心血管外科术后患者ICU后综合征风险预测模型的构建及验证

doi:10.3761/j.issn.0254-1769.2022.12.012

摘要/Abstract

摘要：

目的探讨心血管外科术后患者发生ICU后综合征（post-intensive care syndrome,PICS）的危险因素,建立风险预测模型,并进行内部验证检验预测效果。方法采用便利抽样法,选取2019年7月—10月在长沙市2所三级甲等医院心血管外科术后转入ICU的304例患者进行调查,并于患者转出ICU后3个月时进行随访,根据随访时患者是否发生PICS分为发生PICS组（n=103）和未发生PICS组（n=201）,筛选PICS的危险因素并根据Logistic回归结果建立预测模型。采用受试者操作特征曲线（receiver operating characteristic curve,ROC）下面积检验模型的预测效果,采用Bootstrap自助法重抽样1 000次对模型进行内部验证。结果最终构建的模型为：Logit P=0.722×性别+0.903×年龄+0.968×心理弹性得分+1.073×术前左心室射血分数+0.706×手术时长+0.797×入住ICU时长-3.212。预测模型的ROC下面积为0.817,灵敏度为59.2%,特异度为85.6%。Bootstrap自助法进行内部验证的结果显示,C指数为0.804,提示模型预测效果良好。结论该研究构建的心血管外科术后患者PICS风险预测模型预测效能良好,有助于预测PICS的发生,为今后相关干预措施的制订与实施提供了参考依据。

关键词: 心血管外科手术, ICU后综合征, 影响因素, Logistic模型, 重症护理

Abstract:

Objective To investigate the risk factors of post-intensive care syndrome（PICS） in patients after cardiovascular surgery,and to establish a risk prediction model and conduct internal validation to verify the prediction effect. Methods A total of 304 patients after cardiovascular surgery in 2 tertiary A hospitals in Changsha were selected by convenience sampling method from July to October 2019. The patients were followed up 3 months after they were transferred out of ICU. According to the occurrence of PICS during follow-up,the patients were divided into a PICS group （n=103） and a non-PICS group （n=201）. Risk factors of PICS were screened and a predictive model was established based on Logistic regression results. The area under receiver operating characteristic（ROC） curve was used to test the prediction effect of the model,and Bootstrap method was used to re-sample 1 000 times for internal verification of the model. Results The final model was Logit P=0.722×gender+0.903×age+0.968×score of CD-RISC+1.073×pre-operative left ventricular ejection fraction+0.706×length of operation+0.797×length of ICU stay-3.212. The area under ROC curve of the prediction model was 0.817;the sensitivity was 59.2%;the specificity was 85.6%. The results of internal verification by Bootstrap method showed that the C index was 0.804,indicating good prediction effect. Conclusion The risk prediction model for patients with PICS after cardiovascular surgery constructed in this study has good predictive efficacy,which helps to predict the risk of PICS and provides a reference for the implementation of relevant intervention measures in the future.

Key words: Cardiovascular Surgical Procedures, Post-Intensive Care Syndrome, Root Case Analysis, Logistic Models, Critical Care

孟萌, 关玉珠, 郭利敏, 唐莉, 李乐之. 心血管外科术后患者ICU后综合征风险预测模型的构建及验证[J]. 中华护理杂志, 2022, 57(12): 1486-1493.

MENG Meng, GUAN Yuzhu, GUO Limin, TANG Li, LI Lezhi. Construction of a risk prediction model for patients with post-intensive care syndrome after cardiovascular surgery and its prediction effect[J]. Chinese Journal of Nursing, 2022, 57(12): 1486-1493.

图/表 4

表1 有统计学意义变量的最佳截断值

Table 1 Cutoff values of statistically significant variables

表2 心血管外科术后患者ICU后综合征的单因素分析[例（百分比,%）]

Table 2 Univariate analysis of post-intensive care syndrome after cardiovascular surgery

表3 心血管外科术后患者发生ICU后综合征的Logistic回归分析结果

Table 3 Logistic regression analysis of patients with post-intensive care syndrome after cardiovascular surgery

图1 心血管外科术后患者ICU后综合征风险因素预测模型的ROC曲线

Figure 1 ROC curve of the prediction model of risk factors for post-ICU syndrome in patients after cardiovascular surgery

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