Development and validation of a Bayesian index for predicting major adverse cardiac events with percutaneous transluminal coronary angioplasty

Abstract

Objective-To create a risk model for predicting major adverse complicating events of percutaneous transluminal coronary angioplasty (PTCA), and to test the accuracy of the model on a prospective cohort of patients Setting-Tertiary cardiac centre Methods-Available software can predict probabilities of events using Bayes's theorem. To establish the accuracy of these predictive tools, a Bayes table was created to evaluate major adverse complicating events (MACE)-death, emergency coronary artery bypass grafting (CABG), or Q wave infarct occurring during the in-patient episode-on the first 1500 patients in the department PTCA database (development group); the predictive value of this model was then tested with the subsequent 1000 patients (evaluation group). The following probabilities were assessed to determine their association with MACE: age, sex, left ventricular function, American Heart Association lesion morphology classification, cardiogenic shock, previous CABG, diabetes, hypertension, multivessel PTCA. Main outcome measures-To establish the discriminatory ability of the predictive index, calibration plots and receiver operating characteristic (ROC) curves were obtained to compare the development and evaluation groups. Results-The ROC curve plotted to determine the discriminatory value of the Bayesian table created from the development group (n = 1500) in predicting MACE in the evaluation group (n = 1000) showed a moderately predictive area under the curve of 0.76 (SEM 0.07). This predictive accuracy was confirmed with separately constructed calibration plots. Conclusions-Accurate predictions of MACE can be identified in populations undergoing percutaneous intervention. The database used allows operators to obtain consent from patients appropriately from their own experience rather than from other published data. If a national PTCA database existed along similar lines, individual operators and interventional centres could compare themselves with nationally available data. (Heart 2001;85:69-72)

Multivariate prediction of major adverse cardiac events after 9914 percutaneous coronary interventions in the north west of England

“…24 This model uses Bayes theorem and was developed on 1500 consecutive patients who underwent PCI starting from January 1995. The variables identified as predictive of MACE were cardiogenic shock, ejection fraction , 30%, Figure 1 Receiver operator characteristic (ROC) curve for multivariate prediction model.…”

“…Apart from being developed on a cohort of patients from the mid 1990s, de Belder's model is limited by the Bayesian assumption that all risk factors are independent and therefore would have a tendency to overestimate the risk. 24 Strengths and meaning of the NWQIP model…”

Risk scoring for percutaneous coronary intervention: let's do it!

“…There are, in fact, more than half-a-dozen other scoring systems which have been developed for PCI, from the days of balloon angioplasty to the modern era of drug-eluting stents and glycoprotein IIb/IIIa inhibitors. [4][5][6][7][8][9][10][11][12] They have confirmed that the main risk factors are age, recent MI, shock, emergency case, poor left ventricular function, renal impairment, peripheral vascular disease, multi-vessel disease, left main PCI and American Heart Association type C lesion. These studies all have their shortcomings.…”

External validation of established risk adjustment models for procedural complications after percutaneous coronary intervention

“…Moreover, there is concern about their current applicability given the changes in practice that have taken place since the models were derived (eg, the use of thienopyridines, glycoprotein inhibitors and stents, as well as the greater application of PCI to patients with acute coronary syndromes). So far two models reported in the UK population setting have looked at major adverse cardiovascular and cerebrovascular events (MACCE) 17 18…”