Division of Pulmonary and Critical Care Medicine, University of Washington, Harborview Medical Center, PO Box 359762, Seattle, WA 98104, USA.
JAMA. 2010 Aug 18;304(7):747-54. doi: 10.1001/jama.2010.1140.
Early identification of nontrauma patients in need of critical care services in the emergency setting may improve triage decisions and facilitate regionalization of critical care.
To determine the out-of-hospital clinical predictors of critical illness and to characterize the performance of a simple score for out-of-hospital prediction of development of critical illness during hospitalization.
Population-based cohort study of an emergency medical services (EMS) system in greater King County, Washington (excluding metropolitan Seattle), that transports to 16 receiving facilities.
Nontrauma, non-cardiac arrest adult patients transported to a hospital by King County EMS from 2002 through 2006. Eligible records with complete data (N = 144,913) were linked to hospital discharge data and randomly split into development (n = 87,266 [60%]) and validation (n = 57,647 [40%]) cohorts.
Development of critical illness, defined as severe sepsis, delivery of mechanical ventilation, or death during hospitalization.
Critical illness occurred during hospitalization in 5% of the development (n = 4835) and validation (n = 3121) cohorts. Multivariable predictors of critical illness included older age, lower systolic blood pressure, abnormal respiratory rate, lower Glasgow Coma Scale score, lower pulse oximetry, and nursing home residence during out-of-hospital care (P < .01 for all). When applying a summary critical illness prediction score to the validation cohort (range, 0-8), the area under the receiver operating characteristic curve was 0.77 (95% confidence interval [CI], 0.76-0.78), with satisfactory calibration slope (1.0). Using a score threshold of 4 or higher, sensitivity was 0.22 (95% CI, 0.20-0.23), specificity was 0.98 (95% CI, 0.98-0.98), positive likelihood ratio was 9.8 (95% CI, 8.9-10.6), and negative likelihood ratio was 0.80 (95% CI, 0.79- 0.82). A threshold of 1 or greater for critical illness improved sensitivity (0.98; 95% CI, 0.97-0.98) but reduced specificity (0.17; 95% CI, 0.17-0.17).
In a population-based cohort, the score on a prediction rule using out-of-hospital factors was significantly associated with the development of critical illness during hospitalization. This score requires external validation in an independent population.
在急诊环境中早期识别需要重症监护服务的非创伤患者,可能会改善分诊决策并促进重症监护的区域化。
确定院外临床危重症的预测因素,并描述一种简单的评分方法,用于预测住院期间发展为危重症的院外表现。
华盛顿州大国王县(不包括西雅图都会区)的一个基于人群的急救医疗服务(EMS)系统的队列研究,该系统将患者转运至 16 个接收机构。
2002 年至 2006 年期间,由国王县 EMS 转运至医院的非创伤性、非心搏骤停的成年患者。合格记录中有完整数据(N=144913),与医院出院数据相关联,并随机分为发展(n=87266[60%])和验证(n=57647[40%])队列。
发展为危重症,定义为严重脓毒症、机械通气或住院期间死亡。
发展队列(n=4835)和验证队列(n=3121)中,分别有 5%的患者在住院期间发生危重症。危重症的多变量预测因素包括年龄较大、收缩压较低、呼吸频率异常、格拉斯哥昏迷评分较低、脉搏血氧饱和度较低,以及院外护理期间住在疗养院(所有 P<.01)。当将一个总结性危重症预测评分应用于验证队列(范围为 0-8)时,接收者操作特征曲线下面积为 0.77(95%置信区间[CI],0.76-0.78),校准斜率令人满意(1.0)。使用评分阈值为 4 或更高时,灵敏度为 0.22(95%CI,0.20-0.23),特异性为 0.98(95%CI,0.98-0.98),阳性似然比为 9.8(95%CI,8.9-10.6),阴性似然比为 0.80(95%CI,0.79-0.82)。临界值为 1 或更高时,灵敏度(0.98;95%CI,0.97-0.98)提高,但特异性(0.17;95%CI,0.17-0.17)降低。
在一个基于人群的队列中,使用院外因素的预测规则评分与住院期间发生危重症显著相关。该评分需要在独立人群中进行外部验证。