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. Machine learning software programs that disclose the reasoning behind a prediction allow for "what-if" models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that "affect" the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.

ANMCO/ISS/AMD/ANCE/ARCA/FADOI/GICR-IACPR/SICI-GISE/SIBioC/SIC/SICOA/SID/SIF/SIMEU/SIMG/SIMI/SISA Joint Consensus Document on cholesterol and cardiovascular risk: diagnostic-therapeutic pathway in Italy.

Atherosclerotic cardiovascular disease still represents the leading cause of death in Western countries. A wealth of scientific evidence demonstrates that increased blood cholesterol levels have a major impact on the outbreak and progression of atherosclerotic plaques. Moreover, several cholesterol-lowering pharmacological agents, including statins and ezetimibe, have proved effective in improving clinical outcomes. This document focuses on the clinical management of hypercholesterolaemia and has been conceived by 16 Italian medical associations with the support of the Italian National Institute of Health. The authors discuss in detail the role of hypercholesterolaemia in the genesis of atherosclerotic cardiovascular disease. In addition, the implications for high cholesterol levels in the definition of the individual cardiovascular risk profile have been carefully analysed, while all available therapeutic options for blood cholesterol reduction and cardiovascular risk mitigation have been explored. Finally, this document outlines the diagnostic and therapeutic pathways for the clinical management of patients with hypercholesterolaemia.

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. Inclusion criteria comprised patients on metformin monotherapy with two consecutive mean HbA1c levels exceeding 7.0%. The cohort was divided into "inertia-NO" (20,067 patients with prompt intensification) and "inertia-YES" (13,029 patients without timely intensification). RESULTS: The LLM model demonstrated robust discriminatory ability among the two groups (ROC-AUC = 0.81, accuracy = 0.71, precision = 0.80, recall = 0.71, F1 score = 0.75). The main novelty of our results is indeed the identification of two main distinct subtypes of therapeutic inertia. The first exhibited a gradual but steady HbA1c increase, while the second featured a moderate, non-uniform rise with substantial fluctuations. CONCLUSIONS: Our analysis sheds light on the significant impact of HbA1c levels over time on therapeutic inertia in patients with T2D, emphasizing the importance of early intervention in the presence of specific HbA1c patterns.

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. Data were subjected to a first stage of modeling to allow ML to automatically select the most relevant factors related to inertia, and then four further modeling steps individuated key variables that discriminated the presence or absence of inertia. RESULTS: The LLM model revealed a key role for average glycated hemoglobin (HbA1c) threshold values correlated with the presence or absence of insulin therapeutic inertia with an accuracy of 0.79. The model indicated that a patient's dynamic rather than static glycemic profile has a greater effect on therapeutic inertia. Specifically, the difference in HbA1c between two consecutive visits, what we call the HbA1c gap, plays a crucial role. Namely, insulin therapeutic inertia is correlated with an HbA1c gap of <6.6 mmol/mol (0.6%), but not with an HbA1c gap of >11 mmol/mol (1.0%). CONCLUSIONS: The results reveal, for the first time, the interrelationship between a patient's glycemic trend defined by sequential HbA1c measurements and timely or delayed initiation of insulin therapy. The results further demonstrate that LLM can provide insight in support of evidence-based medicine using real world data.

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 11.252 patients from the Annals of the Italian Association of Medical Diabetologists (AMD) database from 2005 to 2019. We used a Logic Learning Machine (LLM) to extract and classify the most relevant variables predicting the achievement of a low-density lipoprotein cholesterol (LDL-C) value lower than 100 mg/dL (2.60 mmol/L) within two years of the start of lipid-lowering therapy. Our analysis showed that 61.4% of the patients achieved the treatment goal. The LLM model demonstrated good predictive performance, with a precision of 0.78, accuracy of 0.69, recall of 0.70, F1 Score of 0.74, and ROC-AUC of 0.79. The most significant predictors of achieving the treatment goal were LDL-C values at the start of lipid-lowering therapy and their reduction after six months. Other predictors of a greater likelihood of reaching the target included high-density lipoprotein cholesterol, albuminuria, and body mass index at baseline, as well as younger age, male sex, more follow-up visits, no therapy discontinuation, higher Q-score, lower blood glucose and HbA1c levels, and the use of anti-hypertensive medication. At baseline, for each LDL-C range analysed, the LLM model also provided the minimum reduction that needs to be achieved by the next six-month visit to increase the likelihood of reaching the therapeutic goal within two years. These findings could serve as a useful tool to inform therapeutic decisions and to encourage further in-depth analysis and testing.

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 HbA1c 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 HbA1c or weight. The analysis revealed late prescribing of SGLT-2is and GLP-1-RAs (innovative drugs), and considering a time frame of 4 years (2014-2017), a paradoxic greater percentage of combined-goal (HbA1c <7% and weight gain <2%) achievement was found with older drugs than with innovative drugs, demonstrating aspects of therapeutic inertia. Through a machine-learning AI technique, a "what-if" analysis was performed, using query models of two outcomes: (1) achievement of the combined goal at the visit subsequent to a hypothetical initial prescribing of an SGLT-2i or a GLP-1-RA, with and without insulin, selected according to the 2018 ADA-EASD diabetes recommendations; and (2) persistence of the combined goal for 18 months. The precision values of the two models were, respectively, sensitivity, 71.1 % and 69.8%, and specificity, 67% and 76%. FINDINGS: The first query of the AI analysis showed a great improvement in achievement of the combined goal: 38.8% with prescribing in clinical practice versus 66.5% with prescribing in the "what-if" simulation. Addressing persistence at 18 months after the initial achievement of the combined goal, the simulation showed a potential better performance of SGLT-2is and GLP-1-RAs with respect to each antidiabetic pharmacologic class or combination considered. IMPLICATIONS: AI appears potentially useful in the analysis of a great amount of data, such as that derived from the AMD Annals. In the present study, an LLM analysis revealed a great potential improvement in achieving metabolic targets with SGLT-2i and GLP-1-RA utilization. These results underscore the importance of early, timely, and extended use of these new drugs.

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 1.5 million patients of the Italian diabetes clinics network). Logic learning machine, a specific type of machine learning technique, was used to extract and rank the most relevant variables and to create the best model underlying the achievement of HbA1c<7 and no weight gain. RESULTS: The combined goal was achieved in 37.5% of measurements. High HbA1c and fasting glucose values and slow drop of HbA1c have the greatest relevance and emerge as first, main, obstacles the doctor has to overcome. However, as a second line of negative factors, markers of insulin resistance, microvascular complications, years of observation and proxy of duration of disease appear to be important determinants. Quality of assistance provided by the clinic plays a positive role. Almost all the available oral agents are effective whereas insulin use shows positive impact on glucometabolism but negative on weight containment. We also tried to analyze the contribution of each component of the combined endpoint; we found that weight gain was less frequently the reason for not reaching the endpoint and that HbA1c and weight have different determinants. Of note, use of glucagon-like peptide-1 receptor agonists (GLP1-RA) and glifozins improves weight control. CONCLUSIONS: Treating diabetes as early as possible with the best quality of care, before beta-cell deterioration and microvascular complications occurrence, make it easier to compensate patients. This message is a warning against clinical inertia. All medications play a role in goal achievements but use of GLP1-RAs and glifozins contributes to overweight prevention.

An integrated care pathway for cancer patients with diabetes: A proposal from the Italian experience.

Diabetes and cancer frequently coexist in the same subject, often with relevant clinical effects on the management and prognosis of the comorbid patient. The existing guidelines, however, do not appropriately address many clinical issues in this setting. Although collaboration between diabetologists and oncologists should play an important role in achieving appropriate levels of care, close coordination or agreement between these specialists is seldom offered. There is an urgent need for greater interdisciplinary integration between all specialists involved in this setting, for a shared approach ensuring that organisational silos are overcome. To this end, the Italian Associations of Medical Diabetologists (AMD) and the Italian Association of Medical Oncology (AIOM) recently established a dedicated Working Group on 'Diabetes and Cancer'. The working group outlined a diagnostic and therapeutic clinical pathway dedicated to hospitalised patients with diabetes and cancer. In this article, we describe the Italian proposal including some suggested measures to assess, monitor and improve blood glucose control in the hospital setting, to integrate different specialists from both areas, as well as to ensure discharge planning and continuity of care from the hospital to the territory.

Real-world use of self-monitoring of blood glucose in people with type 2 diabetes: an urgent need for improvement.

AIMS: To assess use of self-monitoring of blood glucose (SMBG) in type 2 diabetes (T2DM) in the context of a continuous quality improvement initiative (AMD Annals). METHODS: 14 quality-of-care indicators were developed, including frequency of SMBG, fasting blood glucose (FBG), and post-prandial glucose (PPG) levels, and hypoglycemia and hyperglycemia episodes. Clinical data and SMBG values downloaded from any glucose meter were obtained from electronic medical records. The most frequently used glucose-lowering treatment regimens were identified and the indicators were assessed separately by regimen. RESULTS: Overall, 21 Italian centers and 13,331 patients (accounting for 35,657 HbA1c tests and 8.44 million SMBG values collected during 2014 and 2015) were included in the analysis; 11 therapeutic regimens were selected. Patients in regimens not including insulin performed 15-23 measurements per patient-month, those treated with basal insulin 32.1 tests/patient-month, and those treated with basal and short-acting insulin 53-58 tests/patient-month. In all treatment regimens, PPG measurements represented a minority of all tests; pre-breakfast measurements accounted for about 50% of all FBG values. Mean FBG levels exceeded 130 mg/dl in 49.3-88.3% of the cases in the different treatment regimens, while PPG levels were over 140 mg/dl in 46.7-81.0%. From 5.7 to 32.7%, patients in the different regimens had at least one episode of hypoglycemia (< 70 mg/dl), while from 3.7 to 47.7% had at least one episode of hyperglycemia (> 300 mg/dl). CONCLUSIONS: SMBG is underutilized in patients with T2DM treated or not with insulin. In all treatment groups, PPG is seldom investigated. Poor metabolic control and rates of hyper- and hypoglycemia deserve consideration in all treatment groups.

Portrait of women with type 1 or type 2 diabetes of childbearing age attending diabetes clinics in Italy: the AMD-Annals initiative.

AIM: To describe characteristics relevant in case of an unplanned pregnancy for T1D or T2D women of childbearing age. METHODS: We analyzed the 2011 AMD-Annals dataset, compiling information from 300 clinics (28,840 T1D patients and 532,651 T2D patients). A risk score of unfavorable conditions for pregnancy included HbA1c > 8.0%; BMI ≥ 35; systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg; microalbuminuria/proteinuria; use of statins, ACE inhibitors, ARB; use of diabetes drugs other than metformin/insulin. RESULTS: The proportion of T2D cases increased from 30.8% (95% CI 29.9-32.4) at age 18-30 years to 67.5% (66.6-68.5) at age 36-45 years. The proportion of women with HbA1c < 7.0% was 20.4% (20.0-20.8) in T1D and 43.4% (42.8-43.9) in T2D women. Furthermore, 47.6% (47.0-48.3) of T1D women and 34.5% (33.9-35.0) of T2D women had HbA1c ≥ 8.0%. The prevalence of obesity (BMI ≥ 30) was sevenfold higher among T2D than T1D women [49.9% (49.4-50.5) and 7.4% (7.2-7.5), respectively]. T2D women were more likely to have hypertension or microalbuminuria than T1D women. Almost half of the T2D women were taking drugs not approved during pregnancy. At least one unfavorable condition for starting a pregnancy was present in 51% of T1D women of childbearing age and in 66.7% of T2D women. CONCLUSIONS: Women with either T1D or T2D of childbearing age in Italy were far from the ideal medical condition for conception. Our data strongly support the need for counseling all women with diabetes about pregnancy planning.

Gender-Disparities in Adults with Type 1 Diabetes: More Than a Quality of Care Issue. A Cross-Sectional Observational Study from the AMD Annals Initiative.

We evaluated gender-differences in quality of type 1 diabetes (T1DM) care. Starting from electronic medical records of 300 centers, 5 process indicators, 3 favorable and 6 unfavorable intermediate outcomes, 6 treatment intensity/appropriateness measures and an overall quality score were measured. The likelihood of women vs. men (reference class) to be monitored, to reach outcomes, or to be treated has been investigated through multilevel logistic regression analyses; results are expressed as Odd Ratios (ORs) and 95% confidence intervals (95%CIs). The inter-center variability in the achievement of the unfavorable outcomes was also investigated. Overall, 28,802 subjects were analyzed (45.5% women). Women and men had similar age (44.5±16.0 vs. 45.0±17.0 years) and diabetes duration (18.3±13.0 vs. 18.8±13.0 years). No between-gender differences were found in process indicators. As for intermediate outcomes, women showed 33% higher likelihood of having HbA1c ≥8.0% (OR = 1.33; 95%CI: 1.25-1.43), 29% lower risk of blood pressure ≥140/90 mmHg (OR = 0.71; 95%CI: 0.65-0.77) and 27% lower risk of micro/macroalbuminuria (OR = 0.73; 95%CI: 0.65-0.81) than men, while BMI, LDL-c and GFR did not significantly differ; treatment intensity/appropriateness was not systematically different between genders; overall quality score was similar in men and women. Consistently across centers a larger proportion of women than men had HbA1c ≥8.0%, while a smaller proportion had BP ≥140/90 mmHg. No gender-disparities were found in process measures and improvements are required in both genders. The systematic worse metabolic control in women and worse blood pressure in men suggest that pathophysiologic differences rather than the care provided might explain these differences.

[ANMCO/ISS/AMD/ANCE/ARCA/FADOI/GICR-IACPR/SICI-GISE/SIBioC/SIC/SICOA/SID/SIF/SIMEU/SIMG/SIMI/SISA Consensus document. Hypercholesterolemia and cardiovascular risk: diagnostic and therapeutic pathways in Italy]. / Documento di consenso intersocietario ANMCO/ISS/AMD/ANCE/ARCA/FADOI/ GICR-IACPR/SICI-GISE/SIBioC/SIC/SICOA/ SID/SIF/SIMEU/SIMG/SIMI/SISA Colesterolo e rischio cardiovascolare: percorso diagnostico-terapeutico in Italia.

Atherosclerotic cardiovascular disease still represents the leading cause of death in western countries. A wealth of scientific evidence demonstrates that increased blood cholesterol levels have a major impact on the outbreak and progression of atherosclerotic plaques. Moreover, several cholesterol-lowering pharmacological agents, including statins and ezetimibe, have proven effective in improving clinical outcomes. This document is focused on the clinical management of hypercholesterolemia and has been conceived by 16 Italian medical associations with the support of the Italian National Institute of Health. The authors have considered with particular attention the role of hypercholesterolemia in the genesis of atherosclerotic cardiovascular disease. Besides, the implications of high cholesterol levels in the definition of the individual cardiovascular risk profile have been carefully analyzed, while all available therapeutic options for blood cholesterol reduction and cardiovascular risk mitigation have been considered. Finally, this document outlines the diagnostic and therapeutic pathways for the clinical management of patients with hypercholesterolemia.

Cardiac events in 735 type 2 diabetic patients who underwent screening for unknown asymptomatic coronary heart disease: 5-year follow-up report from the Milan Study on Atherosclerosis and Diabetes (MiSAD).

OBJECTIVE: To report the cardiac events in type 2 diabetic outpatients screened for unknown asymptomatic coronary heart disease (CHD) and followed for 5 years. RESEARCH DESIGN AND METHODS: During 1993, 925 subjects aged 40-65 years underwent an exercise treadmill test (ETT). If it was abnormal, the subjects then underwent an exercise scintigraphy. Of the 925 subjects, 735 were followed for 5 years and cardiac events were recorded. RESULTS: At the entry of the study, 638 of the 735 followed subjects had normal ETT, 45 had abnormal ETT with normal scintigraphy, and 52 had abnormal ETT and abnormal scintigraphy. The 52 subjects with abnormal scintigraphy and ETT underwent a cardiological and diabetological follow-up; the subjects with just abnormal ETT had a diabetological follow-up only. During the follow-ups, 42 cardiac events occurred: 1 fatal myocardial infarction (MI), 20 nonfatal MIs, and 10 cases of angina in the 638 subjects with normal ETT; 1 fatal MI in the 45 subjects with normal scintigraphy; and 1 fatal MI and 9 cases of angina in the 52 subjects with abnormal scintigraphy. In these 52 subjects all cardiac events were significantly more frequent (chi(2) = 21.40, P < 0.0001) but the ratio of major (cardiac death and MI) to minor (angina) cardiac events was significantly lower (P = 0.002). Scintigraphy abnormality (hazard ratio 5.47; P < 0.001; 95% CI 2.43-12.29), diabetes duration (1.06; P = 0.021; 1.008-1.106), and diabetic retinopathy (2.371; P = 0.036; 1.059-5.307) were independent predictors of cardiac events on multivariate analysis. CONCLUSIONS: The low ratio of major to minor cardiac events in the positive scintigraphy group may suggest, although it does not prove, that the screening program followed by appropriate management was effective for the reduction of risk of major cardiac events.