Potential of machine learning methods in operational risk stratification in patients with coronary artery disease scheduled for coronary bypass surgery

Aim. To develop and evaluate the effectiveness of models for predicting mortality after coronary bypass surgery, obtained using machine learning analysis of preoperative data.Material and methods. As part of a cohort study, a retrospective prediction of in-hospital mortality after coronary artery bypass grafting (CABG) was performed in 2182 patients with stable coronary artery disease. Patients were divided into 2 following samples: learning (80%, n=1745) and training (20%, n=437). The initial ratio of surviving (n=2153) and deceased (n=29) patients in the total sample indicated a pronounced class imbalance, and therefore the resampling method was used in the training sample. Five machine learning (ML) algorithms were used to build predictive risk models: Logistic regression, Random Forrest, CatBoost, LightGBM, XGBoost. For each of these algorithms, cross-validation and hyperparameter search were performed on the training sample. As a result, five predictive models with the best parameters were obtained. The resulting predictive models were applied to the learning sample, after which their performance was compared in order to determine the most effective model.Results. Predictive models implemented on ensemble classifiers (CatBoost, LightGBM, XGBoost) showed better results compared to models based on logistic regression and random forest. The best quality metrics were obtained for CatBoost and LightGBM based models (Precision — 0,667, Recall — 0,333, F1-score — 0,444, ROC AUC — 0,666 for both models). There were following common high-ranking parameters for deciding on the outcome for both models: creatinine and blood glucose levels, left ventricular ejection fraction, age, critical stenosis (>70%) of carotid arteries and main lower limb arteries.Conclusion. Ensemble machine learning methods demonstrate higher predictive power compared to traditional methods such as logistic regression. The prognostic models obtained in the study for preoperative prediction of in-hospital mortality in patients referred for CABG can serve as a basis for developing systems to support medical decision-making in patients with coronary artery disease

Bulk crystallisation of LaBGeO5 glass produced by Pr2O3. A DTA study

The nonisothermal crystallisation of La(1-x)PrxBGeO5 glasses with 0.00 less than or equal to x less than or equal to 0.28 has been studied by differential thermal analysis. The effects on the glass transition temperature and on devitrification of substitution of La2O3 by Pr2O3 were evaluated. The influence on devitrification mechanisms of the specific surface area of samples and nucleation heat treatments are also reported

Non-isothermal crystallization of lanthanum-borate glasses

A non-isothermal crystn. of 3 La borate glasses of the La3O3.nB2O3 (n = 2, 3, 4) compns. were studied by differential thermal anal. The compns. corresponding to n = 2 and 4 lie in the stable immiscibility area and outside the glass-forming range, while the glass corresponding to n = 3 is situated in the middle of that range. The crystal phase formed the n = 2 glass with heating to the 1st of 2 DTA exo peaks was the aragonite form of LaBO3; that in n = 3 and 4 was LaB3O6. Heating the glasses to the 2nd exo peak resulted in no new phases obsd. in the n = 4 glass while an unidentified phase was obsd. in n = 2 and 3 glasses

Crystallization of the K2O.Nb2O5.2SiO2 glass: evidences for existence of bulk nanocrystalline structure

The crystallization of the K2O . Nb2O5. 2SiO2 (KNS-50) glass has been studied by DTA, XRD, SEM and FTIR. The as-quenched glass crystallizes in two steps during a DTA run. In the first step only potassium niobates crystallize while at the higher temperature step KNbSi207 ferroelectric crystallizes. Heat treatments of the bulk glass at temperatures not far from T-g produce a nanocrystalline structure penetrating the whole volume of the glass and clearly observed by high resolution SEM as isometric nuclei. The observed nanostructure is the result of two processes occurring during the heat treatment amorphous phase separation followed by the crystallization of the high alkali and niobium content glassy phase. The heat-treated glass, in which the nanostructure is grown, shows an additional DTA peak at about 900 degrees C related to the crystallization of an unknown crystalline phase, nor obtained in the as-quenched glass. The existence of the nanostructure clarifies the origin of SHG discovered recently in potassium niobium silicate glasses. (C) 2000 Elsevier Science B.V. All rights reserved

Structure and crystallization behavior of glasses in the BaO-B2O3-Al2O3system

The structure and the devitrification behavior of barium aluminoborate glasses, examined by Fourier transform infrared spectroscopy, differential thermal analysis and X-ray diffraction are reported. The glass compositions are expressed by the general formula: (50 - x/2)BaO .(50 - x/2)B2O3. xAl(2)O(3) with x = 2, 4 and 8. All the as-quenched glasses were phase separated. Aluminum acting as glass former gives a more polymerized structure increasing the glass stability with respect to the devitrification process. Surface crystallization was found to be dominant for all glasses, forming beta BaB2O4 nanocrystals as the main crystalline phase. (C) 1999 Elsevier Science B.V. All rights reserved