Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.

Background: People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at significantly high risk of cardiovascular events (CVEs), however the predictive performance of traditional risk prediction methods are limited.

Methods: We utilised machine learning (ML) model to predict CVEs in persons with DM and CKD from the Silesia Diabetes-Heart Project, a routine standard of care dataset. CVEs were defined as composite of nonfatal myocardial infarction, new onset heart failure, nonfatal stroke, incident atrial fibrillation, undergoing percutaneous coronary intervention or coronary artery bypass grafting, hospitalisation or death due to cardiovascular disease.

Metabolically "extremely unhealthy" obese and non-obese people with diabetes and the risk of cardiovascular adverse events: the Silesia Diabetes - Heart Project.

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).

Cardio-reno-vascular protection in type 2 diabetes mellitus: new insights into pharmacotherapeutic management.

Introduction: From 2008 and following the withdrawal of rosiglitazone, obligatory cardiovascular outcomes trials are performed for glucose lowering drugs introduced to the market to ensure their cardiovascular (CV) safety. Paradoxically, these studies have demonstrated CV safety but also shown additional cardio-reno-vascular protection of some therapeutic agents. Additionally, nonsteroidal mineralocorticoid receptor antagonists (ns-MRA) have emerged as novel drugs for cardio - and renoprotection in type 2 diabetes (T2D) and chronic kidney disease (CKD).

Machine learning identification of risk factors for heart failure in patients with diabetes mellitus with metabolic dysfunction associated steatotic liver disease (MASLD): the Silesia Diabetes-Heart Project.

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.

Machine Learning Predicts Cardiovascular Events in Patients With Diabetes: The Silesia Diabetes-Heart Project.

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.

The Review of Insulin Pens-Past, Present, and Look to the Future.

Currently, there are about 150-200 million diabetic patients treated with insulin globally. The year 2021 is special because the 100th anniversary of the insulin discovery is being celebrated. It is a good occasion to sum up the insulin pen technology invention and improvement which are nowadays the leading mode of an insulin delivery.

Bone status in adolescents and young adults with type 1 diabetes: a 10-year longitudinal study.

Introduction: This study presents a 10-year longitudinal assessment of bone status in adolescents and young adults with type 1 diabetes (T1D).

Material And Methods: Thirty-two patients (12 female, aged 20.5 ± 3.