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The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.

MRI can assess plaque composition and has demonstrated an association between some atherosclerotic risk factors (RF) and markers of plaque vulnerability in naive patients. We aimed at investigating this association in medically treated asymptomatic patients. This is a cross-sectional interim analysis (August 2013–September 2016) of a single center prospective study on carotid plaque vulnerability (MAGNETIC study). We recruited patients with asymptomatic carotid atherosclerosis (US stenosis > 30%, ECST criteria), receiving medical treatments at a tertiary cardiac rehabilitation. Atherosclerotic burden and plaque composition were quantified with 3.0 T MRI. The association between baseline characteristics and extent of lipid-rich necrotic core (LRNC), fibrous cap (CAP) and intraplaque hemorrhage (IPH) was studied with multiple regression analysis. We enrolled 260 patients (198 male, 76%) with median age of 71-y (interquartile range: 65–76). Patients were on antiplatelet therapy, ACE-inhibitors/angiotensin receptor blockers and statins (196–229, 75–88%). Median LDL-cholesterol was 78 mg/dl (59–106), blood pressure 130/70 mmHg (111–140/65–80), glycosylated hemoglobin 46 mmol/mol (39–51) and BMI 25 kg/m2 (23–28); moreover, 125 out of 187 (67%) patients were ex-smokers. Multivariate analysis of a data-set of 487 (94%) carotid arteries showed that a history of hypercholesterolemia, diabetes, hypertension or smoking did not correlate with LRNC, CAP or IPH. Conversely, maximum stenosis was the strongest independent predictor of LRNC, CAP and IPH (p

Objective The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity. Materials and Methods Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site. Results The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability—up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review. Discussion We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions. Conclusions We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.

Objectives Despite the widespread diffusion of continuous glucose monitoring (CGM) systems, which includes both real-time CGM (rtCGM) and intermittently scanned CGM (isCGM), an effective application of CGM technology in clinical practice is still limited. The study aimed to investigate the relationship between isCGM-derived glycemic metrics and glycated hemoglobin (HbA1c), identifying overall CGM targets and exploring the inter-subject variability. Methods A group of 27 children and adolescents with type 1 diabetes under multiple daily injection insulin-therapy was enrolled. All participants used the isCGM Abbott’s FreeStyle Libre system on average for eight months, and clinical data were collected from the Advanced Intelligent Distant-Glucose Monitoring platform. Starting from each HbA1c exam date, windows of past 30, 60, and 90 days were considered to compute several CGM metrics. The relationships between HbA1c and each metric were explored through linear mixed models, adopting an HbA1c target of 7%. Results Time in Range and Time in Target Range show a negative relationship with HbA1c (R2>0.88) whereas Time Above Range and Time Severely Above Range show a positive relationship (R2>0.75). Focusing on Time in Range in 30-day windows, random effect represented by the patient’s specific intercept reveals a high variability compared to the overall population intercept. Conclusions This study confirms the relationship between several CGM metrics and HbA1c; it also highlights the importance of an individualized interpretation of the CGM data.

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

Diabetes is a high-prevalence disease that leads to an alteration in the patient&rsquo, s blood glucose (BG) values. Several factors influence the subject&rsquo, s BG profile over the day, including meals, physical activity, and sleep. Wearable devices are available for monitoring the patient&rsquo, s BG value around the clock, while activity trackers can be used to record his/her sleep and physical activity. However, few tools are available to jointly analyze the collected data, and only a minority of them provide functionalities for performing advanced and personalized analyses. In this paper, we present AID-GM, a web application that enables the patient to share with his/her diabetologist both the raw BG data collected by a flash glucose monitoring device, and the information collected by activity trackers, including physical activity, heart rate, and sleep. AID-GM provides several data views for summarizing the subject&rsquo, s metabolic control over time, and for complementing the BG profile with the information given by the activity tracker. AID-GM also allows the identification of complex temporal patterns in the collected heterogeneous data. In this paper, we also present the results of a real-world pilot study aimed to assess the usability of the proposed system. The study involved 30 pediatric patients receiving care at the Fondazione IRCCS Policlinico San Matteo Hospital in Pavia, Italy.

Objective In this work, we propose an ontology-driven approach to identify events and their attributes from episodes of care included in medical reports written in Italian. For this language, shared resources for clinical information extraction are not easily accessible. Materials and methods The corpus considered in this work includes 5432 non-annotated medical reports belonging to patients with rare arrhythmias. To guide the information extraction process, we built a domain-specific ontology that includes the events and the attributes to be extracted, with related regular expressions. The ontology and the annotation system were constructed on a development set, while the performance was evaluated on an independent test set. As a gold standard, we considered a manually curated hospital database named TRIAD, which stores most of the information written in reports. Results The proposed approach performs well on the considered Italian medical corpus, with a percentage of correct annotations above 90% for most considered clinical events. We also assessed the possibility to adapt the system to the analysis of another language (i.e., English), with promising results. Discussion and conclusion Our annotation system relies on a domain ontology to extract and link information in clinical text. We developed an ontology that can be easily enriched and translated, and the system performs well on the considered task. In the future, it could be successfully used to automatically populate the TRIAD database.

In this work we describe the application of a careflow mining algorithm to detect the most frequent patterns of care in a type 2 diabetes patients cohort. The applied method enriches the detected patterns with clinical data to define temporal phenotypes across the studied population. Novel phenotypes are discovered from heterogeneous data of 424 Italian patients, and compared in terms of metabolic control and complications. Results show that careflow mining can help to summarize the complex evolution of the disease into meaningful patterns, which are also significant from a clinical point of view.

[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.

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.

Display Omitted A careflow mining approach designed to analyze heterogeneous longitudinal data is proposed.Temporal electronic phenotyping of EHR and billing data is performed.Better insights on the health care processes of breast cancer patients are obtained. In this work we present a careflow mining approach designed to analyze heterogeneous longitudinal data and to identify phenotypes in a patient cohort. The main idea underlying our approach is to combine methods derived from sequential pattern mining and temporal data mining to derive frequent healthcare histories (careflows) in a population of patients. This approach was applied to an integrated data repository containing clinical and administrative data of more than 4000 breast cancer patients. We used the mined histories to identify sub-cohorts of patients grouped according to healthcare activities pathways, then we characterized these sub-cohorts with clinical data. In this way, we were able to perform temporal electronic phenotyping of electronic health records (EHR) data.

Background Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a leading cause of sudden cardiac death, but its progression over time and predictors of arrhythmias are still being defined. Objectives This study sought to describe the clinical course of ARVC and occurrence of life-threatening arrhythmic events (LAE) and cardiovascular mortality; identify risk factors associated with increased LAE risk; and define the response to therapy. Methods We determined the clinical course of 301 consecutive patients with ARVC using the Kaplan-Meier method adjusted to avoid the bias of delayed entry. Predictors of LAE over 5.8 years of follow-up were determined with Cox multivariable analysis. Treatment efficacy was assessed comparing LAE rates during matched time intervals. Results A first LAE occurred in 1.5 per 100 person-years between birth and age 20 years, in 4.0 per 100 person-years between ages 21 and 40 years, and in 2.4 per 100 person-years between ages 41 and 60 years. Cumulative probability of a first LAE at follow-up was 14% at 5 years, 23% at 10 years, and 30% at 15 years. Higher risk of LAE was predicted by atrial fibrillation (hazard ratio [HR]: 4.38; p = 0.002), syncope (HR: 3.36; p Conclusions The high risk of life-threatening arrhythmias in patients with ARVC spans from adolescence to advanced age, reaching its peak between ages 21 and 40 years. Atrial fibrillation, syncope, participation in strenuous exercise after the diagnosis of ARVC, hemodynamically tolerated sustained monomorphic ventricular tachycardia, and male sex predicted lethal arrhythmias at follow-up. The lack of efficacy of antiarrhythmic therapy and the life-saving role of the implantable cardioverter-defibrillator highlight the importance of risk stratification for patient management.

Background:Quinidine at high dose is suggested as antiarrhythmic treatment in patients with Brugada Syndrome (BrS), but its efficacy to prevent life-threatening arrhythmic events (LAE) in this population is unproven and its use limited by frequent side effects. We assessed whether low-dose quinidine in patients with BrS reduces the following: (1) the occurrence of LAEs at follow-up, as compared to no therapy, in a population of both high-risk survivors of LAE and lower risk patients asymptomatic for LAEs and (2) the arrhythmic burden in the high-risk group of cardiac arrest survivors.Methods:We compared the clinical course of 53 patients with BrS treated with quinidine to that of 441 untreated controls, matched by sex, age, symptoms, and the duration of observation. Furthermore, we calculated the annual incidence of LAE off-quinidine and on-quinidine in 123 patients with BrS who were cardiac arrest survivors.Results:Fifty-three patients with BrS (89% males, median age 39.8 years) received quinidine (439±115 mg/d) for 5.0±3.7 years. Therapy was stopped in 3 cases (6%) for side effects. Quinidine reduced by 26% the risk of experiencing an LAE in cases versus controls (hazard ratio, 0.74; 95% CI, 0.22–2.48;P=0.62). Furthermore, in 27 of 123 patients with BrS symptomatic for LAEs who were treated for 7.0±3.5 years, the annual rate of LAEs decreased from 14.7% while off-quinidine to 3.9% while on-quinidine (P=0.03). Of note, recurrent LAEs were recorded in 4 (15%) cardiac arrest survivors while on-quinidine.Conclusions:We demonstrated for the first time in the long-term that low-dose quinidine reduces recurrent LAEs in patients with BrS who had already survived an LAE, with few side effects. Remarkably, 15% of patients symptomatic for LAEs experience recurrent life-threatening arrhythmias while on-treatment, suggesting that quinidine cannot replace implantable defibrillators in high-risk subjects.

Lombardy was the first area in Italy to have an outbreak of coronavirus disease 19 (COVID-19) at the beginning of 2020. In this context, cancer has been reported as a major risk factor for adverse outcomes and death, so oncology societies have quickly released guidelines on cancer care during the pandemic. The aim of this study was to investigate the management of cancer patients and oncological treatments during the COVID-19 pandemic and to describe the containment measures performed in our outpatient clinic at Pavia (Lombardy). A comparison with the same period of the four previous years (2019, 2018, 2017, and 2016) was also performed. Using our electronic databases, we evaluated the number and characteristics of patients accessing the hospital for anticancer drug infusion from 24 February, 2020 to 30 April, 2020 and the number of radiological exams performed. Although a significant reduction in access for therapy was seen when compared with 2019 (2590 versus 2974, access rate ratio (ARR) = 0.85, p &lt, 0.001), no significant differences in access numbers and ARR was evident between 2020 and 2018, 2017, or 2016 (2590 versus 2626 (ARR = 0.07), 2660 (ARR = 0.99), and 2694 (ARR = 0.96), respectively, p &gt, 0.05). In 2020, 63 patients delayed treatment: 38% for &ldquo, pandemic fear&rdquo, 18% for travel restrictions, 13% for quarantine, 18% for flu syndrome other than COVID-19, and 13% for worsening of clinical conditions and death. Only 7/469 patients developed COVID-19. A significant reduction in radiological exams was found in 2020 versus all the other years considered (211 versus 360, 355, 385, 390 for the years 2020, 2019, 2018, 2017, and 2016, respectively, p &lt, 0.001). The low incidence of COVID-19 among our cancer patients, along with the hospital policy to control infection, enabled safe cancer treatment and a continuum of care in most patients, while a small fraction of patients experienced a therapeutic delay due to patient-related reasons.

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.

Diagnostic accuracy and reliable estimation of clinical evolution are challenging issues in the management of patients with disorders of consciousness (DoC). Longitudinal systematic investigations conducted in large cohorts of patients with DoC could make it possible to identify reliable diagnostic and prognostic markers. On the basis of this consideration, we devised a multicentre prospective registry for patients with DoC admitted to ten intensive rehabilitation units. The registry collects homogeneous and detailed data on patients' demographic and clinical features, neurophysiological and neuroimaging findings, and medical and surgical complications. Here we present the rationale and the design of the registry and the preliminary results obtained in 53 patients with DoC (vegetative state or minimally conscious state) enrolled during the first seven months of the study. Data at 6-month post-injury follow-up were available for 46 of them. This registry could be an important tool for collecting high-quality data through the application of rigorous methods, and it could be used in the routine management of patients with DoC admitted to rehabilitation settings.

Short QT syndrome (SQTS) is a rare and life-threatening arrhythmogenic syndrome characterized by abbreviated repolarization. Hydroquinidine (HQ) prolongs the QT interval in SQTS patients, although whether it reduces cardiac events is currently unknown. This study investigated whether long-term treatment with HQ reduces the occurrence of life-threatening arrhythmic events (LAE) (cardiac arrest or sudden cardiac death) in SQTS patients. In this cohort study on consecutive SQTS patients, 2 analyses were performed: 1) a matched-period analysis for the occurrence of LAE in 17 SQTS patients who received long-term HQ; and 2) a comparison of the annual incidence of LAE off- and on-HQ in 16 SQTS patients who survived a cardiac arrest. A total of 17 patients (82% male, age 29 ± 3 years, QTc before treatment 331 ± 3 ms) received HQ therapy (584 ± 53 mg/day). Therapy was stopped in 2 cases (12%) due to gastrointestinal intolerance, and 15 patients continued treatment for 6 ± 1 year. QTc prolongation was observed in all patients (by 60 ± 6 ms; p < 0.001). We compared the occurrence of LAE during 6 ± 1 years before and after HQ, observing that patients on HQ experienced a reduction in both the rate of LAE from 40% to 0% (p = 0.03) and the number of LAE per patient from 0.73 ± 0.3 to 0 (p = 0.026). Furthermore, the annual rate of LAE in the 16 patients with a previous cardiac arrest dropped from 12% before HQ to 0 on therapy (p = 0.028). We demonstrated for the first time that treatment with HQ was associated with a lower incidence of LAE in SQTS patients. These data point to the importance that quinidine, that in several countries has been removed from the market, remains available worldwide for patients with SQTS. In the present study, therapy with HQ has been proven to be safe, with a relatively low rate of side effects. This work was supported by the Ricerca Corrente Funding scheme of the Italian Ministry of Health. Dr. Bellazzi is a shareholder of Biomeris; and is a stakeholder of Engenome. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Pedro Brugada, MD, served as Guest Editor for this paper. Sí