Publications by authors named "Mirela Hendel"
Background: Cachexia, defined as the combination of weight loss, weakness, fatigue, anorexia and abnormal biochemical markers based on Evans' criteria, is known to exacerbate the prognosis of heart failure (HF) patients. This systematic review and meta-analysis investigates the prognostic impact and prevalence of cachexia, as defined by Evans' criteria, in patients with HF.
Methods: PubMed, Cochrane Library, Scopus and Web of Science were searched from inception until December 2023, including HF patients for whom the Evans' criteria were applied to explore the prevalence and prognostic impact of cachexia.
Article Synopsis
- Metformin, the most commonly prescribed medication for type 2 diabetes, is associated with gastrointestinal (GI) adverse events that can limit its use in patients.
- A systematic review and meta-analysis of 21 studies aimed to determine the prevalence of these GI adverse events, finding that diarrhea (6.9%) and bloating (6.2%) were the most common issues.
- Extended release (XR) metformin was shown to have lower incidences of GI issues, suggesting it's better tolerated than the immediate release (IR) formulation.
Background: There is a growing burden of non-obese people with diabetes mellitus (DM). However, their cardiovascular risk (CV), especially in the presence of cardiovascular-kidney-metabolic (CKM) comorbidities is poorly characterised. The aim of this study was to analyse the risk of major CV adverse events in people with DM according to the presence of obesity and comorbidities (hypertension, chronic kidney disease, and dyslipidaemia).
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- Cardiac autonomic neuropathy (CAN) is a serious complication in diabetes that can lead to cardiovascular events, but it's not commonly diagnosed due to time constraints.
- A study explored using AI and deep learning to analyze retinal images from diabetic patients for diagnosing CAN, successfully applying techniques like ResNet 18 and Multiple Instance Learning.
- The results showed high accuracy, with the AI model identifying 93% of CAN cases and 89% of non-CAN cases, particularly excelling in distinguishing severe CAN stages, indicating a promising diagnostic tool for clinical practice.
Article Synopsis
- The study aimed to develop machine learning algorithms to analyze electrocardiograms (ECGs) for diagnosing cardiac autonomic neuropathy (CAN) in diabetic patients.
- Researchers utilized motif and discord extraction techniques along with long short-term memory networks to evaluate the effectiveness of these methods using various performance metrics via 10-fold cross-validation.
- The findings showed high accuracy in detecting severe CAN (dsCAN) with an accuracy of 0.92, while detecting any stage of CAN had a lower accuracy of 0.65, suggesting the potential of machine learning in improving CAN diagnosis and screening.
Article Synopsis
- MASLD is often underdiagnosed in diabetes patients, increasing their cardiovascular disease risk, prompting the need for effective detection methods.
- Researchers developed machine learning models to assess the risk of MASLD by analyzing 8 key patient parameters, achieving a high sensitivity and specificity in identifying affected individuals.
- The study's findings indicate that this ML approach can improve risk stratification and prevention strategies for diabetes patients potentially facing MASLD.
Background: Diabetes mellitus (DM), heart failure (HF) and metabolic dysfunction associated steatotic liver disease (MASLD) are overlapping diseases of increasing prevalence. Because there are still high numbers of patients with HF who are undiagnosed and untreated, there is a need for improving efforts to better identify HF in patients with DM with or without MASLD. This study aims to develop machine learning (ML) models for assessing the risk of the HF occurrence in patients with DM with and without MASLD.
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- * Researchers analyzed data from 238 diabetic patients, achieving a predictive accuracy (AUC) of 0.86, indicating strong potential for identifying those at risk for CVD.
- * The findings suggest that older patients, particularly those on ACE inhibitors or beta-blockers with a history of foot ulcers, have a significantly higher risk of developing overt CVD.
We aimed to develop a machine learning (ML) model for predicting cardiovascular (CV) events in patients with diabetes (DM). This was a prospective, observational study where clinical data of patients with diabetes hospitalized in the diabetology center in Poland (years 2015-2020) were analyzed using ML. The occurrence of new CV events following discharge was collected in the follow-up time for up to 5 years and 9 months.
View Article and Find Full Text PDFType 2 diabetes mellitus (T2DM) and diminished myocardial perfusion increase the risk of heart failure (HF) and/or all-cause mortality during 6-year follow up following primary percutaneous coronary intervention (pPCI) for ST elevation myocardial infarction (STEMI). The aim of the present study was to evaluate the impact of myocardial perfusion on infarct size and left ventricular ejection fraction (LVEF) in patients with T2DM and STEMI treated with pPCI. This is an ancillary analysis of an observational cohort study of T2DM patients with STEMI.
View Article and Find Full Text PDFBackground: Nonalcoholic fatty liver disease is associated with an increased cardiovascular disease (CVD) risk, although the exact mechanism(s) are less clear. Moreover, the relationship between newly redefined metabolic-associated fatty liver disease (MAFLD) and CVD risk has been poorly investigated. Data-driven machine learning (ML) techniques may be beneficial in discovering the most important risk factors for CVD in patients with MAFLD.
View Article and Find Full Text PDFIntroduction: Metformin is the first choice drug in the treatment of type 2 diabetes mellitus but its administration may be linked to gastrointestinal adverse events limiting its use.
Objectives: The objective of this systematic review and meta-analysis was to assess the risk of gastrointestinal adverse events related to metformin use in patients with type 2 diabetes treated with metformin.
Methods: PUB MED/CINAHL/Web of Science/Scopus were searched from database inception until 08.
Sodium-glucose cotransporter 2 inhibitors (SGLT2i) have been recognized as potent antioxidant agents. Since SGLT2i are nephroprotective drugs, we aimed to examine the urine antioxidant status in patients with type 2 diabetes mellitus (T2DM). One hundred and one subjects participated in this study, including 37 T2DM patients treated with SGLT2i, 31 T2DM patients not using SGLT2i, and 33 healthy individuals serving as a control group.
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