A Nomogram to Predict Patients with Obstructive Coronary Artery Disease: Development and Validation

Abstract

Objective: To develop and validate clinical prediction models for the development of a nomogram to estimate the probability of patients having coronary artery disease (CAD).Methods and Results: A total of 1,025 patients referred for coronary angiography were included in a retrospective, single-center study. Randomly, 720 patients (70%) were selected as the development group and the other patients were selected as the validation group. Multivariate logistic regression analysis showed that the seven risk factors age, sex, systolic blood pressure, lipoprotein-associated phospholipase A2, type of angina, hypertension, and diabetes were significant for diagnosis of CAD, from which we established model A. We established model B with the risk factors age, sex, height, systolic blood pressure, low-density lipoprotein cholesterol, lipoprotein-associated phospholipase A2, type of angina, hypertension, and diabetes via the Akaike information criterion. The risk factors from the original Framingham Risk Score were used for model C. From comparison of the areas under the receiver operating characteristic curve, net reclassification improvement, and integrated discrimination improvement of models A, B, and C, we chose model B to develop the nomogram because of its fitness in discrimination, calibration, and clinical efficiency. The nomogram for diagnosis of CAD could be used easily and conveniently. Conclusion: An individualized clinical prediction model for patients with CAD allowed an accurate estimation in Chinese populations. The Akaike information criterion is a better method in screening risk factors. The net reclassification improvement and integrated discrimination improvement are better than the area under the receiver operating characteristic curve in discrimination. Decision curve analysis can be used to evaluate the efficiency of clinical prediction models.

The level of serum total bile acid is related to atherosclerotic lesions, prognosis and gut Lactobacillus in acute coronary syndrome patients

“…Three hundred and sixty ACS patients were included in this study to explore the relationship between serum TBA and the severity, prognosis and Lactobacillus . The overall characteristics of the patients were consistent with previous clinical studies of obstructive CAD [ 30 ]. Our research mainly found that: (1) lower level of serum TBA was highly correlated with the coronary lesion severity, myocardial damage, inflammation and gut Lactobacillus levels in ACS patients, especially in AMI; (2) lower level of serum TBA was highly associated with mortality (including all-cause mortality and cardiac mortality) after PCI in ACS, especially in AMI; (3) serum TBA had a partial mediating effect rather than regulating effect between gut Lactobacillus and coronary lesions and prognosis in ACS.…”

Prediction of major adverse cardiovascular events in patients with acute coronary syndrome: Development and validation of a non-invasive nomogram model based on autonomic nervous system assessment

“…Acute coronary events may occur in patients who have none of the well-established classical cardiovascular risk factors, suggesting there may be additional coronary risk factors that have not yet been recognized ( 3 – 5 , 10 , 11 ). Therefore, systematic ACS risk-assessment tools and appropriate recommendations for risk assessment in clinical guidelines are essential for the implementation of strategies that prevent MACEs in these patients.…”

“…Coronary artery disease (CAD) is the major cause of death worldwide ( 1 , 2 ). Even though risk-factor targeted management and revascularization have dramatically improved the outcomes for these patients, especially those with acute coronary syndrome (ACS), these patients remain at high risk for morbidity and mortality ( 3 – 5 ). Strategies for risk stratification and risk-adjusted management of ACS have been a major focus of researchers during the past decade ( 6 , 7 ).…”

Low body temperature and mortality in critically ill patients with coronary heart disease: a retrospective analysis from MIMIC-IV database

“…cardiovascular disease within 1 year after admission was found to be 16.1%, making it the second highest cause of death following malignant tumors [2]. Therefore, the role of predictive factors, biomarkers, and scores in the development and prognosis of CHD has been widely acknowledged [3][4][5][6][7]. Although these factors are of good efficiency in predicting, some of them are too complicated to be routinely used and postponed awaiting laboratory tests.…”