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1.
Rev. bras. cir. cardiovasc ; 37(1): 1-6, Jan.-Feb. 2022. tab, graf
Article in English | LILACS-Express | LILACS | ID: biblio-1365542

ABSTRACT

Abstract Introduction: Coronary artery bypass grafting (CABG) performed with and without cardiopulmonary bypass (CPB) support has been widely discussed in the literature. However, little is known about the outcomes of those techniques in Brazil. This study aims at exploring 30-day mortality and morbidity outcomes of on- and off-pump isolated CABG in a large sample from Southern Brazil. Methods: A single-center cohort with 1,767 patients undergoing isolated CABG (January 2013 - December 2018) was initially evaluated. Patients undergoing off-pump (N=397) and on-pump (N=1,370) CABG were identified. To obtain two completely homogeneous study groups, propensity score matching was used. The paired groups were compared by descriptive and univariate analyses. Then, logistic regression was used to verify the effects of on- and off-pump CABG on 30-day mortality. Results: None of the baseline characteristics showed significant difference between the groups (P>0.05). None of the analyzed morbidity outcomes showed any difference between the groups, including acute myocardial infarction (3.0% vs. 1.5%; P=0.192), stroke (2.4% vs. 4.2%; P=0.193), and major reoperation (0.6% vs. 0.3%; P=1.000), as well as the major adverse cardiovascular and cerebrovascular events composite outcome (6.3% vs. 7.5%; P=0.541). Mortality also did not differ (1.5% vs. 2.4%; P=0.401), and CPB support was not an independent predictor of risk for 30-day mortality (odds ratio: 2.052; 95% confidence interval: 0,609-6.913; P=0.246). Conclusion: After matching by propensity analyses, similar rates of on- and off-pump 30-day mortality and other major outcomes were observed. In addition, the use of CPB support was not an independent predictor of risk for the occurrence of 30-day mortality.

2.
Rev. bras. cir. cardiovasc ; 33(4): 391-397, July-Aug. 2018. tab, graf
Article in English | LILACS | ID: biblio-958426

ABSTRACT

Abstract Introduction: The interest in Expert systems has increased in the medical area. Some of them are employed even for diagnosis. With the variability of transcatheter prostheses, the most appropriate choice can be complex. This scenario reveals an enabling environment for the use of an Expert system. The goal of the study was to develop an Expert system based on artificial intelligence for supporting the transcatheter aortic prosthesis selection. Methods: The system was developed on Expert SINTA. The rules were created according to anatomical parameters indicated by the manufacturing company. Annular aortic diameter, aortic area, aortic perimeter, ascending aorta diameter and Valsalva sinus diameter were considered. After performing system accuracy tests, it was applied in a retrospective cohort of 22 patients with submitted to the CoreValve prosthesis implantation. Then, the system indications were compared to the real heart team decisions. Results: For 10 (45.4%) of the 22 patients there was no concordance between the Expert system and the heart team. In all cases with discordance, the software was right in the indication. Then, the patients were stratified in two groups (same indication vs. divergent indication). The baseline characteristics did not show any significant difference. Mortality, stroke, acute myocardial infarction, atrial fibrillation, atrioventricular block, aortic regurgitation and prosthesis leak did not present differences. Therefore, the maximum aortic gradient in the post-procedure period was higher in the Divergent Indication group (23.9 mmHg vs. 11.9 mmHg, P=0.03), and the mean aortic gradient showed a similar trend. Conclusion: The utilization of the Expert system was accurate, showing good potential in the support of medical decision. Patients with divergent indication presented high post-procedure aortic gradients and, even without clinical repercussion, these parameters, when elevated, can lead to early prosthesis dysfunction and the necessity of reoperation.


Subject(s)
Humans , Male , Female , Aged , Aged, 80 and over , Heart Valve Prosthesis/standards , Artificial Intelligence , Transcatheter Aortic Valve Replacement/standards , Reference Standards , Software Design , Reproducibility of Results , Retrospective Studies , Statistics, Nonparametric , Clinical Decision-Making
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