International Journal of Cardiovascular Sciences. 01/Jul/2017;30(4):307-12.
Probabilistic Model for Prediction of Prognostics in Myocardial Revascularization: Complications in Coronary Surgery
DOI: 10.5935/2359-4802.20170049
Abstract
Introduction:
Risk scores evaluate pre-operatory risk and present support for clinical decisions, however the performance of these tools in samples different from the original ones remains unclear.
Objectives:
Investigate the external validity of risk scores (STS and Euroscore) in cardiac surgery and the predictive performance of clinical features derived from the sample.
Methods:
Retrospective Cohort study conducted between October, 2010, and April, 2015. We used logistic regression to identify risk factors for hospital morbidity. The sample was divided for cross-validation, with 2/3 of the patients selected for model fitting and 1/3 for prediction testing. The performance of risk scores and clinical features was evaluated through AUROC and calibraton the Hosmer-Lemeshow test (H-L).
Results:
Data was retrieved from 472 patients who underwent coronary cardiac surgery in Hospital Santa Izabel da Santa Casa, BA. Mean age was 62.8 years old and 32.5% of the sample were women. Traditional surgical risk scores did not present significant discriminative performance for this sample. Factors associated with the outcome after adjusting for covariates were: age, previous myocardial revascularization and pre-surgical creatinine levels. The adjusted model presented similar discrimination and calibration values during training (AUROC = 0,72; IC 95% 0,59-0,84; H-L valor p: 0,41) and validation (AUROC = 0,70; IC 95% 0,55 – 0,84; H-L valor p: 0,197).
Conclusion:
Traditional scores may be inaccurate when applied to different environments. New risk scores with good predictive power can be developed using local clinical variables.
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