ArticleInternational journal of general medicine2022
Development and Evaluation of a Risk Prediction Model for Left Ventricular Aneurysm in Patients with Acute Myocardial Infarction in Northwest China.
Article in International journal of general medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed, 6 citations in OpenAlex.
- Exploratory analysis of predictors of ventricular aneurysm in a cohort of 291 patients with acute myocardial infarction.BMC cardiovascular disorders · 2024Article
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Authors and funding
6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose: 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.
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