Comparison of European System for Cardiac Operative Risk Evaluation (EuroSCORE) and the Society of Thoracic Surgeons(STS) score for risk prediction in Indian patients undergoing coronary artery bypass grafting

CAROTIDSCORE.RU-The first Russian computer program for risk stratification of postoperative complications of carotid endarterectomy

GOAL

Presentation of the first Russian computer program (www.carotidscore.ru) for risk stratification of postoperative complications of carotid endarterectomy (CEE).

MATERIAL AND METHODS

The present study is based on the analysis of a multicenter Russian database that includes 25,812 patients after CEE operated on from 01/01/2010 to 04/01/2022. The following types of CEE were implemented: 6814 classical CEE with plastic reconstruction of the reconstruction zone with a patch; 18,998 eversion CEE.

RESULTS

In the hospital postoperative period, 0.18% developed a lethal outcome, 0.14%-myocardial infarction, 0.35%-stroke. The combined endpoint was 0.68%. For each factor present in patients, a predictive coefficient was calculated. The prognostic coefficient was a numerical indicator reflecting the strength of the influence of each factor on the development of postoperative complications. Based on this formula, predictive coefficients were calculated for each factor present in patients in our study. The total contribution of these factors was reflected in "%" and denoted the risk of postoperative complications with a minimum value of 0% and a maximum of 100%. On the basis of the obtained calculations, a computer program CarotidSCORE was created. Its graphical interface is based on the QT framework (https://www.qt.io), which has established itself as one of the best solutions for desktop applications. It is possible not only to calculate the probability of developing a complication, but also to save all data about the patient in JSON format (for the patient's personal card and his anamnesis). The CarotidSCORE program contains 47 patient parameters, including clinical-demographic, anamnestic and angiographic characteristics. It allows you to choose one of the four types of CEE, which will provide an accurate stratification of the risk of complications for each of them in person.

CONCLUSION

CarotidSCORE (www.carotidscore.ru) is able to determine the likelihood of postoperative complications in patients undergoing CEE.

Machine learning algorithms for population-specific risk score in coronary artery bypass grafting

BACKGROUND

The aim of this study was to develop a new risk prediction score (NH Score) for patients undergoing coronary artery bypass grafting (CABG) specific to the Indian population and compare it to the Society of Thoracic Surgeon (STS) Score and the EuroSCORE II.

METHOD

The baseline features of adult patients who underwent CABG between the years 2015 and 2021 (n = 6703) were taken and split into training data (2015-2020; n = 5561) and validation data (2020-2021; n = 1142). The CatBoost algorithm was trained to predict risk scores (NH score), and the performance was tested on the validation set by Precision-Recall Curve and F1 Score. Model calibration was measured by the Brier Score, Expected Calibration Error and Maximum Calibration Error.

RESULTS

The NH score outperformed both the STS and EuroSCORE II for all outcomes. For mortality, the PR AUC for NH Score was (0.463 [95% confidence interval [CI], 0.28-0.64]) compared to 0.113 [95% CI, 0.04-0.22] for the STS score and 0.146 [95% CI, 0.06-0.31] for the EuroSCORE II (p ≪ 0.0001). With respect to morbidity NH Score was superior to the STS score (0.43 [95% CI, 0.33-0.50]) vs. (0.229 [95% CI, 0.18-0.3, p < 0.0001). The observed to the predicted ratio for NH score was superior to the STS Score and similar to EuroSCORE II. NH Score was also more accurate at predicting the risk of prolonged ventilation compared to the STS Score.

CONCLUSION

NH score shows an excellent improvement over the performance of STS score and EuroSCORE II for modelling risk predictions for patients undergoing CABG in Indian population. It warrants further validation for larger datasets.