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一种用于预测接受瓣膜手术的疑似心力衰竭患者术后死亡率的新风险评分。

A New Risk Score for Predicting Postoperative Mortality in Suspected Heart Failure Patients Undergoing Valvular Surgery.

作者信息

Lin Hongyuan, Gong Jiamiao, An Kang, Wu Yongjian, Zheng Zhe, Hou Jianfeng

机构信息

Cardiac Surgery Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China.

出版信息

Rev Cardiovasc Med. 2023 Feb 2;24(2):38. doi: 10.31083/j.rcm2402038. eCollection 2023 Feb.

DOI:10.31083/j.rcm2402038
PMID:39077403
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11273104/
Abstract

BACKGROUND

Heart failure (HF) is one of the most important indications of the severity of valvular heart disease (VHD). VHD with HF is frequently associated with a higher surgical risk. Our study sought to develop a risk score model to predict the postoperative mortality of suspected HF patients after valvular surgery.

METHODS

Between January 2016 and December 2018, all consecutive adult patients suspected of HF and undergoing valvular surgery in the Chinese Cardiac Surgery Registry (CCSR) database were included. Finally, 14,645 patients (55.39 11.6 years, 43.5% female) were identified for analysis. As a training group for model derivation, we used patients who had surgery between January 2016 and May 2018 (11,292 in total). To validate the model, patients who underwent surgery between June 2018 and December 2018 (a total of 3353 patients) were included as a testing group. In training group, we constructed and validated a scoring system to predict postoperative mortality using multivariable logistic regression and bootstrapping method (1000 re-samples). We validated the scoring model in the testing group. Brier score and calibration curves using bootstrapping with 1000 re-samples were used to evaluate the calibration. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the discrimination. The results were also compared to EuroSCORE II.

RESULTS

The final score ranged from 0 to 19 points and involved 9 predictors: age 60 years; New York Heart Association Class (NYHA) IV; left ventricular ejection fraction (LVEF) 35%; estimated glomerular filtration rate (eGFR) 50 mL/min/1.73 ; preoperative dialysis; Left main artery stenosis; non-elective surgery; cardiopulmonary bypass (CPB) time 200 minutes and perioperative transfusion. In training group, observed and predicted postoperative mortality rates increased from 0% to 45.5% and from 0.8% to 50.3%, respectively, as the score increased from 0 up to 10 points. The scoring model's Brier scores in the training and testing groups were 0.0279 and 0.0318, respectively. The area under the curve (AUC) values of the scoring model in both the training and testing groups were 0.776, which was significantly higher than EuroSCORE II in both the training (AUC = 0.721, Delong test, 0.001) and testing (AUC = 0.669, Delong test, 0.001) groups.

CONCLUSIONS

The new risk score is an effective and concise tool that could accurately predict postoperative mortality rates in suspected HF patients after valve surgery.

摘要

背景

心力衰竭(HF)是心脏瓣膜病(VHD)严重程度的最重要指标之一。伴有HF的VHD通常与更高的手术风险相关。我们的研究旨在开发一种风险评分模型,以预测瓣膜手术后疑似HF患者的术后死亡率。

方法

纳入2016年1月至2018年12月期间在中国心脏手术注册数据库(CCSR)中所有连续接受瓣膜手术的疑似HF成年患者。最终,确定14645例患者(年龄55.39±11.6岁,43.5%为女性)进行分析。作为模型推导的训练组,我们使用了2016年1月至2018年5月期间接受手术的患者(共11292例)。为验证该模型,将2018年6月至2018年12月期间接受手术的患者(共3353例)纳入作为测试组。在训练组中,我们使用多变量逻辑回归和自抽样法(1000次重新抽样)构建并验证了一个预测术后死亡率的评分系统。我们在测试组中验证了评分模型。使用1000次重新抽样的自抽样法的Brier评分和校准曲线来评估校准情况。使用受试者操作特征曲线下面积(AUROC)来评估辨别力。结果还与欧洲心脏手术风险评估系统II(EuroSCORE II)进行了比较。

结果

最终评分范围为0至19分,涉及9个预测因素:年龄≥60岁;纽约心脏协会心功能分级(NYHA)IV级;左心室射血分数(LVEF)≤35%;估计肾小球滤过率(eGFR)≤50 mL/min/1.73 ;术前透析;左主干动脉狭窄;非择期手术;体外循环(CPB)时间≥200分钟和围手术期输血。在训练组中,随着评分从0分增加到10分,观察到的和预测的术后死亡率分别从0%增加到45.5%和从0.8%增加到50.3%。评分模型在训练组和测试组中的Brier评分分别为0.0279和0.0318。评分模型在训练组和测试组中的曲线下面积(AUC)值均为0.776,在训练组(AUC = 0.721,德龙检验,P<0.001)和测试组(AUC = 0.669,德龙检验,P<0.001)中均显著高于EuroSCORE II。

结论

新的风险评分是一种有效且简洁的工具,能够准确预测瓣膜手术后疑似HF患者的术后死亡率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27a4/11273104/b08290a35c7c/2153-8174-24-2-038-g5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27a4/11273104/f17d8c2a4653/2153-8174-24-2-038-g1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27a4/11273104/e105a5a01362/2153-8174-24-2-038-g2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27a4/11273104/67251411d3c5/2153-8174-24-2-038-g3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27a4/11273104/3f0f9821b4ee/2153-8174-24-2-038-g4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27a4/11273104/b08290a35c7c/2153-8174-24-2-038-g5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27a4/11273104/f17d8c2a4653/2153-8174-24-2-038-g1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27a4/11273104/e105a5a01362/2153-8174-24-2-038-g2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27a4/11273104/67251411d3c5/2153-8174-24-2-038-g3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27a4/11273104/3f0f9821b4ee/2153-8174-24-2-038-g4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/27a4/11273104/b08290a35c7c/2153-8174-24-2-038-g5.jpg

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