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Background: Cardiovascular surgery has been undergoing transformations due to the advancement of percutaneous techniques, clinical treatment and primary prevention.

Rev Bras Cir Cardiovasc | Braz J Cardiovasc SurgRev Bras Cir Cardiovasc 2014;29(1) power of discrimination. Still, we must not forget that "few variables as possible" prevails in a model in order to have a greater acceptance [12,13]. At the Heart Institute, Clinics

Rev Bras Cir Cardiovasc 2012;27(2):203-10

The quality of data in electronic healthcare databases is a critical component when used for research and health practice. The aim of the present study was to assess the data quality in the Paulista Cardiovascular Surgery Registry II (REPLICCAR II) using two different audit methods, direct and indirect. The REPLICCAR II database contains data from 9 hospitals in São Paulo State with over 700 variables for 2229 surgical patients. The data collection was performed in REDCap platform using trained data managers to abstract information. We directly audited a random sample (n = 107) of the data collected after 6 months and indirectly audited the entire sample after 1 year of data collection. The indirect audit was performed using the data management tools in REDCap platform. We computed a modified Aggregate Data Quality Score (ADQ) previously reported by Salati et al. (2015). The agreement between data elements was good for categorical data (Cohen κ = 0.7, 95%CI = 0.59-0.83). For continuous data, the intraclass coefficient (ICC) for only 2 out of 15 continuous variables had an ICC < 0.9. In the indirect audit, 77% of the selected variables (n = 23) had a good ADQ score for completeness and accuracy. Data entry in the REPLICCAR II database proved to be satisfactory and showed competence and reliable data for research in cardiovascular surgery in Brazil.

BackgroundMortality prediction after cardiac procedures is an essential tool in clinical decision making. Although rheumatic cardiac disease remains a major cause of heart surgery in the world no previous study validated risk scores in a sample exclusively with this condition.ObjectivesDevelop a novel predictive model focused on mortality prediction among patients undergoing cardiac surgery secondary to rheumatic valve conditions.MethodsWe conducted prospective consecutive all-comers patients with rheumatic heart disease (RHD) referred for surgical treatment of valve disease between May 2010 and July of 2015. Risk scores for hospital mortality were calculated using the 2000 Bernstein-Parsonnet, EuroSCORE II, InsCor, AmblerSCORE, GuaragnaSCORE, and the New York SCORE. In addition, we developed the rheumatic heart valve surgery score (RheSCORE).ResultsA total of 2,919 RHD patients underwent heart valve surgery. After evaluating 13 different models, the top performing areas under the curve were achieved using Random Forest (0.982) and Neural Network (0.952). Most influential predictors across all models included left atrium size, high creatinine values, a tricuspid procedure, reoperation and pulmonary hypertension. Areas under the curve for previously developed scores were all below the performance for the RheSCORE model: 2000 Bernstein-Parsonnet (0.876), EuroSCORE II (0.857), InsCor (0.835), Ambler (0.831), Guaragna (0.816) and the New York score (0.834). A web application is presented where researchers and providers can calculate predicted mortality based on the RheSCORE.ConclusionsThe RheSCORE model outperformed pre-existing scores in a sample of patients with rheumatic cardiac disease.

BackgroundHeart surgery has developed with increasing patient complexity.ObjectiveTo assess the use of resources and real costs stratified by risk factors of patients submitted to surgical cardiac procedures and to compare them with the values reimbursed by the Brazilian Unified Health System (SUS).MethodAll cardiac surgery procedures performed between January and July 2013 in a tertiary referral center were analyzed. Demographic and clinical data allowed the calculation of the value reimbursed by the Brazilian SUS. Patients were stratified as low, intermediate and high-risk categories according to the EuroSCORE. Clinical outcomes, use of resources and costs (real costs versus SUS) were compared between established risk groups.ResultsPostoperative mortality rates of low, intermediate and high-risk EuroSCORE risk strata showed a significant linear positive correlation (EuroSCORE: 3.8%, 10%, and 25%; p < 0.0001), as well as occurrence of any postoperative complication EuroSCORE: 13.7%, 20.7%, and 30.8%, respectively; p = 0.006). Accordingly, length-of-stay increased from 20.9 days to 24.8 and 29.2 days (p < 0.001). The real cost was parallel to increased resource use according to EuroSCORE risk strata (R$ 27.116,00 ± R$ 13.928,00 versus R$ 34.854,00 ± R$ 27.814,00 versus R$ 43.234,00 ± R$ 26.009,00, respectively; p < 0.001). SUS reimbursement also increased (R$ 14.306,00 ± R$ 4.571,00 versus R$ 16.217,00 ± R$ 7.298,00 versus R$ 19.548,00 ± R$935,00; p < 0.001). However, as the EuroSCORE increased, there was significant difference (p < 0.0001) between the real cost increasing slope and the SUS reimbursement elevation per EuroSCORE risk strata.ConclusionHigher EuroSCORE was related to higher postoperative mortality, complications, length of stay, and costs. Although SUS reimbursement increased according to risk, it was not proportional to real costs.

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