Emergency Medical Services Division of Public Health for Seattle and King County, Seattle, WA 98104, USA.
Resuscitation. 2012 Jan;83(1):134-7. doi: 10.1016/j.resuscitation.2011.09.022. Epub 2011 Oct 6.
Optimal care for out-of hospital cardiac arrest (OHCA) patients may depend on the underlying aetiology of OHCA. Specifically chest compression only bystander CPR may provide greater benefit among those with cardiac aetiology and chest compressions plus rescue breathing may provide greater benefit among those with non-cardiac aetiology. The aim of this study was to generate a simple predictor model to identify OHCA patients with non-cardiac aetiology in order to accurately allocate rescue breathing.
We used two independent cohorts of OHCA patients from a randomized pre-hospital trial and a prospective hospital registry (total n=3086) to assess whether the characteristics of age, gender and arrest location (private versus public) could sufficiently discriminate non-cardiac aetiology. We used logistic regression models to generate a receiver operator curve and likelihood ratios.
Overall, 965/3086 (31%) had a final diagnosis of a non-cardiac cause. Using 8 exclusive groups according to age, gender, and location, the frequency of non-cardiac aetiology varied from a low of 16% (55/351) among men >age 50 in a public location up to 58% (199/346) among women <60 in a private location. Although each characteristic was predictive in the logistic regression model, the area under the curve in the receiver operating curve was only 0.66. The associated positive likelihood ratios ranged from 1 to 3 and the negative likelihood ratios ranged from 1 to 0.4.
The results highlight the challenge of accurately identifying non-cardiac aetiology by characteristics that could be consistently used to allocate bystander rescue breathing.
院外心脏骤停(OHCA)患者的最佳治疗可能取决于 OHCA 的潜在病因。具体来说,对于心源性病因的患者,单纯胸外按压的旁观者心肺复苏可能会带来更大的益处,而对于非心源性病因的患者,胸外按压加人工呼吸可能会带来更大的益处。本研究旨在生成一个简单的预测模型,以识别非心源性病因的 OHCA 患者,从而准确分配人工呼吸。
我们使用一项随机院前试验和一项前瞻性医院登记处的两个独立 OHCA 患者队列(共 3086 例)来评估年龄、性别和发病地点(私人场所与公共场)是否足以区分非心源性病因。我们使用逻辑回归模型生成受试者工作特征曲线和似然比。
总体而言,3086 例患者中有 965 例(31%)最终诊断为非心源性病因。根据年龄、性别和地点将患者分为 8 个排他性组别,非心源性病因的发生率从男性>50 岁且在公共场所有 16%(55/351)到女性<60 岁且在私人场所有 58%(199/346)不等。虽然每个特征在逻辑回归模型中都具有预测性,但受试者工作特征曲线下的面积仅为 0.66。阳性似然比范围为 1 至 3,阴性似然比范围为 1 至 0.4。
这些结果突出了通过特征准确识别非心源性病因的挑战,这些特征可以被一致用于分配旁观者人工呼吸。