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  2. Keyword: Thoracic Surgery/complications
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IJCS
Original Article

Probabilistic Model for Prediction of Prognostics in Myocardial Revascularization: Complications in Coronary Surgery

Valcellos José da Cruz Viana et al. Felipe Coelho Argolo Nilzo Augusto Mendes Ribeiro Augusto Ferreira da Silva Junior Luis Claudio Lemos Correia

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.

  • Forecasting
  • Myocardial Infarction
  • Myocardial Revascularization
  • Risk Factors
  • Thoracic Surgery/complications
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Creative Commons Attribution (CC BY) Creative Commons license
01/01/2017 DOI: 10.5935/2359-4802.20170049 Views 2

How to cite this article

EndNote RIS

Electronic Document Format (Vancouver)

Viana VJC, Argolo FC, Ribeiro NAM, Silva Junior AF, Correia LCL. Probabilistic Model for Prediction of Prognostics in Myocardial Revascularization: Complications in Coronary Surgery. International Journal of Cardiovascular Sciences 2017;30(4):307-12.

Electronic Document Format (ABNT)

Viana, Valcellos José da Cruz; Argolo, Felipe Coelho; Ribeiro, Nilzo Augusto Mendes; Silva Junior, Augusto Ferreira da; Correia, Luis Claudio Lemos. Probabilistic Model for Prediction of Prognostics in Myocardial Revascularization: Complications in Coronary Surgery. International Journal of Cardiovascular Sciences, v. 30, n. 4, p. 307-312, Dec. 2016.

Electronic Document Format (APA)

Viana, V. J. C., Argolo, F. C., Ribeiro, N. A. M., Silva Junior, A. F. , & Correia, L. C. L. (2017). Probabilistic Model for Prediction of Prognostics in Myocardial Revascularization: Complications in Coronary Surgery. International Journal of Cardiovascular Sciences, 30(4), 307-312.

Electronic Document Format (ISO)

Viana, Valcellos José da Cruz and Argolo, Felipe Coelho and Ribeiro, Nilzo Augusto Mendes and Silva Junior, Augusto Ferreira da and Correia, Luis Claudio Lemos. Probabilistic Model for Prediction of Prognostics in Myocardial Revascularization: Complications in Coronary Surgery. International Journal of Cardiovascular Sciences [online]. 2017, vol. 30, n. 4, [cited 2026-10-14], pp. 307-312. Available from: <https://journals.cardiol.br/en/article/probabilistic-model-for-prediction-of-prognostics-in-myocardial-revascularization-complications-in-coronary-surgery/>. ISSN 2359-4802

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