Sudden Cardiac Death-Etiology, Risk Factors and Demographic Characteristics: An Extensive Study of 1618 Forensic Autopsies.

BACKGROUND: Sudden cardiac death (SCD) is a major public health concern worldwide, affecting all age and social groups. METHODS: In this retrospective study, of the 8265 autopsies performed in the Institute of Legal Medicine, 1618 cases of SCD were included. The aim of this study is to identify demographic characteristics, etiological factors, epidemiological characteristics and risk factors that lead to SCD. RESULTS: The highest incidence of SCD was in age group 40-69 years (65.0%), 71.6% of this age group being men. Of the total number, 32.1% (520) occurred in the emergency room. The most common cause of sudden death is represented by coronary atherosclerotic disease, reported in 89.8% (1453) of cases, tricoronary lesions being found in 60% (870) of cases. Etiological factors of SCD encountered during autopsies were acute myocardial infarction in 13.9% (225), dilated cardiomyopathy 43.9% (710), cardiac hypertrophy 579 (36.07%), pericarditis 1.9% (30), myocarditis 1.73% (28) and adipositas cordis 5% (81). Along with epicardial fat and BMI, alcohol consumption was recorded in 17.9% (290), this being a potential trigger. CONCLUSIONS: Based on forensic autopsy and histological findings, a wide variety of factors are involved in the etiopathogenesis of SCD, some of which can be eliminated through preventive measures implemented early.

Serum Levels of Adipolin and Adiponectin and Their Correlation with Perinatal Outcomes in Gestational Diabetes Mellitus.

Objectives: This study aimed to investigate the serum level of adipolin and adiponectin in healthy pregnant women and pregnant women with gestational diabetes mellitus (GDM) during the second trimester, the prepartum period, and in the newborns of these patients. Methods: A total of 55 women diagnosed with GDM and 110 healthy pregnant women were included in this study. Pearson's and Spearman's correlation coefficients were calculated to determine the association of adipolin and adiponectin with anthropometric markers of obesity (body mass index (BMI), mid-upper arm circumference (MUAC), tricipital skinfold thickness (TST)), inflammation markers (neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), C-reactive protein (CRP)), and maternal glucose homeostasis parameters (fasting glucose, insulin, C peptide, glycosylated hemoglobin A1c (HbA1c), Insulin Resistance-Homeostatic Model Assessment (IR HOMA)). Results: There were no statistical differences between the adipolin value in patients with GDM compared to healthy patients (p = 0.65 at diagnosis and p = 0.50 prepartum) and in newborns from mothers with GDM compared to healthy mothers (p = 0.24). Adipolin levels are significantly higher in patients with GDM who gave birth via cesarean section (p = 0.01). In patients with GDM, the adipolin level correlates positively with HgA1c in the prepartum period. We found a positive correlation between the maternal adipolin values at diagnosis and prepartum and neonatal adipolin (respectively: r = 0.556, p = 0.001; r = 0.332, p = 0.013). Adiponectin levels were significantly lower in patients with GDM at diagnosis and prepartum (p = 0.0009 and p = 0.02), but their levels increased prepartum (5267 ± 2114 ng/mL vs. 6312 ± 3150 ng/mL p = 0.0006). Newborns of mothers with GDM had lower adiponectin levels than newborns of healthy mothers (p < 0.0001). The maternal adiponectin value correlates negatively with maternal BMI, MUAC, and IR HOMA in both groups at diagnosis and prepartum. There were no differences between the groups in terms of cesarean rate (p > 0.99). The relative risk of occurrence of adverse events in patients with GDM compared to healthy ones was 2.15 (95% CI 1.416 to 3.182), and the odds ratio for macrosomia was 4.66 (95% CI 1.591 to 12.69). Conclusions: There was no difference in adipolin levels between mothers with GDM and healthy mothers during the second trimester and the prepartum period. Adipolin is known to enhance insulin sensitivity and reduce inflammation, but unlike adiponectin, it does not appear to contribute to the development of GDM.

Interrelationship between Altered Left Ventricular Ejection Fraction and Nutritional Status in the Post-Acute Myocardial Infarction Patient.

There is currently little research on the effects of reduced left ventricular ejection fraction and altered nutritional status in patients with acute myocardial infarction. We therefore examined the interrelationship between the parameters of left ventricular dysfunction after acute myocardial infarction and changes in the Geriatric Nutrition Risk Index (GNRI) and the Nutrition Status Control Index (CONUT). Based on the evidence, frailty is considered to be an important factor affecting the prognosis of cardiovascular disease, so it is important to detect malnutrition early to prevent adverse cardiovascular events. This study was an observational, prospective study that included a total of 73 subjects who presented at the 3-month AMI follow-up. All subjects were subjected to laboratory tests and the groups were divided as follows: group 1, in which we calculated the CONUT score, (CONUT < 3 points, n = 57) patients with normal nutritional status and patients with moderate to severe nutritional deficiency (CONUT ≥ 3, n = 16). In group 2, the GNRI score was calculated and out of the 73 patients we had: GNRI ≥ 98, n = 50, patients with normal nutritional status, and GNRI < 98, n = 23, patients with altered nutritional status. The results of this study showed that we had significant differences between LVEF values at 3 months post-infarction where, in the CONUT group, patients with altered nutritional status had lower LVEF values (46.63 ± 3.27% versus 42.94 ± 2.54%, p < 0.001) compared to CONUT < 3. Also, in the GNRI group, we had lower LVEF values in patients with impaired nutritional status (46.48 ± 3.35% versus 44.39 ± 3.35%, p = 0.01). It can be seen that LVEF values are improved at 3 months post infarction in both groups, in patients with impaired nutritional status and in patients with good nutritional status. Patients with impaired nutritional status have lower ejection fraction and worse outcomes in both the CONUT and GNRI groups at 3 months post acute myocardial infarction.

Nutritional Status and Recurrent Major Cardiovascular Events Following Acute Myocardial Infarction-A Follow-Up Study in a Primary Percutaneous Coronary Intervention Center.

BACKGROUND: Acute myocardial infarction is often accompanied by malnutrition, which is associated with an imbalance between catabolic and anabolic processes. This ultimately leads to cardiac cachexia, which worsens the patient's prognosis. We aimed to assess the correlation between nutritional status, assessed using the controlling nutritional status (CONUT) score, and the rate of major cardiovascular adverse events (MACE). METHODS: The present investigation was a non-randomized, prospective, observational study in which 108 patients with acute myocardial infarction were included. Nutritional status was assessed using the CONUT score. Based on the CONUT score, the patients were divided as follows: Group 1-normal or mild nutritional status (CONUT < 3 points, n = 76), and Group 2-moderate to severe nutritional deficiency (CONUT ≥ 3 points, n = 32). Demographic, echocardiographic, and laboratory parameters were obtained for all patients, as well as the MACE rate at 1 and 3 months of follow-up. RESULTS: The MACE occurred more frequently in patients with impaired nutritional status at both 1-month follow-up (46.9% versus 9.2%; p < 0.0001) and 3-month follow-up (68.8% versus 10.5%; p < 0.0001). In terms of cardiovascular events, patients with poor nutritional status, with a CONUT score ≥ 3, presented more frequent non-fatal myocardial infarction, stroke, revascularization procedure, and ventricular arrhythmia. Also, the number of cardiovascular deaths was higher in the undernourished group. CONCLUSIONS: This study found that patients with poor nutritional status experienced inflammatory status, frailty, and cardiovascular events more often than those with normal nutritional status at 1-month and 3-month follow-up after an acute myocardial infarction.

Machine learning based assessment of preclinical health questionnaires.

BACKGROUND: Within modern health systems, the possibility of accessing a large amount and a variety of data related to patients' health has increased significantly over the years. The source of this data could be mobile and wearable electronic systems used in everyday life, and specialized medical devices. In this study we aim to investigate the use of modern Machine Learning (ML) techniques for preclinical health assessment based on data collected from questionnaires filled out by patients. METHOD: To identify the health conditions of pregnant women, we developed a questionnaire that was distributed in three maternity hospitals in the Mureș County, Romania. In this work we proposed and developed an ML model for pattern detection in common risk assessment based on data extracted from questionnaires. RESULTS: Out of the 1278 women who answered the questionnaire, 381 smoked before pregnancy and only 216 quit smoking during the period in which they became pregnant. The performance of the model indicates the feasibility of the solution, with an accuracy of 98 % confirmed for the considered case study. CONCLUSION: The proposed solution offers a simple and efficient way to digitize questionnaire data and to analyze the data through a reduced computational effort, both in terms of memory and computing power used.