk-stratification tool, has not been widely tested for prehospital use. This study's objective was to assess predictive characteristics of initial prehospital REMS for ED disposition and overall patient mortality. This retrospective analysis used linked prehospital and hospital data from the national ESO Data Collaborative. All 911 responses from 1/1/2019-12/31/2019 were included. REMS (0-26) was calculated using age and first prehospital values for: pulse rate, mean arterial pressure, respiratory rate, oxygen saturation, and Glasgow Coma Scale. Non-transports, patients <18 and cardiac arrests prior to EMS arrival were excluded. The primary outcome was ED disposition, dichotomized to discharge versus admission, transfer, or death. The secondary outcome was overall survival to discharge (ED or inpatient). Transfers and records without inpatient disposition were excluded from the secondary analysis. Predictive ability was assessed using area under the receiver operating curve (AUROC). Optimal REMS cut points were determined using test characteristic curves. Univariable logistic regression modeling was used to quantify the association between initial prehospital REMS and each outcome. Of 579,505 eligible records, 94,640 (16%) were excluded due to missing data needed to calculate REMS. Overall, 62% ( = 298,223) of patients were discharged from the ED, 36% ( = 175,212) were admitted, 2% ( = 10,499) were transferred, and 0.2% ( = 931) died in the ED. A REMS of 5 or lower demonstrated optimal statistical prediction for ED discharge versus not discharged (admission/transfer/death) (AUROC: 0.68). Patients with initial prehospital REMS of 5 or lower showed a three-fold increase in odds of ED discharge (OR: 3.28, 95%CI: 3.24-3.32). Of the 457,226 patients included in overall mortality analysis, >98% ( = 450,112) survived. AUROC of initial prehospital REMS for overall mortality was 0.79. A score 7 or lower was statistically optimal for predicting survival. Initial prehospital REMS of 7 or lower was associated with a five-fold increase in odds of overall survival (OR:5.41, 95%CI:5.15-5.69). Initial prehospital REMS was predictive of ED disposition and overall patient mortality, suggesting value as a risk-stratification measure for EMS agencies, systems and researchers.
需要一种标准化的客观方法来衡量院前患者住院或死亡的风险。快速急诊医学评分（REMS）是一种经过验证的风险分层工具，但尚未在院前使用中得到广泛测试。本研究的目的是评估院前初始REMS对急诊科处置和患者总体死亡率的预测特征。这项回顾性分析使用了来自国家ESO数据协作组织的院前和医院关联数据。纳入了2019年1月1日至2019年12月31日期间所有的911响应数据。REMS（0 - 26）通过年龄以及院前首次测量的以下指标计算得出：脉搏率、平均动脉压、呼吸频率、血氧饱和度和格拉斯哥昏迷量表。非转运患者、年龄小于18岁的患者以及急救医疗服务（EMS）到达之前发生心脏骤停的患者被排除。主要结局是急诊科处置情况，分为出院与入院、转院或死亡。次要结局是出院时的总体生存率（急诊科或住院患者）。转院患者以及没有住院处置记录的患者被排除在次要分析之外。使用受试者操作特征曲线下面积（AUROC）评估预测能力。使用检验特征曲线确定最佳REMS切点。采用单变量逻辑回归模型量化院前初始REMS与每个结局之间的关联。在579,505条符合条件的记录中，有94,640条（16%）因缺少计算REMS所需的数据而被排除。总体而言，62%（n = 298,223）的患者从急诊科出院，36%（n = 175,212）的患者入院，2%（n = 10,499）的患者转院，0.2%（n = 931）的患者在急诊科死亡。REMS为5或更低对急诊科出院与未出院（入院/转院/死亡）具有最佳的统计预测能力（AUROC：0.68）。院前初始REMS为5或更低的患者急诊科出院几率增加了两倍（OR：3.28，95%CI：3.24 - 3.32）。在纳入总体死亡率分析的457,226名患者中，超过98%（n = 450,112）存活。院前初始REMS对总体死亡率的AUROC为0.79。分数为7或更低对预测生存具有统计学上的最佳效果。院前初始REMS为7或更低与总体生存几率增加五倍相关（OR：5.41，95%CI：5.15 - 5.69）。院前初始REMS可预测急诊科处置情况和患者总体死亡率，表明其作为EMS机构、系统和研究人员的风险分层措施具有价值。
