ArticleFrontiers in cardiovascular medicine2023
Development and validation of a nomogram to predict mortality risk in patients with ischemic heart disease.
Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 5 citations in OpenAlex.
- CABIT: a novel biomarkers-integrated inflammatory risk tool for ischemic heart disease developed in the USA and prospectively validated in China.Journal of translational medicine · 2026Article
- Nomogram to screen older adult patients attending the radiology department for sarcopenia.BMC geriatrics · 2025Article
- A nomogram model in elderly patients with coronary heart disease for predicting prognosis: research based on a real-world registry in China.Frontiers in cardiovascular medicine · 2025Article
- Article
- Global burden of ischemic heart disease associated with high red and processed meat consumption: an analysis of 204 countries and territories between 1990 and 2019.BMC public health · 2023Article
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Authors and funding
7 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Ischemic Heart Disease (IHD) is the leading cause of death from cardiovascular disease. Currently, most studies have focused on factors influencing IDH or mortality risk, while few predictive models have been used for mortality risk in IHD patients. In this study, we constructed an effective nomogram prediction model to predict the risk of death in IHD patients by machine learning. Methods: We conducted a retrospective study of 1,663 patients with IHD. The data were divided into training and validation sets in a 3:1 ratio. The least absolute shrinkage and selection operator (LASSO) regression method was used to screen the variables to test the accuracy of the risk prediction model. Data from the training and validation sets were used to calculate receiver operating characteristic (ROC) curves, C-index, calibration plots, and dynamic component analysis (DCA), respectively. Results: Using LASSO regression, we selected six representative features, age, uric acid, serum total bilirubin, albumin, alkaline phosphatase, and left ventricular ejection fraction, from 31 variables to predict the risk of death at 1, 3, and 5 years in patients with IHD, and constructed the nomogram model. In the reliability of the validated model, the C-index at 1, 3, and 5 years was 0.705 (0.658-0.751), 0.705 (0.671-0.739), and 0.694 (0.656-0.733) for the training set, respectively; the C-index at 1, 3, and 5 years based on the validation set was 0.720 (0.654-0.786), 0.708 (0.650-0.765), and 0.683 (0.613-0.754), respectively. Both the calibration plot and the DCA curve are well-behaved. Conclusion: Age, uric acid, total serum bilirubin, serum albumin, alkaline phosphatase, and left ventricular ejection fraction were significantly associated with the risk of death in patients with IHD. We constructed a simple nomogram model to predict the risk of death at 1, 3, and 5 years for patients with IHD. Clinicians can use this simple model to assess the prognosis of patients at the time of admission to make better clinical decisions in tertiary prevention of the disease.
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