This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Personal Folders Download to citation manager Author home page(s): Pedro E. Antunes Manuel J. Antunes Permission Requests Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Antunes, P. E. Articles by Antunes, M. J. Search for Related Content PubMed PubMed Citation Articles by Antunes, P. E. Articles by Antunes, M. J. Related Collections Cardiac - other Coronary disease

Mortality risk prediction in coronary surgery: a locally developed model outperforms external risk models

Cardiothoracic Surgery, University Hospital, Coimbra, Portugal

*Corresponding author. Cirurgia Cardiotorácica, Hospitais da Universidade, 3000 Coimbra, Portugal. Tel.: +351-239400418; fax: +351-239829674.

E-mail address: antunes.cct.huc{at}sapo.pt (M.J. Antunes).

This study aimed at assessing the performance of three external risk-adjusted models – logistic EuroSCORE, Parsonnet score and Ontario Province Risk (OPR) score – in predicting in-hospital mortality in patients submitted to coronary artery bypass graft (CABG) and to develop a local risk-score model. Data on 4567 patients who underwent isolated CABG (1992–2001) were extracted from our clinical database. Hospital mortality was 0.96% (44 patients). For the three external systems, observed and predicted mortalities were compared, and discrimination and calibration were assessed. A local risk model was developed and validated by means of logistic regression and bootstrap analysis. The EuroSCORE predicted a mortality of 2.34% (P<0.001 vs. observed), the Parsonnet 4.43% (P<0.0001) and the OPR 1.66% (P<0.005). All models overestimated mortality significantly in almost all tertile risk groups. The areas under the ROC curve (AUC) for EuroSCORE, Parsonnet and OPR were 0.754, 0.664 and 0.683, respectively. The local model exhibited good calibration and discrimination AUC, 0.752. In conclusion, the three risk-score systems analyzed do not accurately predict in-hospital mortality in our coronary surgery patients; hence their use for risk prediction may not be appropriate in our population. We developed a risk-prediction model that can be used as an instrument to provide accurate information about the risk of in-hospital mortality in our patient population.

Key Words: Coronary artery bypass surgery; Mortality predictive models; Risk-adjusted mortality