Long-term benefits of dapagliflozin on renal outcomes of type 2 diabetes under routine care: a comparative effectiveness study on propensity score matched cohorts at low renal risk.

Background: Despite the overall improvement in care, people with type 2 diabetes (T2D) experience an excess risk of end-stage kidney disease. We evaluated the long-term effectiveness of dapagliflozin on kidney function and albuminuria in patients with T2D. Methods: We included patients with T2D who initiated dapagliflozin or comparators from 2015 to 2020. Propensity score matching (PSM) was performed to balance the two groups. The primary endpoint was the change in estimated glomerular filtration rate (eGFR) from baseline to the end of observation. Secondary endpoints included changes in albuminuria and loss of kidney function. Findings: We analysed two matched groups of 6197 patients each. The comparator group included DPP-4 inhibitors (40%), GLP-1RA (22.3%), sulphonylureas (16.1%), pioglitazone (8%), metformin (5.8%), or acarbose (4%). Only 6.4% had baseline eGFR <60 ml/min/1.73 m2 and 15% had UACR >30 mg/g. During a mean follow-up of 2.5 year, eGFR declined significantly less in the dapagliflozin vs comparator group by 1.81 ml/min/1.73 m2 (95% C.I. from 1.13 to 2.48; p < 0.0001). The mean eGFR slope was significantly less negative in the dapagliflozin group by 0.67 ml/min/1.73 m2/year (95% C.I. from 0.47 to 0.88; p < 0.0001). Albuminuria declined significantly in new-users of dapagliflozin within 6 months and remained on average 44.3 mg/g lower (95% C.I. from -66.9 to -21.7; p < 0.0001) than in new-users of comparators. New-users of dapagliflozin had significantly lower rates of new-onset CKD, loss of kidney function, and a composite renal outcome. Results were confirmed for all SGLT2 inhibitors, in patients without baseline CKD, and when GLP-1RA were excluded from comparators. Interpretation: Initiating dapagliflozin improved kidney function outcomes and albuminuria in patients with T2D and a low renal risk. Funding: Funded by the Italian Diabetes Society and partly supported by a grant from AstraZeneca.

Deep-learning-based natural-language-processing models to identify cardiovascular disease hospitalisations of patients with diabetes from routine visits' text.

Writing notes is the most widespread method to report clinical events. Therefore, most of the information about the disease history of a patient remains locked behind free-form text. Natural language processing (NLP) provides a solution to automatically transform free-form text into structured data. In the present work, electronic healthcare records data of patients with diabetes were used to develop deep-learning based NLP models to automatically identify, within free-form text describing routine visits, the occurrence of hospitalisations related to cardiovascular disease (CVDs), an outcome of diabetes. Four possible time windows of increasing level of expected difficulty were considered: infinite, 24 months, 12 months, and 6 months. Model performance was evaluated by means of the area under the precision recall curve, as well as precision, recall, and F1-score after thresholding. Results showed that the proposed NLP approach was successful for both the infinite and 24-month windows, while, as expected, performance deteriorated with shorter time windows. Possible clinical applications of tools based on the proposed NLP approach include the retrospective filling of medical records with respect to a patient's CVD history for epidemiological and research purposes as well as for clinical decision making.

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review.

BACKGROUND: Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous. OBJECTIVE: This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS. METHODS: We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics. RESULTS: Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only. CONCLUSION: This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.

Management of type 2 diabetes with a treat-to-benefit approach improved long-term cardiovascular outcomes under routine care.

BACKGROUND: Results of cardiovascular outcome trials enabled a shift from "treat-to-target" to "treat-to-benefit" paradigm in the management of type 2 diabetes (T2D). However, studies validating such approach are limited. Here, we examined whether treatment according to international recommendations for the pharmacological management of T2D had an impact on long-term outcomes. METHODS: This was an observational study conducted on outpatient data collected in 2008-2018 (i.e. prior to the "treat-to-benefit" shift). We defined 6 domains of treatment based on the ADA/EASD consensus covering all disease stages: first- and second-line treatment, intensification, use of insulin, cardioprotective, and weight-affecting drugs. At each visit, patients were included in Group 1 if at least one domain deviated from recommendation or in Group 2 if aligned with recommendations. We used Cox proportional hazard models with time-dependent co-variates or Cox marginal structural models (with inverse-probability of treatment weighing evaluated at each visit) to adjust for confounding factors and evaluate three outcomes: major adverse cardiovascular events (MACE), hospitalization for heart failure or cardiovascular mortality (HF-CVM), and all-cause mortality. RESULTS: We included 5419 patients, on average 66-year old, 41% women, with a baseline diabetes duration of 7.6 years. Only 11.7% had pre-existing cardiovascular disease. During a median follow-up of 7.3 years, patients were seen 12 times at the clinic, and we recorded 1325 MACE, 1593 HF-CVM, and 917 deaths. By the end of the study, each patient spent on average 63.6% of time in Group 1. In the fully adjusted model, being always in Group 2 was associated with a 45% lower risk of MACE (HR 0.55; 95% C.I. 0.46-0.66; p < 0.0001) as compared to being in Group 1. The corresponding HF-CVM and mortality risk were similar (HR 0.56; 95%CI 0.47-0.66, p < 0.0001 and HR 0.56; 95% C.I. 0.45-0.70; p < 0.0001. respectively). Sensitivity analyses confirmed these results. No single domain individually explained the better outcome of Group 2, which remained significant in all subgroups. CONCLUSION: Managing patients with T2D according to a "treat-to-benefit" approach based international standards was associated with a lower risk of MACE, heart failure, and mortality. These data provide ex-post validation of the ADA/EASD treatment algorithm.

Time-resolved trajectory of glucose lowering medications and cardiovascular outcomes in type 2 diabetes: a recurrent neural network analysis.

AIM: Treatment algorithms define lines of glucose lowering medications (GLM) for the management of type 2 diabetes (T2D), but whether therapeutic trajectories are associated with major adverse cardiovascular events (MACE) is unclear. We explored whether the temporal resolution of GLM usage discriminates patients who experienced a 4P-MACE (heart failure, myocardial infarction, stroke, death for all causes). METHODS: We used an administrative database (Veneto region, North-East Italy, 2011-2018) and implemented recurrent neural networks (RNN) with outcome-specific attention maps. The model input included age, sex, diabetes duration, and a matrix of GLM pattern before the 4P-MACE or censoring. Model output was discrimination, reported as area under receiver characteristic curve (AUROC). Attention maps were produced to show medications whose time-resolved trajectories were the most important for discrimination. RESULTS: The analysis was conducted on 147,135 patients for training and model selection and on 10,000 patients for validation. Collected data spanned a period of ~ 6 years. The RNN model efficiently discriminated temporal patterns of GLM ending in a 4P-MACE vs. those ending in an event-free censoring with an AUROC of 0.911 (95% C.I. 0.904-0.919). This excellent performance was significantly better than that of other models not incorporating time-resolved GLM trajectories: (i) a logistic regression on the bag-of-words encoding all GLM ever taken by the patient (AUROC 0.754; 95% C.I. 0.743-0.765); (ii) a model including the sequence of GLM without temporal relationships (AUROC 0.749; 95% C.I. 0.737-0.761); (iii) a RNN model with the same construction rules but including a time-inverted or randomised order of GLM. Attention maps identified the time-resolved pattern of most common first-line (metformin), second-line (sulphonylureas) GLM, and insulin (glargine) as those determining discrimination capacity. CONCLUSIONS: The time-resolved pattern of GLM use identified patients with subsequent cardiovascular events better than the mere list or sequence of prescribed GLM. Thus, a patient's therapeutic trajectory could determine disease outcomes.

Performance assessment across different care settings of a heart failure hospitalisation risk-score for type 2 diabetes using administrative claims.

Predicting the risk of cardiovascular complications, in particular heart failure hospitalisation (HHF), can improve the management of type 2 diabetes (T2D). Most predictive models proposed so far rely on clinical data not available at the higher Institutional level. Therefore, it is of interest to assess the risk of HHF in people with T2D using administrative claims data only, which are more easily obtainable and could allow public health systems to identify high-risk individuals. In this paper, the administrative claims of > 175,000 patients with T2D were used to develop a new risk score for HHF based on Cox regression. Internal validation on the administrative data cohort yielded satisfactory results in terms of discrimination (max AUROC = 0.792, C-index = 0.786) and calibration (Hosmer-Lemeshow test p value < 0.05). The risk score was then tested on data gathered from two independent centers (one diabetes outpatient clinic and one primary care network) to demonstrate its applicability to different care settings in the medium-long term. Thanks to the large size and broad demographics of the administrative dataset used for training, the proposed model was able to predict HHF without significant performance loss concerning bespoke models developed within each setting using more informative, but harder-to-acquire clinical variables.

Time-series analysis of multidimensional clinical-laboratory data by dynamic Bayesian networks reveals trajectories of COVID-19 outcomes.

BACKGROUND AND OBJECTIVE: COVID-19 severity spans an entire clinical spectrum from asymptomatic to fatal. Most patients who require in-hospital care are admitted to non-intensive wards, but their clinical conditions can deteriorate suddenly and some eventually die. Clinical data from patients' case series have identified pre-hospital and in-hospital risk factors for adverse COVID-19 outcomes. However, most prior studies used static variables or dynamic changes of a few selected variables of interest. In this study, we aimed at integrating the analysis of time-varying multidimensional clinical-laboratory data to describe the pathways leading to COVID-19 outcomes among patients initially hospitalised in a non-intensive care setting. METHODS: We collected the longitudinal retrospective data of 394 patients admitted to non-intensive care units at the University Hospital of Padova (Padova, Italy) due to COVID-19. We trained a dynamic Bayesian network (DBN) to encode the conditional probability relationships over time between death and all available demographics, pre-existing conditions, and clinical laboratory variables. We applied resampling, dynamic time warping, and prototyping to describe the typical trajectories of patients who died vs. those who survived. RESULTS: The DBN revealed that the trajectory linking demographics and pre-existing clinical conditions to death passed directly through kidney dysfunction or, more indirectly, through cardiac damage. As expected, admittance to the intensive care unit was linked to markers of respiratory function. Notably, death was linked to elevation in procalcitonin and D-dimer levels. Death was associated with persistently high levels of procalcitonin from admission and throughout the hospital stay, likely reflecting bacterial superinfection. A sudden raise in D-dimer levels 3-6 days after admission was also associated with subsequent death, possibly reflecting a worsening thrombotic microangiopathy. CONCLUSIONS: This innovative application of DBNs and prototyping to integrated data analysis enables visualising the patient's trajectories to COVID-19 outcomes and may instruct timely and appropriate clinical decisions.

Cardiovascular outcomes after initiating GLP-1 receptor agonist or basal insulin for the routine treatment of type 2 diabetes: a region-wide retrospective study.

AIM: We aimed to compare cardiovascular outcomes of patients with type 2 diabetes (T2D) who initiated GLP-1 receptor agonists (GLP-1RA) or basal insulin (BI) under routine care. METHODS: We accessed the administrative claims database of the Veneto Region (Italy) to identify new users of GLP-1RA or BI in 2014-2018. Propensity score matching (PSM) was implemented to obtain two cohorts of patients with superimposable characteristics. The primary endpoint was the 3-point major adverse cardiovascular events (3P-MACE). Secondary endpoints included 3P-MACE components, hospitalization for heart failure, revascularizations, and adverse events. RESULTS: From a background population of 5,242,201 citizens, 330,193 were identified as having diabetes. PSM produced two very well matched cohorts of 4063 patients each, who initiated GLP-1RA or BI after an average of 2.5 other diabetes drug classes. Patients were 63-year-old and only 15% had a baseline history of cardiovascular disease. During a median follow-up of 24 months in the intention-to-treat analysis, 3P-MACE occurred less frequently in the GLP-1RA cohort (HR versus BI 0.59; 95% CI 0.50-0.71; p < 0.001). All secondary cardiovascular endpoints were also significantly in favor of GLP-1RA. Results were confirmed in the as-treated approach and in several stratified analyses. According to the E-value, confounding by unmeasured variables were unlikely to entirely explain between-group differences in cardiovascular outcomes. CONCLUSIONS: Patients with T2D who initiated a GLP-1RA experienced far better cardiovascular outcomes than did matched patients who initiated a BI in the same healthcare system. These finding supports prioritization of GLP-1RA as the first injectable regimen for the management of T2D.

Outcomes of patients with type 2 diabetes treated with SGLT-2 inhibitors versus DPP-4 inhibitors. An Italian real-world study in the context of other observational studies.

AIMS: We compared cardiovascular outcomes of patients with type 2 diabetes (T2D) receiving sodium glucose cotransporter-2 inhibitors (SGLT2i) or dipeptidyl peptidase-4 inhibitors (DPP4i) under routine care. METHODS: From an administrative claims database of >5.2M citizen, we identified patients with T2D who initiated SGLT2i or DPP4i from 2014 to 2018. Patients were matched by propensity scores. The primary outcome was the 3-point major adverse cardiovascular events (3P-MACE). RESULTS: After matching, we included 3216 patients/group, with mean age of 63 years, diabetes duration of 8.7 years, and 20% had cardiovascular disease. During a median follow-up of 18 months, the rate of 3P-MACE was lower among patients who initiated SGLT2i versus DPP4i (HR 0.74; 95 %C.I. 0.58-0.94). Initiators of SGLT2i also showed significantly lower rates of myocardial infarction (HR 0.75; 95 %C.I. 0.56-1.00), hospitalization for heart failure (HR 0.44; 95 %C.I. 0.25-0.95) or cardiovascular causes (HR 0.72; 95 %C.I. 0.60-0.87), and all-cause death (HR 0.49; 95 %C.I. 0.25-0.95). Renal failure was less common with SGLT2i than with DPP4i. Results were consistent to those obtained in a meta-analysis of 10 observational studies on ~1.5M patients. CONCLUSIONS: Patients with T2D who initiated SGLT2i under routine care had better cardio-renal outcomes and lower all-cause mortality than similar patients who initiated DPP4i.

A Deep Learning Approach to Predict Diabetes' Cardiovascular Complications From Administrative Claims.

People with diabetes require lifelong access to healthcare services to delay the onset of complications. Their disease management processes generate great volumes of data across several domains, from clinical to administrative. Difficulties in accessing and processing these data hinder their secondary use in an institutional setting, even for highly desirable applications, such as the prediction of cardiovascular disease, the main driver of excess mortality in diabetes. Hence, in the present work, we propose a deep learning model for the prediction of major adverse cardiovascular events (MACE), developed and validated using the administrative claims of 214,676 diabetic patients of the Veneto region, in North East Italy. Specifically, we use a year of pharmacy and hospitalisation claims, together with basic patient's information, to predict the 4P-MACE composite endpoint, i.e., the first occurrence of death, heart failure, myocardial infarction, or stroke, with a variable prediction horizon of 1 to 5 years. Adapting to the time-to-event nature of this task, we cast our problem as a multi-outcome (4P-MACE and components), multi-label (1 to 5 years) classification task with a custom loss to account for the effect of censoring. Our model, purposefully specified to minimise data preparation costs, exhibits satisfactory performance in predicting 4P-MACE at all prediction horizons: AUROC from 0.812 (C.I.: 0.797 - 0.827) to 0.792 (C.I.: 0.781 - 0.802); C-index from 0.802 (C.I.: 0.788 - 0.816) to 0.770 (C.I.: 0.761 - 0.779). Components' prediction performance is also adequate, ranging from death's 0.877 1-year AUROC to stroke's 0.689 5-year AUROC.

Cardiovascular effectiveness of human-based vs. exendin-based glucagon like peptide-1 receptor agonists: a retrospective study in patients with type 2 diabetes.

AIMS: Glucagon like peptide-1 (GLP-1) receptor agonists (GLP-1RA) are effective to control type 2 diabetes (T2Ds) and can protect from adverse cardiovascular outcomes. GLP-1RA are based on the human GLP-1 or the exendin-4 sequence. We compared cardiovascular outcomes of patients with T2D who received human-based or exendin-based GLP-1RA in routine clinical practice. METHODS AND RESULTS: We performed a retrospective study on the administrative database of T2D patients from the Veneto Region (North-East Italy). We identified patients who initiated a human-based or exendin-based GLP-1RA from 2011 to 2018. The primary outcome was occurrence of major adverse cardiovascular events (MACE). Secondary outcomes were individual MACE components, revascularization, hospitalization for heart failure, or for cardiovascular causes. From 330 193 patients with diabetes, 6620 were new users of GLP-1RA. After propensity score matching, we analysed 1098 patients in each group, who were on average 61 years old, 59.5% males, 13% with established cardiovascular disease, had an estimated diabetes duration of 8.4 years, and a baseline HbA1c of 7.9%. During a median follow-up of 18 months, patients treated with human-based GLP-1RA as compared to those treated with exendin-based GLP-1RA, showed lower rates of MACE [hazard ratio 0.61; 95% confidence interval (CI) 0.39-0.95], myocardial infarction (0.51; 95% CI 0.28-0.94), and hospitalization for cardiovascular causes (0.66; 95% CI 0.47-0.92). CONCLUSION: We observed better cardiovascular outcomes among patients treated with human-based vs. exendin-based GLP-1RA under routine care. In the absence of comparative trials and in view of the limitations of retrospective studies, this finding provides a moderate level of evidence to guide clinical decision.

Addressing practical issues of predictive models translation into everyday practice and public health management: a combined model to predict the risk of type 2 diabetes improves incidence prediction and reduces the prevalence of missing risk predictions.

INTRODUCTION: Many predictive models for incident type 2 diabetes (T2D) exist, but these models are not used frequently for public health management. Barriers to their application include (1) the problem of model choice (some models are applicable only to certain ethnic groups), (2) missing input variables, and (3) the lack of calibration. While (1) and (2) drives to missing predictions, (3) causes inaccurate incidence predictions. In this paper, a combined T2D risk model for public health management that addresses these three issues is developed. RESEARCH DESIGN AND METHODS: The combined T2D risk model combines eight existing predictive models by weighted average to overcome the problem of missing incidence predictions. Moreover, the combined model implements a simple recalibration strategy in which the risk scores are rescaled based on the T2D incidence in the target population. The performance of the combined model was compared with that of the eight existing models using data from two test datasets extracted from the Multi-Ethnic Study of Atherosclerosis (MESA; n=1031) and the English Longitudinal Study of Ageing (ELSA; n=4820). Metrics of discrimination, calibration, and missing incidence predictions were used for the assessment. RESULTS: The combined T2D model performed well in terms of both discrimination (concordance index: 0.83 on MESA; 0.77 on ELSA) and calibration (expected to observed event ratio: 1.00 on MESA; 1.17 on ELSA), similarly to the best-performing existing models. However, while the existing models yielded a large percentage of missing predictions (17%-45% on MESA; 63%-64% on ELSA), this was negligible with the combined model (0% on MESA, 4% on ELSA). CONCLUSIONS: Leveraging on existing literature T2D predictive models, a simple approach based on risk score rescaling and averaging was shown to provide accurate and robust incidence predictions, overcoming the problem of recalibration and missing predictions in practical application of predictive models.

Exposure to dipeptidyl-peptidase 4 inhibitors and the risk of pneumonia among people with type 2 diabetes: Retrospective cohort study and meta-analysis.

AIM: Concerns have been raised that dipeptidyl-peptidase 4 inhibitors (DPP-4i) may increase the risk of pneumonia. We analysed observational data and clinical trials to explore whether use of DPP-4i modifies the risk of pneumonia. METHODS: We identified patients with diabetes in the Veneto region administrative database and performed propensity score matching between new users of DPP-4 inhibitors and new users of other oral glucose-lowering medications (OGLMs). We compared the rate of hospitalization for pneumonia between matched cohorts using the Cox proportional hazard model. The same analysis was repeated using the database of a local diabetes outpatient clinic. We retrieved similar observational studies from the literature to perform a meta-analysis. Results from trials reporting pneumonia rates among patients randomized to DPP-4 inhibitors versus placebo/active comparators were also meta-analysed. RESULTS: In the regional database, after matching 6495 patients/group, new users of DPP-4 inhibitors had a lower rate of hospitalization for pneumonia than new users of other OGLMs (HR 0.76; 95% CI 0.61-0.95). In the outpatient database, after matching 867 patients/group, new users of DPP-4 inhibitors showed a non-significantly lower rate of hospitalization for pneumonia (HR 0.65; 95% CI 0.41-1.04). The meta-analysis of observational studies yielded an overall non-significant lower risk of hospitalization for pneumonia among DPP-4 inhibitor users (RR 0.81; 95% CI 0.65-1.01). The meta-analysis of randomized controlled trials showed no overall effect of DPP-4 inhibitors on pneumonia risk (RR 1.06; 95% CI 0.93-1.20). CONCLUSION: The use of DPP-4 inhibitors can be considered as safe with regard to the risk of pneumonia.

A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models.

Developing a prognostic model for biomedical applications typically requires mapping an individual's set of covariates to a measure of the risk that he or she may experience the event to be predicted. Many scenarios, however, especially those involving adverse pathological outcomes, are better described by explicitly accounting for the timing of these events, as well as their probability. As a result, in these cases, traditional classification or ranking metrics may be inadequate to inform model evaluation or selection. To address this limitation, it is common practice to reframe the problem in the context of survival analysis, and resort, instead, to the concordance index (C-index), which summarises how well a predicted risk score describes an observed sequence of events. A practically meaningful interpretation of the C-index, however, may present several difficulties and pitfalls. Specifically, we identify two main issues: i) the C-index remains implicitly, and subtly, dependent on time, and ii) its relationship with the number of subjects whose risk was incorrectly predicted is not straightforward. Failure to consider these two aspects may introduce undesirable and unwanted biases in the evaluation process, and even result in the selection of a suboptimal model. Hence, here, we discuss ways to obtain a meaningful interpretation in spite of these difficulties. Aiming to assist experimenters regardless of their familiarity with the C-index, we start from an introductory-level presentation of its most popular estimator, highlighting the latter's temporal dependency, and suggesting how it might be correctly used to inform model selection. We also address the nonlinearity of the C-index with respect to the number of correct risk predictions, elaborating a simplified framework that may enable an easier interpretation and quantification of C-index improvements or deteriorations.

Cardiovascular outcomes of type 2 diabetic patients treated with SGLT-2 inhibitors versus GLP-1 receptor agonists in real-life.

INTRODUCTION: Sodium glucose cotransporter-2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) protect type 2 diabetic (T2D) patients from cardiovascular events, but no trial has directly compared their cardiovascular effects. We aimed to address this gap using real-world data. RESEARCH DESIGN AND METHODS: We performed a retrospective real-world study on a population of ~5 million inhabitants from North-East Italy. We identified T2D patients who received new prescription of SGLT2i or GLP-1RA from 2014 to 2018. SGLT2i and GLP-1RA initiators were matched 1:1 by propensity scores. The primary outcome was a composite of all-cause death, myocardial infarction, and stroke (three-point major adverse cardiovascular events (3P-MACE)). Secondary endpoints were each component of the primary endpoint, hospitalization for heart failure (HF), revascularization, hospitalization for cardiovascular causes, and adverse events. RESULTS: From a population of 330 193 diabetic patients, we followed 8596 SGLT2i and GLP-1RA matched initiators for a median of 13 months. Patients in both groups were on average 63 years old, 63% men, and 18% had pre-existing cardiovascular disease. T2D patients treated with SGLT2i versus GLP-1RA, experienced a lower rate of 3P-MACE (HR 0.68; 95% CI 0.61 to 0.99; p=0.043), myocardial infarction (HR 0.72; 95% CI 0.53 to 0.98; p=0.035), hospitalization for HF (HR 0.59; 95% CI 0.35 to 0.99; p=0.048), and hospitalization for cardiovascular causes (HR 0.82; 95% CI 0.69 to 0.99; p=0.037). Adverse events were not significantly different between the two groups. CONCLUSIONS: In the absence of dedicated trials, this observational study suggests that SGLT2i may be more effective than GLP-1RA in improving cardiovascular outcomes of T2D. TRIAL REGISTRATION NUMBER: NCT04184947.

Better cardiovascular outcomes of type 2 diabetic patients treated with GLP-1 receptor agonists versus DPP-4 inhibitors in clinical practice.

BACKGROUND: Cardiovascular outcome trials in high-risk patients showed that some GLP-1 receptor agonists (GLP-1RA), but not dipeptidyl-peptidase-4 inhibitors (DPP-4i), can prevent cardiovascular events in type 2 diabetes (T2D). Since no trial has directly compared these two classes of drugs, we performed a comparative outcome analysis using real-world data. METHODS: From a database of ~ 5 million people from North-East Italy, we retrospectively identified initiators of GLP-1RA or DPP-4i from 2011 to 2018. We obtained two balanced cohorts by 1:1 propensity score matching. The primary outcome was the 3-point major adverse cardiovascular events (3P-MACE; a composite of death, myocardial infarction, or stroke). 3P-MACE components and hospitalization for heart failure were secondary outcomes. RESULTS: From 330,193 individuals with T2D, we extracted two matched cohorts of 2807 GLP-1RA and 2807 DPP-4i initiators, followed for a median of 18 months. On average, patients were 63 years old, 60% male; 15% had pre-existing cardiovascular disease. The rate of 3P-MACE was lower in patients treated with GLP-1RA compared to DPP4i (23.5 vs. 34.9 events per 1000 person-years; HR: 0.67; 95% C.I. 0.53-0.86; p = 0.002). Rates of myocardial infarction (HR 0.67; 95% C.I. 0.50-0.91; p = 0.011) and all-cause death (HR 0.58; 95% C.I. 0.35-0.96; p = 0.034) were lower among GLP-1RA initiators. The as-treated and intention-to-treat approaches yielded similar results. CONCLUSIONS: Patients initiating a GLP-1RA in clinical practice had better cardiovascular outcomes than similar patients who initiated a DPP-4i. These data strongly confirm findings from cardiovascular outcome trials in a lower risk population.

Exposure to dipeptidyl-peptidase-4 inhibitors and COVID-19 among people with type 2 diabetes: A case-control study.

Because other coronaviruses enter the cells by binding to dipeptidyl-peptidase-4 (DPP-4), it has been speculated that DPP-4 inhibitors (DPP-4is) may exert an activity against severe acute respiratory syndrome coronavirus 2. In the absence of clinical trial results, we analysed epidemiological data to support or discard such a hypothesis. We retrieved information on exposure to DPP-4is among patients with type 2 diabetes (T2D) hospitalized for COVID-19 at an outbreak hospital in Italy. As a reference, we retrieved information on exposure to DPP-4is among matched patients with T2D in the same region. Of 403 hospitalized COVID-19 patients, 85 had T2D. The rate of exposure to DPP-4is was similar between T2D patients with COVID-19 (10.6%) and 14 857 matched patients in the region (8.8%), or 793 matched patients in the local outpatient clinic (15.4%), 8284 matched patients hospitalized for other reasons (8.5%), and when comparing 71 patients hospitalized for COVID-19 pneumonia (11.3%) with 351 matched patients with pneumonia of another aetiology (10.3%). T2D patients with COVID-19 who were on DPP-4is had a similar disease outcome as those who were not. In summary, we found no evidence that DPP-4is might affect hospitalization for COVID-19.

Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices.

BACKGROUND: Many glycemic variability (GV) indices exist in the literature. In previous works, we demonstrated that a set of GV indices, extracted from continuous glucose monitoring (CGM) data, can distinguish between stages of diabetes progression. We showed that 25 indices driving a logistic regression classifier can differentiate between healthy and nonhealthy individuals; whereas 37 GV indices and four individual parameters, feeding a polynomial-kernel support vector machine (SVM), can further distinguish between impaired glucose tolerance (IGT) and type 2 diabetes (T2D). The latter approach has some limitations to interpretability (complex model, extensive index pool). In this article, we try to obtain the same performance with a simpler classifier and a parsimonious subset of indices. METHODS: We analyzed the data of 62 subjects with IGT or T2D. We selected 17 interpretable GV indices and four parameters (age, sex, BMI, waist circumference). We trained a SVM on the data of a baseline visit and tested it on the follow-up visit, comparing the results with the state-of-art methods. RESULTS: The linear SVM fed by a reduced subset of 17 GV indices and four basic parameters achieved 82.3% accuracy, only marginally worse than the reference 87.1% (41-features polynomial-kernel SVM). Cross-validation accuracies were comparable (69.6% vs 72.5%). CONCLUSION: The proposed SVM fed by 17 GV indices and four parameters can differentiate between IGT and T2D. Using a simpler model and a parsimonious set of indices caused only a slight accuracy deterioration, with significant advantages in terms of interpretability.

Diabetes diagnosis from administrative claims and estimation of the true prevalence of diabetes among 4.2 million individuals of the Veneto region (North East Italy).

BACKGROUND AND AIMS: Diabetes can often remain undiagnosed or unregistered in administrative databases long after its onset, even when laboratory test results meet diagnostic criteria. In the present work, we analyse healthcare data of the Veneto Region, North East Italy, with the aims of: (i) developing an algorithm for the identification of diabetes from administrative claims (4,236,007 citizens), (ii) assessing its reliability by comparing its performance with the gold standard clinical diagnosis from a clinical database (7525 patients), (iii) combining the algorithm and the laboratory data of the regional Health Information Exchange (rHIE) system (543,520 subjects) to identify undiagnosed diabetes, and (iv) providing a credible estimate of the true prevalence of diabetes in Veneto. METHODS AND RESULTS: The proposed algorithm for the identification of diabetes was fed by administrative data related to drug dispensations, outpatient visits, and hospitalisations. Evaluated against a clinical database, the algorithm achieved 95.7% sensitivity, 87.9% specificity, and 97.6% precision. To identify possible cases of undiagnosed diabetes, we applied standard diagnostic criteria to the laboratory test results of the subjects who, according to the algorithm, had no diabetes-related claims. Using a simplified probabilistic model, we corrected our claims-based estimate of known diabetes (6.17% prevalence; 261,303 cases) to account for undiagnosed cases, yielding an estimated total prevalence of 7.50%. CONCLUSION: We herein validated an algorithm for the diagnosis of diabetes using administrative claims against the clinical diagnosis. Together with rHIE laboratory data, this allowed to identify possibly undiagnosed diabetes and estimate the true prevalence of diabetes in Veneto.

Improved long-term cardiovascular outcomes after intensive versus standard screening of diabetic complications: an observational study.

BACKGROUND: Complication screening is recommended for patients with type 2 diabetes (T2D), but the optimal screening intensity and schedules are unknown. In this study, we evaluated whether intensive versus standard complication screening affects long-term cardiovascular outcomes. METHODS: In this observational study, we included 368 T2D patients referred for intensive screening provided as a 1-day session of clinical-instrumental evaluation of diabetic complications, followed by dedicated counseling. From a total of 4906 patients, we selected control T2D patients who underwent standard complication screening at different visits, by 2:1 propensity score matching. The primary endpoint was the 4p-MACE, defined as cardiovascular mortality, or non-fatal myocardial infarction, stroke, or heart failure. The Cox proportional regression analyses was used to compare outcome occurrence in the two groups, adjusted for residual confounders. RESULTS: 357 patients from the intensive screening group (out of 368) were matched with 683 patients in the standard screening group. Clinical characteristics were well balanced between the two groups, except for a slightly higher prevalence of microangiopathy in the intensive group (56% vs 50%; standardized mean difference 0.11, p = 0.1). Median follow-up was 5.6 years. The adjusted incidence of 4p-MACE was significantly lower in the intensive versus standard screening group (HR 0.70; 95% CI 0.52-0.95; p = 0.02). All components of the primary endpoint had nominally lower rates in the intensive versus standard screening group, which was particularly significant for heart failure (HR 0.43; 95% CI 0.22-0.83; p = 0.01). CONCLUSION: Among T2D patients attending a specialist outpatient clinic, intensive complication screening is followed by better long-term cardiovascular outcomes. No significant effect was noted for cardiovascular and all-cause mortality and the benefit was mainly driven by a reduced rate of hospitalization for heart failure.