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院外急救中危重症的预测。

Prediction of critical illness during out-of-hospital emergency care.

机构信息

Division of Pulmonary and Critical Care Medicine, University of Washington, Harborview Medical Center, PO Box 359762, Seattle, WA 98104, USA.

出版信息

JAMA. 2010 Aug 18;304(7):747-54. doi: 10.1001/jama.2010.1140.

Abstract

CONTEXT

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.

OBJECTIVES

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.

DESIGN AND SETTING

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.

PATIENTS

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.

MAIN OUTCOME MEASURE

Development of critical illness, defined as severe sepsis, delivery of mechanical ventilation, or death during hospitalization.

RESULTS

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).

CONCLUSIONS

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.

摘要

背景

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。

目的

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。

设计和地点

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。

患者

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。

主要观察指标

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。

结果

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。

结论

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。

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