Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts

Abstract

Objectives To develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations.Design Retrospective pooled analysis of individual patient data.Setting 18 hospitals in Europe and the United States.Participants Patients with stable chest pain without evidence for previous coronary artery disease, if they were referred for computed tomography (CT) based coronary angiography or catheter based coronary angiography (indicated as low and high prevalence settings, respectively). Main outcome measuresObstructive coronary artery disease (≥50% diameter stenosis in at least one vessel found on catheter based coronary angiography). Multiple imputation accounted for missing predictors and outcomes, exploiting strong correlation between the two angiography procedures. Predictive models included a basic model (age, sex, symptoms, and setting), clinical model (basic model factors and diabetes, hypertension, dyslipidaemia, and smoking), and extended model (clinical model factors and use of the CT based coronary calcium score). We assessed discrimination (c statistic), calibration, and continuous net reclassification improvement by cross validation for the four largest low prevalence datasets separately and the smaller remaining low prevalence datasets combined. ResultsWe included 5677 patients (3283 men, 2394 women), of whom 1634 had obstructive coronary artery disease found on catheter based coronary angiography. All potential predictors were significantly associated with the presence of disease in univariable and multivariable analyses. The clinical model improved the prediction, compared with the basic model (cross validated c statistic improvement from 0.77 to 0.79, net reclassification improvement 35%); the coronary calcium score in the extended model was a major predictor (0.79 to 0.88, 102%). Calibration for low prevalence datasets was satisfactory.Conclusions Updated prediction models including age, sex, symptoms, and cardiovascular risk factors allow for accurate estimation of the pretest probability of coronary artery disease in low prevalence populations. Addition of coronary calcium scores to the prediction models improves the estimates. IntroductionIn the United States, about 10.2 million people have chest pain complaints each year, 1 and more than 1.1 million diagnostic procedures of catheter based coronary angiography are performed on inpatients each year. 2 In a recent report based on the national cardiovascular data registry of the American College of Cardiology, 3 only 41% of patients undergoing elective procedures of catheter based coronary angiographies are diagnosed with obstructive coronary artery disease. The report's authors concluded that better risk stratification was needed, underlined by decision analyses showing that the choice of further diagnostic investigation in patients with chest pain depends primarily on the pretest probability of coronary artery disease. [4][5][6] The American College of Cardiology/American Heart Associatio...

High-Sensitivity Cardiac Troponin I and the Diagnosis of Coronary Artery Disease in Patients With Suspected Angina Pectoris

“…In 2011 and 2012, the CADC updated and extended the earlier DiamondForrester model to estimate more accurately the PTP of obstructive CAD identified on invasive coronary angiography in patients with suspected stable angina. 8,21 The CADC model incorporates age, sex, and chest pain characteristics and underpins the risk tables included in the current European Society of Cardiology guideline on the management of stable CAD.…”

“…3-5 However, there is evidence both that these models may overestimate risk [6][7][8] and that clinician use of stratification tools remains suboptimal. 9,10 In light of these challenges, there is widespread interest in identifying suitable biomarkers that may improve diagnostic accuracy in patients with suspected stable CAD.…”

“…2 Several national and international bodies have proposed standardized pathways that use risk models to estimate the pretest probability (PTP) of obstructive CAD and guide decision making with regards to appropriate use of investigations. [3][4][5] However, there is evidence both that these models may overestimate risk [6][7][8] and that clinician use of stratification tools remains suboptimal. 9,10 In light of these challenges, there is widespread interest in identifying suitable biomarkers that may improve diagnostic accuracy in patients with suspected stable CAD.…”

Application of Prediction Scores in Clinical Medicine

“…Например, в модели претестовой вероятности обструктивной коронарной болезни сердца Duke критерий дислипидемии включает уровень общего холестерина >6,5 ммоль/л, а в разработанной позднее модели CAD Consortium, пороговый уровень холесте-рина был ниже (>5 ммоль/л) [4,5].…”

Comparison of ESC and ACC/AHA guidelines for myocardial revascularization: are the differences clinically relevant? The European perspective

“…For pre-test probability of CAD, many algorithm have been proposed. 11,[15][16][17][18] Other guidelines recommended imaging patients at intermediate or high CAD risk based on the Framingham risk score. 19 The precise definition of intermediate probability of CAD is somewhat arbitrary.…”