scholarly journals Choroidal Thickness and Granulocyte Colony-Stimulating Factor in Tears Improve the Prediction Model for Coronary Artery Disease

Author(s):  
Jose Lorenzo Romero-Trevejo ◽  
Lourdes Fernandez-Romero ◽  
Josue Delgado ◽  
Erika Muñoz-Garcia ◽  
Andres Sanchez-Perez ◽  
...  

Abstract Background: Coronary artery disease (CAD) detection in asymptomatic patients still remains controversial. The aim of our study was to evaluate the usefulness of ophthalmologic findings as predictors of the presence of CAD when added to cardiovascular classic risk factors (CRF) in patients with acute coronary cardiopathy suspicion. Methods: After clinical stabilization, 96 patients with acute coronary cardiopathy suspicion were selected and divided in two groups: 69 patients with coronary lesions and 27 patients without coronary lesions. Their 192 eyes were subjected to a complete routine ophthalmologic examination. Samples of tear fluid were also collected to be used in the detection of cytokines and inflammatory mediators. Logistic regression models, receiver operating characteristic curves and their area under the curve (AUC) were analysed. Results: Suggestive predictors were choroidal thickness (CT) (OR: 1.02, 95% CI: 1.01-1.03) and tear granulocyte colony-stimulating factor (G-CSF) (OR: 0.97, 95% CI: 0.95-0.99). We obtained an AUC of 0.9646 (95% CI: 0.928-0.999) when CT and tear G-CSF were added as independent variables to the logistic regression model with cardiovascular CRF: sex, age, diabetes, high blood pressure, hypercholesterolemia, smoking habit and obesity. This AUC was significantly higher (p=0.003) than the prediction derived from the same logistic regression model without CT and tear G-CSF (AUC=0.828, 95% CI: 0.729-0.927). Conclusions: CT and tear G-CSF improved the predictive model for CAD when added to cardiovascular CRF in our sample of symptomatic patients. Subsequent studies are needed for validation of these findings in asymptomatic patients.

2014 ◽  
Vol 116 (5) ◽  
pp. 532-537 ◽  
Author(s):  
Karsten Krüger ◽  
Rainer Klocke ◽  
Julia Kloster ◽  
Sigrid Nikol ◽  
Johannes Waltenberger ◽  
...  

The study aimed to investigate whether the extent of activities of daily living (ADL) of patients after myocardial infarction affect numbers of circulating CD34+/KDR+ and CD45+/CD34+ cells, which are supposed to protect structural and functional endothelial integrity. In a cross-sectional study, 34 male coronary artery disease patients with a history of myocardial infarction were assessed for times spent per week for specific physical ADL, including basic activities (instrumental ADL), leisure time activities, and sport activities, using a validated questionnaire. Individual specific activity times were multiplied with respective specific metabolic equivalent scores to obtain levels of specific activities. Numbers of circulating CD34+/KDR+ and CD45+/CD34+ cells were analyzed by flow cytometry. Furthermore, the colony-forming capacity of CD34+ cells and the level of granulocyte colony-stimulating factor (G-CSF) in serum were measured. Analysis revealed that the extent of total activities and basic activities, as well as total activity time, were positively correlated with numbers of circulating CD34+/KDR+ cells ( r = 0.60, 0.56, and 0.55, P < 0.05). Higher levels of total activity were also associated with increased colony-forming capacity of CD34+ cells ( r = 0.54, P < 0.05) and with higher systemic levels of G-CSF ( r = 0.44, P < 0.05). These findings indicate that even ADL-related activities of coronary artery disease patients after myocardial infarction exert stimulating effects on CD34+/KDR+ cell mobilization, potentially mediated by increased G-CSF levels. This, in turn, potentially contributes to the beneficial effects of exercise on the diseased cardiovascular system.


2005 ◽  
Vol 46 (9) ◽  
pp. 1643-1648 ◽  
Author(s):  
Jonathan M. Hill ◽  
Mushabbar A. Syed ◽  
Andrew E. Arai ◽  
Tiffany M. Powell ◽  
Jonathan D. Paul ◽  
...  

Circulation ◽  
2001 ◽  
Vol 104 (17) ◽  
pp. 2012-2017 ◽  
Author(s):  
Christian Seiler ◽  
Tilmann Pohl ◽  
Kerstin Wustmann ◽  
Damian Hutter ◽  
Pierre-Alain Nicolet ◽  
...  

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xinyun Liu ◽  
Jicheng Jiang ◽  
Lili Wei ◽  
Wenlu Xing ◽  
Hailong Shang ◽  
...  

Abstract Background Machine learning (ML) can include more diverse and more complex variables to construct models. This study aimed to develop models based on ML methods to predict the all-cause mortality in coronary artery disease (CAD) patients with atrial fibrillation (AF). Methods A total of 2037 CAD patients with AF were included in this study. Three ML methods were used, including the regularization logistic regression, random forest, and support vector machines. The fivefold cross-validation was used to evaluate model performance. The performance was quantified by calculating the area under the curve (AUC) with 95% confidence intervals (CI), sensitivity, specificity, and accuracy. Results After univariate analysis, 24 variables with statistical differences were included into the models. The AUC of regularization logistic regression model, random forest model, and support vector machines model was 0.732 (95% CI 0.649–0.816), 0.728 (95% CI 0.642–0.813), and 0.712 (95% CI 0.630–0.794), respectively. The regularization logistic regression model presented the highest AUC value (0.732 vs 0.728 vs 0.712), specificity (0.699 vs 0.663 vs 0.668), and accuracy (0.936 vs 0.935 vs 0.935) among the three models. However, no statistical differences were observed in the receiver operating characteristic (ROC) curve of the three models (all P > 0.05). Conclusion Combining the performance of all aspects of the models, the regularization logistic regression model was recommended to be used in clinical practice.


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