作者：陈慧慧,陆真

单位：

210029 江苏省南京市，江苏省人民医院心脏大血管外科 ICU 通信作者：陆真，E-mail：luzhen206@163.com

单位（英文）：

CVSICU, Jiangsu Provincial People's Hospital, Nanjing 210029, China Corresponding author: LU Zhen, E-mail: luzhen206@163.com

关键词：

心脏瓣膜置换术; 压力性损伤; 影响因素分析; 列线图;

关键词（英文）：

Heart valve replacement; Pressure injury; Root cause analysis; Nomogram

中图分类号：

DOI：

10.12114/j.issn.1008-5971.2021.00.274

基金项目：

江苏省人民医院 2017 年度护理科研项目（YHK201745）

摘要：

背景目前,心脏瓣膜置换术后压力性损伤的危险因素尚未完全明确,个体化预测压力性损伤高风险人群的方法仍需进一步探索。目的 构建个体化预测心脏瓣膜置换术后压力性损伤发生风险的列线图模型。方法选取2018年5月至2020年4月在江苏省人民医院行心脏瓣膜置换术的患者350作为研究对象,随机将其分为训练集(n=175)和验证集(n=175)。收集患者的临床资料,心脏瓣膜置换术后压力性损伤的影响因素分析采用多因素Logistic回归分析,采用R(R 3.5.3)软件包和rms程序包构建列线图模型,并通过训练集和验证集进行内外部验证,采用一致性指数(CI)、校准曲线、ROC曲线和决策曲线评估列线图模型的预测效能。结果 350例患者中87例发生压力性损伤,压力性损伤发生率为24.86%。根据是否发生压力性损伤将训练集患者分为压力性损伤组(n=46)和非压力性损伤组(n=129)。压力性损伤组和非压力性损伤组患者糖尿病发生率、术前血清白蛋白(Alb)、体外循环时间、手术时间、术中输血量及术中使用血管活性药物者所占比例比较,差异有统计学意义(P <0.05)。多因素Logistic回归分析结果显示,糖尿病、术前血清Alb、体外循环时间、手术时间、术中输血量及术中使用血管活性药物是心脏瓣膜置换术后患者发生压力性损伤的独立影响因素(P <0.05)。基于上述影响因素构建心脏瓣膜置换术后压力性损伤发生风险的列线图模型。模型验证结果显示,训练集和验证集的CI分别为0.827和0.745,列线图模型预测训练集和验证集患者心脏瓣膜置换术后压力性损伤发生风险的校正曲线均趋近于理想曲线,表明该列线图模型的预测准确率较高;ROC曲线分析结果显示,列线图模型预测训练集和验证集患者心脏瓣膜置换术后压力性损伤发生风险的ROC曲线下面积分别为0.840〔95%CI(0.802,0.876)〕、0.751〔95%CI(0.718,0.785)〕,表明该列线图模型的区分度良好;决策曲线分析结果显示,在8%～95%范围内,该列线图模型预测的净获益值较高,表明该列线图模型的临床预测效能良好。结论 基于糖尿病、术前血清Alb、体外循环时间、手术时间、术中输血量和术中使用血管活性药物构建的心脏瓣膜置换术患者压力性损伤发生风险的列线图模型,能够有效预测心脏瓣膜置换术后压力性损伤发生风险,有利于临床筛查心脏瓣膜置换术后压力性损伤高风险患者。

英文摘要：

【Abstract】　Background At present, the risk factors of pressure injury after heart valve replacement have not been fullydefined, and the method of individualized prediction of high-risk population of pressure injury still needs to be further explored.Objective To construct the nomogram model for individualized prediction of pressure injury risk after cardiac valve replacement.Methods A total of 350 patients who underwent heart valve replacement in Jiangsu Provincial People's Hospital from May 2018to April 2020 were selected and randomly divided into training set (n=175) and verification set (n=175) . The clinical data ofpatients were collected, and the influencing factors of pressure injury after heart valve replacement were analyzed by multivariateLogistic regression analysis, R (R 3.5.3) software package and rms program package were used to construct the nomogram model,and internal and external verification was carried out through training set and verification set. C-index (CI) , calibration curve,ROC curve and decision curve were used to evaluate the prediction efficiency of nomogram model.ResultsOf the 350 patients,87 patients had pressure injury, and the incidence of pressure injury was 24.86%. According to the occurrence of pressureinjury, the patients in the training set were divided into pressure injury group (n=46) and non pressure injury group (n=129) .There were significant differences in the incidence of diabetes, preoperative serum albumin (Alb) , cardiopulmonary bypass time,operative time, amount of blood infusion during operation and proportion of patients using vasoactive drugs during operationbetween the pressure injury group and the non pressure injury group (P < 0.05) . Multivariate Logistic regression analysis showedthat diabetes, preoperative serum Alb, cardiopulmonary bypass time, operative time, amount of blood infusion during operationandusing vasoactive drugs during operation were independent factors of pressure injury after cardiac valve replacement (P < 0.05) .Based on the above independent factors, a nomogram model for predicting the risk of independent factors after heart valvereplacement was constructed. The model validation results show that the CI of the training set and the validation set were 0.827and 0.745, respectively; the calibration curve of the nomogram model for predicting the risk of pressure injury after cardiacvalve replacement in training set and verification set were both close to the ideal curve, indicating that the nomogram model hadgood prediction accuracy; the ROC curve analysis results showed that the area under the ROC curve of the nomogram model forpredicting the risk of pressure injury after cardiac valve replacement in training set and verification set were 0.840 [95%CI (0.802,0.876) ] and 0.751 [95%CI (0.718, 0.785) ] , respectively, indicating that the nomogram model had good discrimination; thedecision curve analysis results showed that, in the range of 8%-95%, the net benefit predicted by the nomogram model was high,indicating that the nomogram model had good clinical prediction efficiency. Conclusion The nomogram model for predicting therisk of pressure injury after cardiac valve replacement based on diabetes, preoperative serum Alb, cardiopulmonary bypass time,operative time, amount of blood infusion during operation and using vasoactive drugs during operation can effectively predict therisk of pressure injury after heart valve replacement, which is conducive to clinical screening of patients with high risk of pressureinjury after heart valve replacement.

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