Prediction of Postcoronary Artery Bypass Grafting Atrial Fibrillation: POAFRiskScore Tool.

BACKGROUND: Atrial fibrillation (AF), a condition that might occur after a heart bypass procedure, has caused differing estimates of its occurrence and risk. The current study analyses the possible risk factors of post-coronary artery bypass grafting (post-CABG) AF (postoperative AF [POAF]) and presents a software for preoperative POAF risk prediction. METHODS: This retrospective research was performed on 1,667 patients who underwent CABG surgery using the hospital database. The associations between the variables of the patients and AF risk factors after CABG were examined using multivariable logistic regression (LR) after preprocessing the relevant data. The tool was designed to predict POAF risk using Shiny, an R package, to develop a web-based software. RESULTS: The overall proportion of post-CABG AF was 12.2%. According to the results of univariate tests, in terms of age (p < 0.001), blood urea nitrogen (p = 0.005), platelet (p < 0.001), triglyceride (p = 0.0026), presence of chronic obstructive pulmonary disease (COPD; p = 0.01), and presence of preoperative carotid artery stenosis (PCAS; p < 0.001), there were statistically significant differences between the POAF and non-POAF groups. Multivariable LR analysis disclosed the independent risk factors associated with POAF: PCAS (odds ratio [OR] = 2.360; p = 0.028), COPD (OR = 2.243; p = 0.015), body mass index (OR = 1.090; p = 0.006), age (OR = 1.054, p < 0.001), and platelet (OR = 0.994, p < 0.001). CONCLUSION: The experimental findings from the current research demonstrate that the suggested tool (POAFRiskScore v.1.0) can help clinicians predict POAF risk development in the preoperative period after validated on large sample(s) that can represent the related population(s). Simultaneously, since the updated versions of the proposed tool will be released periodically based on the increases in data dimensions with continuously added new samples and related factors, more robust predictions may be obtained in the subsequent stages of the current study in statistical and clinical terms.

Comparison of left atrial volume and function in non-dipper versus dipper hypertensives: A real-time three-dimensional echocardiography study.

OBJECTIVE: Non-dipper hypertension is associated with an increased cardiovascular morbidity and mortality. Besides this, the left atrial (LA) size and functions are accepted to be prognostic factors in various cardiovascular diseases. In this study, we aimed to evaluate the effect of nondipper hypertension on LA volume and functions using real-time three-dimensional echocardiography (RT3-DE). METHODS: Forty dipper and 52 non-dipper hypertensives enrolled in this prospective cross-sectional study. Patients with any comorbidities that have a potential for causing structural cardiac alterations were excluded. Two-dimensional echocardiography (2-DE) and RT3-DE were performed to assess LA volumes and functions. The statistical tests used in this study were Shapiro-Wilk's test, Student's t-test, Mann-Whitney U test, chi-square test, Spearman's test, and Pearson's correlation test. RESULTS: LA minimal volume, LA volume before LA contraction, and LA total systolic volume were higher in non-dipper hypertensives than in dipper hypertensives (p<0.001, p=0.003, and p=0.03, respectively). Only, the 2-DE measurements of interventricular septal thickness and E/Em ratio were higher in non-dipper hypertensives (p=0.001 and p=0.03, respectively). There was a moderate correlation between LA minimal volume and LA volume before LA contraction with E/Em (r=0.31, p=0.007 and r=0.32, p=0.005, respectively). CONCLUSION: Although LA volume and passive LA systolic functions measured by RT3-DE are significantly increased in non-dipper hypertensives, the alterations in active LA systolic functions are not prominent. RT-3DE may be used to define LA volume and function alterations in conditions that have capabilities of adverse cardiac remodeling such as systemic hypertension.

Predicting coronary artery disease using different artificial neural network models.

OBJECTIVE: Eight different learning algorithms used for creating artificial neural network (ANN) models and the different ANN models in the prediction of coronary artery disease (CAD) are introduced. METHODS: This work was carried out as a retrospective case-control study. Overall, 124 consecutive patients who had been diagnosed with CAD by coronary angiography (at least 1 coronary stenosis > 50% in major epicardial arteries) were enrolled in the work. Angiographically, the 113 people (group 2) with normal coronary arteries were taken as control subjects. Multi-layered perceptrons ANN architecture were applied. The ANN models trained with different learning algorithms were performed in 237 records, divided into training (n=171) and testing (n=66) data sets. The performance of prediction was evaluated by sensitivity, specificity and accuracy values based on standard definitions. RESULTS: The results have demonstrated that ANN models trained with eight different learning algorithms are promising because of high (greater than 71%) sensitivity, specificity and accuracy values in the prediction of CAD. Accuracy, sensitivity and specificity values varied between 83.63%-100%, 86.46%-100% and 74.67%-100% for training, respectively. For testing, the values were more than 71% for sensitivity, 76% for specificity and 81% for accuracy. CONCLUSIONS: It may be proposed that the use of different learning algorithms other than backpropagation and larger sample sizes can improve the performance of prediction. The proposed ANN models trained with these learning algorithms could be used a promising approach for predicting CAD without the need for invasive diagnostic methods and could help in the prognostic clinical decision.

[The comparison of logistic regression model selection methods for the prediction of coronary artery disease]. / Koroner arter hastaliginin tahmininde lojistik regresyon modeli seçim yöntemlerinin karsilastirilmasi.

OBJECTIVE: In this study, logistic regression model selection methods were compared for the prediction of coronary artery disease (CAD). METHODS: Coronary artery disease data were taken from 237 consecutive people who had been applied to Inönü University Faculty of Medicine, Department of Cardiology. Logistic regression model selection methods were applied to CAD data containing continuous and discrete independent variables. Goodness of fit test was performed by Hosmer-Lemeshow statistic. Likelihood-ratio statistic was used to compare the estimated models. RESULTS: Each of the logistic regression model selection methods had sensitivity, specificity and accuracy rates greater than 91.9%. Hosmer-Lemeshow statistic showed that the model selection methods were successful in the description of CAD data. Related factors with CAD were identified and the results were evaluated. CONCLUSION: Logistic regression model selection methods were very successful in the prediction of CAD. Stepwise model selection methods were better than Enter method based on Likelihood-ratio statistic for the prediction of CAD. Age, diabetes mellitus, hypertension, family history, smoking, low-density lipoprotein, triglyceride, stress and obesity variables may be used for the prediction of CAD.