预测接受心脏手术的老年患者术后谵妄风险的实用预后工具:视觉和动态提名图。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Chernor Sulaiman Bah, Bongani Mbambara, Xianhai Xie, Junlin Li, Asha Khatib Iddi, Chen Chen, Hui Jiang, Yue Feng, Yi Zhong, Xinlong Zhang, Huaming Xia, Libo Yan, Yanna Si, Juan Zhang, Jianjun Zou
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引用次数: 0

摘要

目的:在接受心脏手术的老年患者中,术后谵妄(POD)的发生率高达 65%。我们旨在开发两种动态提名图来预测接受心脏手术的老年患者发生 POD 的风险:这是一项单中心回顾性队列研究,纳入了 2021 年 7 月至 2022 年 6 月期间在中国南京市第一医院接受心脏手术的 531 名老年患者。研究采用单变量和多变量逻辑回归来确定构建模型时使用的重要预测因子。我们使用接收器操作特征(ROC)、10倍交叉验证和决策曲线分析(DCA)对模型的性能和准确性进行了评估、验证,并估算了临床效用和净收益:结果:共有 30% 的患者出现了 POD,术前模型中的重要预测因子是 ASA(P 结论:术前模型中的重要预测因子是 ASA(P 结论:术前模型中的重要预测因子是 ASA(P 结论):我们根据术前和术后时间点建立了两个提名图模型,用于预测 POD 风险,并指导在不同时间点灵活实施可能的干预措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Practical prognostic tools to predict the risk of postoperative delirium in older patients undergoing cardiac surgery: visual and dynamic nomograms.

Purpose: Postoperative Delirium (POD) has an incidence of up to 65% in older patients undergoing cardiac surgery. We aimed to develop two dynamic nomograms to predict the risk of POD in older patients undergoing cardiac surgery.

Methods: This was a single-center retrospective cohort study, which included 531 older patients who underwent cardiac surgery from July 2021 to June 2022 at Nanjing First Hospital, China. Univariable and multivariable logistic regression were used to identify the significant predictors used when constructing the models. We evaluated the performances and accuracy, validated, and estimated the clinical utility and net benefit of the models using the receiver operating characteristic (ROC), the 10-fold cross-validation, and decision curve analysis (DCA).

Results: A total of 30% of the patients developed POD, the significant predictors in the preoperative model were ASA ( p < 0.001 OR = 3.220), cerebrovascular disease (p < 0.001 OR = 2.326), Alb (p < 0.037 OR = 0.946), and URE (p < 0.001 OR = 1.137), while for the postoperative model they were ASA (p = 0.044, OR = 1.737), preoperative MMSE score (p = 0.005, OR = 0.782), URE (p = 0.017 OR = 1.092), CPB duration (p < 0.001 OR = 1.010) and APACHE II (p < 0.001, OR = 1.353). The preoperative and postoperative models achieved satisfactory predictive performances, with AUC values of 0.731 and 0.799, respectively. The web calculators can be accessed at https://xxh152.shinyapps.io/Pre-POD/ and https://xxh152.shinyapps.io/Post-POD/ .

Conclusion: We established two nomogram models based on the preoperative and postoperative time points to predict POD risk and guide the flexible implementation of possible interventions at different time points.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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