Background and Objectives: Heart failure is a chronic disease with a high risk of mortality and morbidity. In these patients, inflammatory markers have been shown to be associated with cardiovascular adverse outcomes and disease progression. To investigate the relationships between eosinophil indices and major cardiovascular events (MACE) in patients with acute decompensated heart failure (ADHF) with reduced ejection fraction. Materials and Methods: A total of 395 consecutive patients admitted to the intensive care unit (ICU) with ADHF and reduced ejection fraction between January 2017 and December 2021 were enrolled in this retrospective study. MACE was defined as the composite of death and re-hospitalization for ADHF within 6 months of index hospitalization. All-cause mortality and MACE were assessed with respect to relationships with eosinophil indices, including neutrophil-to-eosinophil ratio (NER), leukocyte-to-eosinophil ratio (LER), eosinophil-to-lymphocyte ratio (ELR), and eosinophil-to-monocyte ratio (EMR). Results: NER and LER were significantly higher in subjects with MACE. Absolute eosinophil, lymphocyte and basophil count, hemoglobin, serum Na+, albumin, and CRP, and EMR and ELR were significantly lower in subjects with MACE compared to those without. NT-proBNP (OR: 1.682, 95% CI: 1.106–2.312, p = 0.001), Na+ (OR: 0.932, 95% CI: 0.897–0.969, p < 0.001), NER (OR: 2.740, 95 % CI: 1.797–4.177, p < 0.001), LER (OR: 2.705, 95% CI: 1.752–4.176, p < 0.001), EMR (OR:1.654, 95% CI 1.123–2.436, p = 0.011), ELR (OR: 2.112, 95% CI 1.424–3.134, p < 0.001), and eosinophil count (OR: 1.833, 95% CI 1.276–2.635) were independent predictors for development of MACE. Conclusions: Patients with ADHF and reduced ejection fraction who developed MACE within the first six months of index hospitalization had lower levels of absolute eosinophil and lymphocyte counts, and EMR and ELR values, whereas NER and LER were higher compared to those without MACE. The eosinophil indices were independently associated with mortality and MACE development. The eosinophil indices may be used to estimate MACE likelihood with acceptable sensitivity and specificity.
Ramadan interferes with circadian rhythms mainly by disturbing the routine patterns of feeding and smoking. The objective of this study was to investigate the circadian pattern of ST elevation acute myocardial infarction (STEMI) during the month of Ramadan. We studied consecutive STEMI patients 1 month before and after Ramadan (non-Ramadan group-NRG) and during Ramadan (Ramadan group-RG). The RG group was also divided into two groups, based on whether they chose to fast: fasting (FG) and non-fasting group (NFG). The time of STEMI onset was compared. A total of 742 consecutive STEMI patients were classified into 4 groups by 6 h intervals according to time-of-day at symptom onset. No consistent circadian variation in the onset of STEMI was observed both between the RG ( P = .938) and NRG ( P = .766) or between the FG ( P = .232) and NFG ( P = .523). When analyzed for subgroups of the study sample, neither smoking nor diabetes showed circadian rhythm. There was a trend towards a delay from symptom onset to hospital presentation, particularly at evening hours in the RG compared with the control group. In conclusion, there was no significant difference in STEMI onset time, but the time from symptom onset to hospital admission was significantly delayed during Ramadan.
Coronary Artery Disease (CAD) occurs when the coronary vessels become hardened and narrowed, limiting blood flow to the heart muscles. It is the most common type of heart disease and has the highest mortality rate. Early diagnosis of CAD can prevent the disease from progressing and can make treatment easier. Optimal treatment, in addition to the early detection of CAD, can improve the prognosis for these patients. This study proposes a new method for non-invasive diagnosis of CAD using iris images. In this study, iridology, a method of analyzing the iris to diagnose health conditions, was combined with image processing techniques to detect the disease in a total of 198 volunteers, 94 with CAD and 104 without. The iris was transformed into a rectangular format using the integral differential operator and the rubber sheet methods, and the heart region was cropped according to the iris map. Features were extracted using wavelet transform, first-order statistical analysis, a Gray-Level Co-Occurrence Matrix (GLCM), and a Gray Level Run Length Matrix (GLRLM). The model’s performance was evaluated based on accuracy, sensitivity, specificity, precision, score, mean, and Area Under the Curve (AUC) metrics. The proposed model has a 93% accuracy rate for predicting CAD using the Support Vector Machine (SVM) classifier. With the proposed method, coronary artery disease can be preliminarily diagnosed by iris analysis without needing electrocardiography, echocardiography, and effort tests. Additionally, the proposed method can be easily used to support telediagnosis applications for coronary artery disease in integrated telemedicine systems.
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