Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts
Fractional Flow Reserve
DOI:
10.1136/bmj.e3485
Publication Date:
2012-06-12T22:48:28Z
AUTHORS (52)
ABSTRACT
<b>Objectives</b> To develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations. <b>Design </b>Retrospective pooled analysis individual patient data. <b>Setting </b>18 hospitals Europe and United States. <b>Participants</b> Patients with stable chest pain without evidence for previous disease, if they were referred computed tomography (CT) based angiography or catheter (indicated as high settings, respectively). <b>Main outcome measures</b> Obstructive (≥50% diameter stenosis at least one vessel found on angiography). Multiple imputation accounted missing predictors outcomes, exploiting strong correlation between two procedures. Predictive included a basic model (age, sex, symptoms, setting), clinical (basic factors diabetes, hypertension, dyslipidaemia, smoking), extended (clinical use CT calcium score). We assessed discrimination (c statistic), calibration, continuous net reclassification improvement by cross validation four largest datasets separately smaller remaining combined. <b>Results</b> 5677 patients (3283 men, 2394 women), whom 1634 had obstructive angiography. All potential significantly associated presence univariable multivariable analyses. The improved prediction, compared (cross validated c statistic from 0.77 to 0.79, 35%); score was major predictor (0.79 0.88, 102%). Calibration satisfactory. <b>Conclusions</b> Updated including age, cardiovascular risk allow accurate estimation Addition scores improves estimates.
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