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[EN] Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. Results The use of the decision support component in clinical activities produced a reduction in visit duration (P¿¿¿.01) and an increase in the number of screening exams for complications (P¿, This work was supported by the European Union in the Seventh Framework Programme, grant number 600914.

Hepatic encephalopathy is suspected in non-cirrhotic cases of encephalopathy because the symptoms are accompanied by hyperammonaemia. Some cases have been misdiagnosed as psychiatric diseases and consequently patients hospitalized in psychiatric institutions or geriatric facilities. Therefore, the importance of accurate diagnosis of this disease should be strongly emphasized. A 68-year-old female patient presented to the Emergency Room with confusion, lethargy, nausea and vomiting. Examination disclosed normal vital signs. Neurological examination revealed a minimally responsive woman without apparent focal deficits and normal reflexes. She had no history of hematologic disorders or alcohol abuse. Her brain TC did not demonstrate any intracranial abnormalities and electroencephalography did not reveal any subclinical epileptiform discharges. Her ammonia level was > 400 mg/dL (reference range < 75 mg/dL) while hepatitis viral markers were negative. The patient was started on lactulose, rifaximin and low-protein diet. On the basis of the doppler ultrasound and abdomen computed tomography angiography findings, the decision was made to attempt portal venography which confirmed the presence of a giant portal-systemic venous shunt. Therefore, mechanic obliteration of shunt by interventional radiology was performed. As a consequence, mesenteric venous blood returned to hepatopetally flow into the liver, metabolic detoxification of ammonia increased and hepatic encephalopathy subsided. It is crucial that physicians immediately recognize the presence of non-cirrhotic encephalopathy, in view of the potential therapeutic resolution after accurate diagnosis and appropriate treatments.

One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

[EN] Patients with type 2 diabetes have a higher chance of developing cardiovascular diseases and an increased odds of mortality. Reliability of randomized clinical trials is continuously judged due to selection, attrition and reporting bias. Moreover, cardiovascular risk is frequently assessed in cross-sectional studies instead of observing the evolution of risk in longitudinal cohorts. In order to correctly assess the course of cardiovascular riskinpatientswithtype 2 diabetes, weappliedprocessminingtechniquesbasedontheprinciples of evidence-based medicine. Using a validated formulation of the cardiovascular risk, process mining allowed to cluster frequent risk pathways and produced 3 major trajectories related to risk management: high risk, medium risk and low risk.This enables the extractionofmeaningful distributions, such as the gender of the patients per cluster in a human understandable manner, leading to more insights to improve themanagementofcardiovasculardiseasesintype2diabetes patients., This work was supported by European Commission Grant No 600914 (MOSAIC Project).

To date treatment protocols in Respiratory and or Internal departments across Italy for treatment of chronic obstructive pulmonary disease (COPD) patients at hospital admission with relapse due to exacerbation do not find adequate support in current guidelines. Here we describe the results of a recent clinical audit, including a systematic review of practices reported in literature and an open discussion comparing these to current real-life procedures. The process was dived into two 8-hour-audits 3 months apart in order to allow work on the field in between meeting and involved 13 participants (3 nurses, 1 physiotherapist, 2 internists and 7 pulmonologists). This document reports the opinions of the experts and their consensus, leading to a bundle of multidisciplinary statements on the use of inhaled drugs for hospitalized COPD patients. Recommendations and topics addressed include: i) monitoring and diagnosis during the first 24 h after admission; ii) treatment algorithm and options (i.e., short and long acting bronchodilators); iii) bronchodilator dosages when switching device or using spacer; iv) flow measurement systems for shifting to LABA+LAMA within 48 h; v) when nebulizers are recommended; vi) use of SMI to deliver LABA+LAMA when patient needs SABA 30 litres/min; viii) contraindication to use DPI; ix) continuation of LABA-LAMA when patient is already on therapy; x) possible LABA-LAMA dosage increase; xi) use of SABA and/or SAMA in addition to LABA+LABA; xii) use of SABA+SAMA restricted to real need; xiii) reconciliation of drugs in presence of comorbidities; xiv) check of knowledge and skills on inhalation therapy; xv) discharge bundle; xvi) use of MDI and SMI in tracheostomized patients in spontaneous and ventilated breathing.

OBJECTIVES: The primary aim of this study was to investigate the value of attenuation imaging (ATI), a novel ultrasound technique for detection of steatosis, by comparing the results to that obtained with controlled attenuation parameter (CAP) and by using MRI-derived proton density fat fraction (PDFF) as reference standard. METHODS: From March to November 2018, 114 consecutive adult subjects potentially at risk of steatosis and 15 healthy controls were enrolled. Each subject underwent ATI and CAP assessment on the same day. MRI-PDFF was performed within a week. RESULTS: The prevalence of steatosis, as defined by MRI-PDFF ≥ 5%, was 70.7%. There was a high correlation of ATI with MRI-PDFF (r = 0.81, P < 0.0001). The correlation of CAP with MRI-PDFF and with ATI, respectively, was moderate (r = 0.65, P < 0.0001 and r = 0.61, P < 0.0001). The correlation of ATI or CAP with PDFF was not affected by age, gender, or body mass index. Area under the receiver operating characteristics of ATI and CAP, respectively, were 0.91 (0.84–0.95; P < 0.0001) and 0.85 (0.77–0.91; P < 0.0001) for detecting S > 0 steatosis (MRI-PDFF ≥ 5%); 0.95 (0.89–0.98; P < 0.0001) and 0.88 (0.81–0.93; P < 0.0001) for detecting S > 1 steatosis (MRI-PDFF ≥ 16.3%). The cutoffs of ATI and CAP, respectively, were 0.63 dB/cm/MHz and 258 dB/m for detecting S > 0 liver steatosis; 0.72 dB/cm/MHz and 304 dB/m for detecting S > 1 steatosis. ATI performed better than CAP, and this improvement was statistically significant for S > 1 (P = 0.04). DISCUSSION: This study shows that, in patients with no fibrosis/mild fibrosis, ATI is a very promising tool for the noninvasive assessment of steatosis.

Aims: In type 2 diabetes, we aimed at clarifying the role of glycated haemoglobin variability and other risk factors in the development of the main micro-vascular complications: peripheral neuropathy, nephropathy and retinopathy. Methods: In a single-centre cohort of 900 patients, glycated haemoglobin variability was evaluated as intra-individual standard deviation, adjusted standard deviation and coefficient of variation of serially measured glycated haemoglobin in the 2-year period before a randomly selected index visit. We devised four models considering different aspects of glycated haemoglobin evolution. Multivariate stepwise logistic regression analysis was performed including the following covariates at the index visit: age, disease duration, body mass index, total cholesterol, high-density lipoprotein cholesterol, triglycerides, sex, smoking habit, hypertension, dyslipidemia, treatment with anti-diabetic drugs, occurrence of macro-vascular events and the presence of another micro-vascular complication. Results: Males with high mean glycated haemoglobin, long duration of diabetes, presence of macro-vascular events and retinopathy emerged at higher risk for peripheral neuropathy. Development of nephropathy was independently associated with higher glycated haemoglobin variability, older age, male sex, current smoking status, presence of retinopathy, of peripheral neuropathy and of hypertension. Higher mean glycated haemoglobin, younger age, longer duration of diabetes, reduced estimated glomerular filtration rate and the presence of peripheral neuropathy were significantly associated with increased incidence of retinopathy. Conclusion: Glycated haemoglobin variability was associated with increased incidence of nephropathy, while mean glycated haemoglobin emerged as independent risk factor for the development of retinopathy and peripheral neuropathy. The presence of macro-vascular events was positively correlated with peripheral neuropathy. Finally, the occurrence of another micro-vascular complication was found to be a stronger risk factor for developing another micro-vascular complication than the mean or variability of glycated haemoglobin.

Big data technologies are nowadays providing health care with powerful instruments to gather and analyze large volumes of heterogeneous data collected for different purposes, including clinical care, administration, and research. This makes possible to design IT infrastructures that favor the implementation of the so-called “Learning Healthcare System Cycle,” where healthcare practice and research are part of a unique and synergic process. In this paper we highlight how “Big Data enabled” integrated data collections may support clinical decision-making together with biomedical research. Two effective implementations are reported, concerning decision support in Diabetes and in Inherited Arrhythmogenic Diseases.

Summary Objective Metformin is widely used for the treatment of type 2 diabetes. Growing evidence supports the beneficial effects of metformin also in patients with polycystic ovary syndrome (PCOS). It was recently reported that metformin has a TSH-lowering effect in hypothyroid patients with diabetes being treated with metformin. Design Aim of this study was to evaluate the effect of metformin treatment on the thyroid hormone profile in patients with PCOS. Patients and measurements Thirty-three patients with PCOS were specifically selected for being either treated with levothyroxine for a previous diagnosis of hypothyroidism (n = 7), untreated subclinically hypothyroid (n = 2) or euthyroid without levothyroxine treatment (n = 24) before the starting of metformin. The serum levels of TSH and FT4 were measured before and after a 4-month period of metformin therapy. Results Thyroid function parameters did not change after starting metformin therapy in euthyroid patients with PCOS. In the 9 hypothyroid patients with PCOS, the basal median serum levels of TSH (3·2 mIU/l, range = 0·4–7·1 mIU/l) significantly (P

Type 1 diabetes is often associated with other autoimmune diseases (1), including primary biliary cirrhosis (2). Furthermore, type 1 diabetes and primary biliary cirrhosis may share similar pathogenetic pathways (3). In type 1 diabetic patients, the identification of markers for associated autoimmune diseases may permit earlier diagnosis and more effective treatment. A 46-year-old man with type 1 diabetes (age of onset 26 years) was admitted into our hospital due to poor glycemic control (HbA1c 11.3%) with severe daily hypoglycemia and significant hyperglycemic spikes. At admission, routine blood tests showed mild eosinophilia (6.7%, 482.4/mmc versus normal 1–4%, 72–282/mmc) and markedly elevated values for γ-glutamyl transpeptidase (γGT) (203 units/l versus normal, 8–61) and alkaline phosphatase (571 units/l …