External validation and extension of a diagnostic model for obstructive coronary artery disease: a cross-sectional predictive evaluation in 4888 patients of the Austrian Coronary Artery disease Risk Determination In Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort

Objective: To externally validate and extend a recently proposed prediction model to diagnose obstructive coronary artery disease (CAD), with the ultimate aim to better select patients for coronary angiography. Design: Analysis of individual baseline data of a prospective cardiology cohort. Setting: Single-centre secondary and tertiary cardiology clinic. Participants: 4888 patients with suspected CAD, without known previous CAD or other heart diseases, who underwent an elective coronary angiography between 2004 and 2008 as part of the prospective Coronary Artery disease Risk Determination In Innsbruck by diaGnostic ANgiography (CARDIIGAN) cohort. Relevant data were recorded as in routine clinical practice. Main outcome measures: The probability of obstructive CAD, defined as a stenosis of minimally 50% diameter in at least one of the main coronary arteries, estimated with the predictors age, sex, type of chest pain, diabetes status, hypertension, dyslipidaemia, smoking status and laboratory data. Missing predictor data were multiply imputed. Performance of the suggested models was evaluated according to discrimination (area under the receiver operating characteristic curve, depicted by the c statistic) and calibration. Logistic regression modelling was applied for model updating. Results: Among the 4888 participants (38% women and 62% men), 2127 (44%) had an obstructive CAD. The previously proposed model had a c statistic of 0.69 (95% CI 0.67 to 0.70), which was lower than the expected c statistic while correcting for case mix (c=0.80). Regarding calibration, there was overprediction of risk for high-risk patients. All logistic regression coefficients were smaller than expected, especially for the predictor 'chest pain'. Extension of the model with high-density lipoprotein and low-density lipoprotein cholesterol, fibrinogen, and C reactive protein led to better discrimination (c=0.72, 95% CI 0.71 to 0.74, p<0.001 for improvement). Conclusions: The proposed prediction model has a moderate performance to diagnose obstructive CAD in an unselected patient group with suspected CAD referred for elective CA. A small, but significant improvement was attained by including easily available and measurable cardiovascular risk factors.