Coronary heart disease (CHD) is one of the major causes of disability in adults as well as one of the main causes of death in the developed countries. Although significant progress has been made in the diagnosis and treatment of CHD, further investigation is still needed. The objective of this study was to develop a data-mining system for the assessment of heart event-related risk factors targeting in the reduction of CHD events. The risk factors investigated were: 1) before the event: a) nonmodifiable-age, sex, and family history for premature CHD, b) modifiable-smoking before the event, history of hypertension, and history of diabetes; and 2) after the event: modifiable-smoking after the event, systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG). A total of 528 cases were collected from the Paphos district in Cyprus, most of them with more than one event. Data-mining analysis was carried out using the C4.5 decision tree algorithm for the aforementioned three events using five different splitting criteria. The most important risk factors, as extracted from the classification rules analysis were: 1) for MI, age, smoking, and history of hypertension; 2) for PCI, family history, history of hypertension, and history of diabetes; and 3) for CABG, age, history of hypertension, and smoking. Most of these risk factors were also extracted by other investigators. The highest percentages of correct classifications achieved were 66%, 75%, and 75% for the MI, PCI, and CABG models, respectively. It is anticipated that data mining could help in the identification of high and low risk subgroups of subjects, a decisive factor for the selection of therapy, i.e., medical or surgical. However, further investigation with larger datasets is still needed.
Although significant progress has been made in the diagnosis and treatment of coronary heart disease (CHD), further investigation is still needed. The objective of this study was to develop a data mining system using association analysis based on the apriori algorithm for the assessment of heart event related risk factors. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG). A total of 369 cases were collected from the Paphos CHD Survey, most of them with more than one event. The most important risk factors, as extracted from the association rule analysis were: sex (male), smoking, high density lipoprotein, glucose, family history, and history of hypertension. Most of these risk factors were also extracted by our group in a previous study using the C4.5 decision tree algorithms, and by other investigators. Further investigation with larger data sets is still needed to verify these findings.
Coronary heart disease (CUD) is a major cause of morbidity and mortality in the western world. Although significant progress has been made in the diagnosis and treatment of CUD, further investigation is still needed. The objective of this study was to develop a data mining system for the assessment of heart event related risk factors. The risk factors investigated were: i. clinical: sex, age, smoking, systolic blood pressure, family history for premature CUD, history of hypertension, and diabetes; and ii. biochemical: cholesterol, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG). A total of 620 cases were collected from the Paphos district in Cyprus, most of them with more than one event. Data mining analysis was carried out using the C4.5 decision trees algorithms. The most important risk factors, as extracted from the classification rules analysis were: sex, age, smoking, blood pressure, and cholesterol. Most of these risk factors were also extracted by other investigators. It is anticipated that data mining could help in the identification of high and low risk subgroups of patients, a decisive factor for the selection of therapy, i.e. medical or surgical. However, further investigation with larger data sets is still needed.
Background: The COVID-19 pandemic carries a high burden of morbidity and mortality worldwide. We aimed to identify possible predictors of in-hospital major cardiovascular (CV) events in COVID-19. Methods: We retrospectively included patients hospitalized for COVID-19 from 10 centers. Clinical, biochemical, electrocardiographic, and imaging data at admission and medications were collected. Primary endpoint was a composite of in-hospital CV death, acute heart failure (AHF), acute myocarditis, arrhythmias, acute coronary syndromes (ACS), cardiocirculatory arrest, and pulmonary embolism (PE). Results: Of the 748 patients included, 141(19%) reached the set endpoint: 49 (7%) CV death, 15 (2%) acute myocarditis, 32 (4%) sustained-supraventricular or ventricular arrhythmias, 14 (2%) cardiocirculatory arrest, 8 (1%) ACS, 41 (5%) AHF, and 39 (5%) PE. Patients with CV events had higher age, body temperature, creatinine, high-sensitivity troponin, white blood cells, and platelet counts at admission and were more likely to have systemic hypertension, renal failure (creatinine ≥ 1.25 mg/dL), chronic obstructive pulmonary disease, atrial fibrillation, and cardiomyopathy. On univariate and multivariate analysis, troponin and renal failure were associated with the composite endpoint. Kaplan–Meier analysis showed a clear divergence of in-hospital composite event-free survival stratified according to median troponin value and the presence of renal failure (Log rank p < 0.001). Conclusions: Our findings, derived from a multicenter data collection study, suggest the routine use of biomarkers, such as cardiac troponin and serum creatinine, for in-hospital prediction of CV events in patients with COVID-19.
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