Influencing factors and risk prediction model of adverse clinical outcomes after heart valve replacement

HAN Ying; HAN Xiao; WEI Wen-ke

doi:10.11659/jjssx.06E025035

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Influencing factors and risk prediction model of adverse clinical outcomes after heart valve replacement

Cardiovascular Surgery Column | 更新时间：2026-03-09

- Influencing factors and risk prediction model of adverse clinical outcomes after heart valve replacement
- JOURNAL OF REGIONAL ANATOMY AND OPERATIVE SURGERY Vol. 35, Issue 2, Pages: 98-103(2026)
- 作者机构：
  
  1.中国医科大学附属第一医院心脏外科，辽宁沈阳 110001
  2.中国医科大学附属第一医院肝胆外科，辽宁沈阳 110001
- 作者简介：
- 基金信息：
- DOI：10.11659/jjssx.06E025035
  CLC： R541
- Received：10 June 2025，
  
  Published：25 February 2026
- 稿件说明：
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韩英,韩啸,魏文珂.心脏瓣膜置换术后不良临床结局的影响因素及风险预测模型[J].局解手术学杂志,2026,35(02):98-103.

HAN Ying,HAN Xiao,WEI Wen-ke.Influencing factors and risk prediction model of adverse clinical outcomes after heart valve replacement[J].JOURNAL OF REGIONAL ANATOMY AND OPERATIVE SURGERY,2026,35(02):98-103.
韩英,韩啸,魏文珂.心脏瓣膜置换术后不良临床结局的影响因素及风险预测模型[J].局解手术学杂志,2026,35(02):98-103. DOI： 10.11659/jjssx.06E025035.

HAN Ying,HAN Xiao,WEI Wen-ke.Influencing factors and risk prediction model of adverse clinical outcomes after heart valve replacement[J].JOURNAL OF REGIONAL ANATOMY AND OPERATIVE SURGERY,2026,35(02):98-103. DOI： 10.11659/jjssx.06E025035.

摘要

目的

探讨心脏瓣膜置换术（HVR）后不良临床结局的危险因素，并构建风险预测模型。

方法

回顾性分析行HVR的321例心脏瓣膜病患者的临床资料，按7∶3的比例将其分为试验集和验证集。根据临床结局将患者分为结局不良组和结局良好组，分析HVR后不良临床结局的危险因素，构建风险预测模型并验证。

结果

心脏瓣膜病患者不良临床结局发生率为33.64%（108/321）。

Cox

回归分析显示，患者术前纽约心脏病学会（NYHA）心功能分级Ⅲ~Ⅳ级比例、急性生理学与慢性健康状况评分系统Ⅱ（APACHEⅡ）评分、氨基末端脑钠肽前体（NT-proBNP）水平升高，主动脉阻断时间、体外循环时间延长，术前预后营养指数（OPNI）、左心室射血分数（LVEF）降低均是HVR后出现不良临床结局的危险因素（

0.05）。受试者工作特征（

ROC

）曲线分析显示，模型在试验集术后6个月、1年、3年预测不良临床结局的曲线下面积（

AUC

）分别为0.845、0.867、0.876，验证集术后6个月、1年、3年预测不良临床结局的

AUC

分别为0.823、0.846和0.857。校准曲线显示，模型在试验集、验证集术后6个月、1年、3年校准曲线与理想曲线贴合度良好，

Hosmer

Lemeshow

检验显示

0.05。决策曲线分析显示，试验集术后6个月、1年、3年阈值概率分别在0.12~0.98、0.13~0.97、0.11~0.96，验证集术后6个月、1年、3年阈值概率分别在0.11~0.67、0.12~0.92、0.11~0.89时，模型具有净收益。

结论

术前心功能分级、APACHEⅡ评分、NT-proBNP升高，主动脉阻断时间、体外循环时间延长及术前OPNI、LVEF降低均是HVR后患者出现不良临床结局的危险因素，据此构建的模型预测效能较好。

Abstract

Objective

To explore the risk factors for adverse clinical outcomes after heart valve replacement （HVR） and to develop a risk prediction model.

Methods

The clinical data of 321 patients with heart valve disease who underwent HVR were retrospectively analyzed，and the patients were divided into the training set and the validation set in a ratio of 7∶3. The patients were divided into the poor outcome group and the good outcome group according to clinical outcomes. The risk factors for adverse clinical outcomes after HVR were analyzed， and a risk prediction model was constructed and validated.

Results

The incidence of adverse clinical outcomes in patients with heart valve disease was 33.64% （108/321）.

Cox

regression analysis revealed that preoperative New York Heart Association （NYHA） class Ⅲ to Ⅳ， elevated acute physiology and chronic health evaluation Ⅱ （APACHEⅡ） score and amino-terminal pro-brain natriuretic peptide （NT-proBNP） level， prolonged aortic cross-clamp time and cardiopulmonary bypass time， decreased Onodera's prognostic nutritional index （OPNI） and left ventricular ejection fraction （LVEF） before operation were the independent risk factors for adverse clinical outcomes after HVR （

0.05）. The

receiver operating characteristic

（

ROC

） curve analysis showed that the

area under the curve

（

AUC

） for predicting adverse clinical outcomes 6 months， 1 year， and 3 years after operation was 0.845， 0.867， and 0.876 in the training set， and 0.823， 0.846， and 0.857 in the validation set， respectively. Calibration curve demonstrated good agreement between the calibration curves and ideal curves 6 months， 1 year， and 3 years after operation in the training set and the validation set， with

Hosmer

Lemeshow

test

0.05.

Decision curve analysis

indicated that the model provided positive net clinical benefit when the threshold probabilities ranged from 0.12 to 0.98， 0.13 to 0.97， and 0.11 to 0.96 in the training set， and from 0.11 to 0.67， 0.12 to 0.92， and 0.11 to 0.89 in the validation set 6 months， 1 year， and 3 years after operation， respectively.

Conclusion

Higher preoperative NYHA classes， elevated APACHEⅡ score and NT-proBNP level， prolonged aortic cross-clamp time and cardiopulmonary bypass time， and decreased OPNI and LVEF before operation are all the risk factors for adverse clinical outcomes after HVR. The prediction model constructed based on these factors demonstrates good performance.

关键词

Keywords

references

Zern EK ， Frank RC ， Yucel E . Valvular heart disease in the cardiac intensive care unit ［J］. Crit Care Clin ， 2024 ， 40 （ 1 ）： 105 - 120 . doi： 10.1016/j.ccc.2023.05.002 http://dx.doi.org/10.1016/j.ccc.2023.05.002 .

Cuko B ， Baudo M ， Busuttil O ， et al . Outcomes of left-sided heart valve surgery after heart transplantation： a systematic review ［J］. Heart Fail Rev ， 2024 ， 29 （ 1 ）： 227 - 234 . doi： 10.1007/s10741-023-10368-5 http://dx.doi.org/10.1007/s10741-023-10368-5 .

Hou J ， Huang X ， Shang L ， et al . Reduced angiopoietin factor 2 levels are correlated with better cardiac function and prognosis in valvular heart disease ［J］. Braz J Cardiovasc Surg ， 2023 ， 38 （ 1 ）： 104 - 109 . doi： 10.21470/1678-9741-2021-0364 http://dx.doi.org/10.21470/1678-9741-2021-0364 .

Dokollari A ， Margaryan R ， Torregrossa G ， et al . Risk predictors that impact long-term prognosis in patients undergoing aortic valve replacement with the perceval sutureless bioprosthesis ［J］. Cardiovasc Revasc Med ， 2023 ， 55 （ 1 ）： 10 - 19 . doi： 10.1016/j.carrev.2023. 04.006 http://dx.doi.org/10.1016/j.carrev.2023.04.006 .

贺宇，牛毅菲，黄琼，等 . 二尖瓣反流面积分级对重度主动脉瓣狭窄合并二尖瓣关闭不全患者经导管主动脉瓣置换术后预后的影响［J］. 实用心脑肺血管病杂志， 2023 ， 31 （ 1 ）： 33 - 37 . doi： 10.12114/j.issn.1008-5971.2023.00.005 http://dx.doi.org/10.12114/j.issn.1008-5971.2023.00.005 .

张倩，王墨扬，吴永健 . 2021 ESC/EACTS心脏瓣膜病管理指南解读［J］. 中华心血管病杂志， 2021 ， 49 （ 12 ）： 1256 - 1260 . doi： 10.3760/cma.j.cn112148-20210924-00818 http://dx.doi.org/10.3760/cma.j.cn112148-20210924-00818 .

Wang K ， Geng B ， Shen Q ， et al . Global，regional，and national incidence，mortality，and disability-adjusted life years of non-rheumatic valvular heart disease and trend analysis from 1990 to 2019： results from the Global Burden of Disease study 2019 ［J］. Asian Cardiovasc Thorac Ann ， 2023 ， 31 （ 8 ）： 706 - 722 . doi： 10.1177/02184923231200695 http://dx.doi.org/10.1177/02184923231200695 .

Panagides V ， Cuervo G ， Llopis J ， et al . Infective endocarditis after transcatheter versus surgical aortic valve replacement ［J］. Clin Infect Dis ， 2024 ， 78 （ 1 ）： 179 - 187 . doi： 10.1093/cid/ciad464 http://dx.doi.org/10.1093/cid/ciad464 .

张超，韩冬，范迪堃，等 . 血清脂联素、成纤维生长因子-23在心脏瓣膜置换术患者预后中的评估价值［J］. 实用临床医药杂志， 2024 ， 28 （ 3 ）： 58 - 62 . doi： 10.7619/jcmp.20233242 http://dx.doi.org/10.7619/jcmp.20233242 .

欧阳春，黄磊，叶小强，等 . 重症心脏瓣膜置换术后患者预后的影响因素分析及风险模型的建立［J］. 岭南心血管病杂志， 2022 ， 28 （ 6 ）： 502 - 507 . doi： 10.3969/j.issn.1007-9688.2022.06.03 http://dx.doi.org/10.3969/j.issn.1007-9688.2022.06.03 .

Xu H ， Xue YY ， Shen X ， et al . Association of critical value with 28-day mortality after cardiac surgery ［J］. Heart Surg Forum ， 2023 ， 26 （ 1 ）： 126 - 130 . doi： 10.1532/HSF.5215 http://dx.doi.org/10.1532/HSF.5215 .

宋跃，曹剑，刘冬 . 术前血清N末端B型脑钠肽前体和心肌肌钙蛋白I水平对老年重症心脏瓣膜病患者术后发生低心排血量综合征的预测价值［J］. 中国医药， 2022 ， 17 （ 3 ）： 394 - 398 . doi： 10.3760/j.issn.1673-4777.2022.03.017 http://dx.doi.org/10.3760/j.issn.1673-4777.2022.03.017 .

张星，连敬 . 老年心脏瓣膜置换术后患者抗凝治疗依从性调查及其预后因素分析［J］. 老年医学与保健， 2024 ， 30 （ 5 ）： 1303 - 1308 . doi： 10.3969/j.issn.1008-8296.2024.05.018 http://dx.doi.org/10.3969/j.issn.1008-8296.2024.05.018 .

洪伟，张永恒，刘建平，等 . 预后营养指数在心脏瓣膜病术后肺部感染预测价值研究［J］. 四川医学， 2024 ， 45 （ 7 ）： 749 - 755 . doi： 10.16252/j.cnki.issn1004-0501-2024.07.010 http://dx.doi.org/10.16252/j.cnki.issn1004-0501-2024.07.010 .

Ibrahim KS ， Kheirallah K ， Mayyas F ， et al . Atrial fibrillation after rheumatic heart valve surgery： incidence，predictors，and outcomes ［J］. Thorac Cardiovasc Surg ， 2023 ， 71 （ 4 ）： 297 - 306 . doi： 10.1055/s-0041-1740985 http://dx.doi.org/10.1055/s-0041-1740985 .

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