A transparent machine learning algorithm uncovers HbA1c patterns associated with therapeutic inertia in patients with type 2 diabetes and failure of metformin monotherapy.

Aims: This study aimed to identify and categorize the determinants influencing the intensification of therapy in Type 2 Diabetes (T2D) patients with suboptimal blood glucose control despite metformin monotherapy.

Methods: Employing the Logic Learning Machine (LLM), an advanced artificial intelligence system, we scrutinized electronic health records of 1.5 million patients treated in 271 diabetes clinics affiliated with the Italian Association of Medical Diabetologists from 2005 to 2019.

Digital technology and healthcare delivery in insulin-treated adults with diabetes: a proposal for analysis of self-monitoring blood glucose patterns using a dedicated platform.

Purpose: A remote platform for diabetes care (Roche Diabetes® Care Platform, RDCP) has been developed that allows combined face-to-face consultations and remote patient monitoring (RPM).

Methods: A dedicated flowchart is proposed as a clinical approach to help healthcare professionals in the appropriate interpretation of structured self-monitoring blood glucose data, as visualized on the RDCP during the visits, and in the optimal management of patients using the integrated RDCP-RPM tools.

Results: The platform organizes patterns in different blocks: (i) hypoglycemia; (ii) hyperglycemia; (iii) blood glucose variability; (iv) treatment adherence, which identifies a possible individual pattern according to glycemic control challenges, potential causal factors, and behavioral type patterns.

Achieving Good Metabolic Control Without Weight Gain with the Systematic Use of GLP-1-RAs and SGLT-2 Inhibitors in Type 2 Diabetes: A Machine-learning Projection Using Data from Clinical Practice.

Purpose: Recently, the 2022 American Diabetes Association and European Association for the Study of Diabetes (ADA-EASD) consensus report stressed the importance of weight control in the management of patients with type 2 diabetes; weight control should be a primary target of therapy. This retrospective analysis evaluated, through an artificial-intelligence (AI) projection of data from the AMD Annals database-a huge collection of most Italian diabetology medical records covering 15 years (2005-2019)-the potential effects of the extended use of sodium-glucose co-transporter 2 inhibitors (SGLT-2is) and of glucose-like peptide 1 receptor antagonists (GLP-1-RAs) on HbA and weight.

Methods: Data from 4,927,548 visits in 558,097 patients were retrospectively extracted using these exclusion criteria: type 1 diabetes, pregnancy, age >75 years, dialysis, and lack of data on HbA or weight.

Uncovering Predictors of Lipid Goal Attainment in Type 2 Diabetes Outpatients Using Logic Learning Machine: Insights from the AMD Annals and AMD Artificial Intelligence Study Group.

Identifying and treating lipid abnormalities is crucial for preventing cardiovascular disease in diabetic patients, yet only two-thirds of patients reach recommended cholesterol levels. Elucidating the factors associated with lipid goal attainment represents an unmet clinical need. To address this knowledge gap, we conducted a real-world analysis of the lipid profiles of 11.

Transparent machine learning suggests a key driver in the decision to start insulin therapy in individuals with type 2 diabetes.

Aims: The objective of this study is to establish a predictive model using transparent machine learning (ML) to identify any drivers that characterize therapeutic inertia.

Methods: Data in the form of both descriptive and dynamic variables collected from electronic records of 1.5 million patients seen at clinics within the Italian Association of Medical Diabetologists between 2005-2019 were analyzed using logic learning machine (LLM), a "clear box" ML technique.

Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis.

Introduction: The aim of this study was to investigate the factors (clinical, organizational or doctor-related) involved in a timely and effective achievement of metabolic control, with no weight gain, in type 2 diabetes.

Research Design And Methods: Overall, 5.5 million of Hab1c and corresponding weight were studied in the Associazione Medici Diabetologi Annals database (2005-2017 data from 1.

Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists.

Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning.

Switching Patients with Type 1 Diabetes to Insulin Degludec from Other Basal Insulins: Real-World Data of Effectiveness and Safety.

Introduction: Real-world evidence on the effectiveness and safety of insulin degludec (IDeg) in patients with diabetes is a priority. We have therefore evaluated the effectiveness and safety of IDeg, including impact on metabolic control, glycemic variability, weight gain and hypoglycemia, in patients with type 1 diabetes under routine clinical practice conditions.

Methods: This was an observational longitudinal multicenter study.

Impact of Insulin Degludec in Type 2 Diabetes: Real-World Data on Effectiveness and Safety.

Introduction: Real-world evidence on effectiveness and safety of insulin degludec (IDeg) in patients with diabetes is a priority. The aim of the study was to evaluate patterns of use and the long-term effectiveness and safety of IDeg in routine clinical practice.

Methods: This was an observational longitudinal study.

Long-Term Effectiveness of Liraglutide for Treatment of Type 2 Diabetes in a Real-Life Setting: A 24-Month, Multicenter, Non-interventional, Retrospective Study.

Introduction: The aim of the study was to evaluate whether the reduction in glycated hemoglobin (HbA1c) observed in clinical trials with liraglutide in type 2 diabetes (T2D) could be attained in routine clinical practice.

Methods: ReaL was a multicenter, non-interventional, observational, retrospective, longitudinal study on the effectiveness of liraglutide, a human glucagon-like peptide-1 analog, in individuals with T2D treated in daily practice in Italy. Between 26 March and 16 November 2015, data were taken from clinical records of patients aged ≥ 18 years with treatment follow-up data of up to 24 months and who received their first prescription of liraglutide in 2011.

Effectiveness and safety of liraglutide after three years of treatment.

Background: The aim of this study was to evaluate long-term effectiveness and safety of liraglutide in real world.

Methods: A diabetes clinic in Italy systematically collected data of all patients treated with liraglutide. Generalized hierarchical linear regression models for repeated measures were applied to assess trends over time of HbA1c, fasting plasma glucose (FPG), body weight, blood pressure and lipid profile.