RESUMO
Context: Perioperative period. Aims: Occurrence of PPM after AVR, factors associated with PPM, impact on mortality. Settings and Design: Teritary Care Referral Cardiac Centre. Materials and Methods: A retrospective analysis of AVR procedures at a single centre over 4 years was conducted. Demographic, echocardiographic and outcome data were collected from institute database. Rahimtoola criteria of indexed effective orifice area (iEOA) were used to stratify patients into PPM categories. Patients with and without PPM were compared for associated factors. Statistical Analysis Used: Independent t-test, chi-square test, logistic regression analysis, ROC-AUC, Youden index. Results: 606 patients with complete data were analysed for PPM. The incidence of mild, moderate and severe PPM was 6.1% (37), 2.5% (15) and 0.5% (3) respectively. There was no impact of PPM on all-cause in-hospital mortality. PPM was observed more with Aortic Stenosis (AS) compared to Aortic Regurgitation (AR) as etiology. Aortic annulus indexed to BSA (iAA) had a very good predictive ability for PPM at <16mm/m2BSA. Conclusions: PPM has lower incidence after AVR in this Indian population and does not increase early mortality. Patients with AS and iAA<16mm/m2BSA should be cautiously dealt with to prevent PPM.
RESUMO
Aims and Objectives: To validate Aristotle basic complexity and Aristotle comprehensive complexity (ABC and ACC) and risk adjustment in congenital heart surgery‑1 (RACHS‑1) prediction models for in hospital mortality after surgery for congenital heart disease in a single surgical unit. Materials and Methods: Patients younger than 18 years, who had undergone surgery for congenital heart diseases from July 2007 to July 2013 were enrolled. Scoring for ABC and ACC scoring and assigning to RACHS‑1 categories were done retrospectively from retrieved case files. Discriminative power of scoring systems was assessed with area under curve (AUC) of receiver operating curves (ROC). Calibration (test for goodness of fit of the model) was measured with Hosmer‑Lemeshow modification of χ2 test. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were applied to assess reclassification. Results: A total of 1150 cases were assessed with an all‑cause in‑hospital mortality rate of 7.91%. When modeled for multivariate regression analysis, the ABC (χ2 = 8.24, P = 0.08), ACC (χ2 = 4.17, P = 0.57) and RACHS‑1 (χ2 = 2.13, P = 0.14) scores showed good overall performance. The AUC was 0.677 with 95% confidence interval (CI) of 0.61-0.73 for ABC score, 0.704 (95% CI: 0.64-0.76) for ACC score and for RACHS‑1 it was 0.607 (95%CI: 0.55-0.66). ACC had an improved predictability in comparison to RACHS‑1 and ABC on analysis with NRI and IDI. Conclusions: ACC predicted mortality better than ABC and RCAHS‑1 models. A national database will help in developing predictive models unique to our populations, till then, ACC scoring model can be used to analyze individual performances and compare with other institutes.