International Journal of General Medicine (Jul 2022)

Development and Evaluation of a Risk Prediction Model for Left Ventricular Aneurysm in Patients with Acute Myocardial Infarction in Northwest China

  • Xing Y,
  • Wang C,
  • Wu H,
  • Ding Y,
  • Chen S,
  • Yuan Z

Journal volume & issue
Vol. Volume 15
pp. 6085 – 6096

Abstract

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Yuanming Xing,1,2 Chen Wang,1,2 Haoyu Wu,1,2 Yiming Ding,1,2 Siying Chen,3 Zuyi Yuan1,2 1Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, People’s Republic of China; 2Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi’an, People’s Republic of China; 3Department of Pharmacy, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, People’s Republic of ChinaCorrespondence: Zuyi Yuan, Department of Cardiovascular Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, People’s Republic of China, Email [email protected]: Left ventricular aneurysm (LVA) is a severe and common mechanical comorbidity with acute myocardial infarction (AMI) that can present high mortality and serious adverse outcomes. Accordingly, there is a need for early identification and prevention of patients at risk of LVA. The aim of this study was to develop and validate a risk prediction model for LVA among AMI patients in Northwest China.Methods: A total of 509 patients with AMI were retrospectively collected between January 2018 and August 2021. All patients were randomly divided into a training group (n=356) and a validation group (n=153). Potential risk factors for LVA were screened for predictive modelling using least absolute shrinkage and selection operator regression, multivariate logistic regression, clinical relevance, and represented by a comprehensive nomogram. Receiver operating characteristic curve, calibration curve, and decision-curve analysis (DCA) were used to assess the discrimination capacity, calibration, and clinical validity, respectively.Results: Seven predictors were finally identified for the establishment of prediction model, including age, cardiovascular disease history, left ventricular ejection fraction, ST-segment elevation, percutaneous coronary intervention history, mean platelet volume, and aspartate aminotransferase. The prediction model achieved acceptable areas under the curves of 0.901 (95% confidence interval [CI]=0.868– 0.933) and 0.908 (95% CI=0.861– 0.956) in the training and validation groups, respectively, and the calibration curves fit well in our model. The DCA result indicated that this nomogram exhibited a favorable performance in terms of clinical utility.Conclusion: An accurate prediction model for LVA development established, which can be applied to rapidly assess the risk of LVA in patients with AMI. Our findings will aid clinical decision-making to reduce the incidence of LVA in high-risk patients, and counteract adverse cardiovascular outcomes.Keywords: cardiovascular disease, acute myocardial infarction, left ventricular aneurysm, risk prediction model, adverse cardiovascular outcomes

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