The development of novel score model as a predictor of in-hospital mortality of adult cardiac surgery patients in Indonesia [version 1; peer review: awaiting peer review]

Introduction In recent years, the number of cardiac surgeries has been on the rise. Several predictive models have been developed to assess the risk of mortality for these patients. EuroSCORE II, a widely used model, demonstrates good discrimination and calibration for the European population, but its accuracy may vary in the Indonesian population. The purpose of this study was to develop a scoring model tailored to Indonesian population, which may have better accuracy in assessing in-hospital mortality risk among adult cardiac surgery patients. Method A retrospective study was conducted using medical records of adult cardiac surgery patients from four participating hospitals. Potential risk factors were included as variables and analyzed using bivariate and multivariable logistic regression with the L-backward method. Bootstrapping was applied to enhance the model's validity. Receiver operating characteristic (ROC) curves were created for each model, and the area under the curve (AUC) was calculated to assess discrimination ability, while the Hosmer-Lemeshow test was used to evaluate calibration. Results We extracted data from 4,875 patients, with a mean age of 50.41 years, and most patients were men (63.1%). The majority of patients were in NYHA class I-II. The in-hospital mortality rate was 6.5%. From 62 potential variables, 13 variables were included in the final model. Our new model demonstrated strong discrimination and calibration (AUC 0.7564; Hosmer-Lemeshow p = 0.9510). Conclusion The newly developed scoring model exhibited good discrimination and calibration, making it a promising tool for predicting in-hospital mortality in adult cardiac surgery patients in Indonesia.