机构地区:[1]首都医科大学附属北京同仁医院放射科,北京100730 [2]首都医科大学附属北京同仁医院护理部,北京100730 [3]首都医科大学附属北京同仁医院急诊科,北京100730
出 处:《中国急救复苏与灾害医学杂志》2023年第11期1493-1496,共4页China Journal of Emergency Resuscitation and Disaster Medicine
基 金:2019年首都医科大学附属北京同仁医院院内种子基金资助项目(编号:2019-YJJ-ZZL-047)。
摘 要:目的了解患者院内死亡的现况与影响因素,为优化危重患者的护理抢救策略提供依据。方法采用便利抽样的方法,收集2020年1—12月首都医科大学附属北京同仁医院崇文门院区急诊抢救室收治的患者,将单因素分析中有统计学意义的变量进一步行多元Logistic回归分析,进一步绘制ROC曲线并计算曲线下面积判断不同变量对患者院内死亡的预测情况。结果本研究共纳入患者1359例,其中生存组共有1192人(87.7%),死亡组167人(12.3%),抢救室停留时间、入院24 h内是否抢救和入院时NEWS评分为影响抢救室患者院内死亡的独立影响因素。Logistic回归模型的Hosmer and Lemeshow检验卡方值=14.744,P=0.064,模型拟合优度较高,Logistic回归模型能够将89.1%的观测正确分类,灵敏度30.5%,特异度97.3%,阳性预测值61.4%,阴性预测值92.2%。所得Logistic回归方程为:Logit(p)=-5.565+0.003×抢救室停留时间+2.180×入院24 h内是否抢救+0.346×入院时NEWS评分。结论抢救室停留时间≥28 h、入院时NEWS评分≥5分、入院后24 h内抢救的患者其院内死亡风险更大,由这三者组成的模型其预测效能更佳,可指导临床护理人员对危重患者院内死亡结局进行快速评估。Objective To understand the current situation and influencing factors of hospital death of patients,and to provide basis for optimizing the nursing rescue strategy of critical patients.Methods The convenient sampling method was used to collect the patients admitted to the emergency rescue room of Chongwenmen Hospital of Beijing Tongren Hospital affiliated to Capital Medical University from January to December 2020.The variables with statistical significance in the univariate analysis were further subjected to multiple logistic regression analysis.The ROC curve was further drawn and the area under the curve(AUC)was calculated to judge the prediction of different variables on the hospital death of patients.Results A total of 1359 patients were included in this study,including 1192(87.7%)in the survival group and 167(12.3%)in the death group.The duration of stay in the rescue room,whether to rescue within 24 hours of admission,and the NEWS score at admission were independent factors affecting the hospital death of patients in the rescue room.The Chi square value of the Hosmer and Lemeshow test of the logistic regression model=14.744,P=0.064,and the goodness of fit of the model was high.The logistic regression model can correctly classify 89.1%of the observations,with sensitivity of 30.5%,specificity of 97.3%,positive predictive value of 61.4%,and negative predictive value of 92.2%.The Logistic regression equation obtained was:Logit(p)=-5.565+0.003×Rescue room stay time+2.180×Whether to rescue within 24 hours after admission+0.346×NEWS score at admission.Conclusion The patients who were rescued within 24 hours after admission had a higher risk of in-hospital death if their stay in the emergency room was≥28 hours and their NEWS score was≥5 points at admission.The model composed of these three factors had a better predictive effect,which could guide clinical nurses to quickly evaluate the in-hospital death outcome of critically ill patients.
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