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Background: The performance of traditional scores is significantly limited to predict mortality in high-risk cardiac surgery. The aim of this study was to compare the performance of STS, ESII and HiriSCORE models in predicting mortality in high-risk patients undergoing CABG., Methods: Cross-sectional analysis in the international prospective database of high-risk patients: HiriSCORE project. We evaluated 248 patients with STS or ESII (5-10%) undergoing CABG in 8 hospitals in Brazil and China. The main outcome was mortality, defined as all deaths occurred during the hospitalization in which the operation was performed, even after 30 days. Five variables were selected as predictors of mortality in this cohort of patients. The model's performance was evaluated through the calibration-in-the-large and the receiver operating curve (ROC) tests., Results: The mean age was 69.90±9.45, with 52.02% being female, 25% of the patients were on New York Heart Association (NYHA) class IV and 49.6% had Canadian Cardiovascular Society (CCS) class 4 angina, and 85.5% had urgency or emergency status. The mortality observed in the sample was 13.31%. The HiriSCORE model showed better calibration (15.0%) compared to ESII (6.6%) and the STS model (2.0%). In the ROC curve, the HiriSCORE model showed better accuracy (ROC = 0.74) than the traditional models STS (ROC = 0.67) and ESII (ROC = 0.50)., Conclusion: Traditional models were inadequate to predict mortality of high-risk patients undergoing CABG. However, the HiriSCORE model was simple and accurate to predict mortality in high-risk patients., Competing Interests: The authors have no conflict of interest to declare in relation to this work.

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., Competing Interests: We know of no conflicts of interest associated with this publication.

Background: Mortality 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., Objectives: Develop a novel predictive model focused on mortality prediction among patients undergoing cardiac surgery secondary to rheumatic valve conditions., Methods: We 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)., Results: A 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., Conclusions: The RheSCORE model outperformed pre-existing scores in a sample of patients with rheumatic cardiac disease., Competing Interests: The authors have declared that no competing interests exist.