Background: Guidelines recommend using multiple drugs in patients with heart failure (HF) with reduced ejection fraction, but there is a paucity of real‐world data on the simultaneous initiation of the 4 pharmacological pillars at discharge after a decompensation event. Methods and Results: A retrospective data mart, including patients diagnosed with HF, was implemented. Consecutively admitted patients with HF with reduced ejection fraction were selected through an automated approach and categorized according to the number/type of treatments prescribed at discharge. The prevalence of contraindications and cautions for HF with reduced ejection fraction treatments was systematically assessed. Logistic regression models were fitted to assess predictors of the number of treatments (≥2 versus <2 drugs) prescribed and the risk of rehospitalization. A population of 305 patients with a first episode of HF hospitalization and a diagnosis of HF with reduced ejection fraction (ejection fraction, <40%) was selected. At discharge, 49.2% received 2 current recommended drugs, β‐blockers were prescribed in 93.4%, while a renin‐angiotensin system inhibitor or an angiotensin receptor–neprilysin inhibitor was prescribed in 68.2%. A mineralocorticoid receptor antagonist was prescribed in 32.5%, although none of the patients showed contraindications to mineralocorticoid receptor antagonist prescription. A sodium‐glucose cotransporter 2 inhibitor could be prescribed in 71.1% of patients. On the basis of current recommendations, 46.2% could receive the 4 foundational drugs at discharge. Renal dysfunction was associated with <2 foundational drugs prescribed. After adjusting for age and renal function, use of ≥2 drugs was associated with lower risk of rehospitalization during the 30 days after discharge. Conclusions: A quadruple therapy could be directly implementable at discharge, potentially providing prognostic advantages. Renal dysfunction was the main prevalent condition limiting this approach.

Background: Artificial Intelligence (AI) is increasingly used for process management in daily life. In the medical field AI is becoming part of computerized systems to manage information and encourage the generation of evidence. Here we present the development of the application of AI to IT systems present in the hospital, for the creation of a DataMart for the management of clinical and research processes in the field of breast cancer. Materials and methods: A multidisciplinary team of radiation oncologists, epidemiologists, medical oncologists, breast surgeons, data scientists, and data management experts worked together to identify relevant data and sources located inside the hospital system. Combinations of open-source data science packages and industry solutions were used to design the target framework. To validate the DataMart directly on real-life cases, the working team defined tumoral pathology and clinical purposes of proof of concepts (PoCs). Results: Data were classified into “Not organized, not ‘ontologized’ data”, “Organized, not ‘ontologized’ data”, and “Organized and ‘ontologized’ data”. Archives of real-world data (RWD) identified were platform based on ontology, hospital data warehouse, PDF documents, and electronic reports. Data extraction was performed by direct connection with structured data or text-mining technology. Two PoCs were performed, by which waiting time interval for radiotherapy and performance index of breast unit were tested and resulted available. Conclusions: GENERATOR Breast DataMart was created for supporting breast cancer pathways of care. An AI-based process automatically extracts data from different sources and uses them for generating trend studies and clinical evidence. Further studies and more proof of concepts are needed to exploit all the potentials of this system.

The health emergency linked to the SARS-CoV-2 pandemic has highlighted problems in the health management of chronic patients due to their risk of infection, suggesting the need of new methods to monitor patients. People living with HIV/AIDS (PLWHA) represent a paradigm of chronic patients where an e-health-based remote monitoring could have a significant impact in maintaining an adequate standard of care. The key objective of the study is to provide both an efficient operating model to “follow” the patient, capture the evolution of their disease, and establish proximity and relief through a remote collaborative model. These dimensions are collected through a dedicated mobile application that triggers questionnaires on the basis of decision-making algorithms, tagging patients and sending alerts to staff in order to tailor interventions. All outcomes and alerts are monitored and processed through an innovative e-Clinical platform. The processing of the collected data aims into learning and evaluating predictive models for the possible upcoming alerts on the basis of past data, using machine learning algorithms. The models will be clinically validated as the study collects more data, and, if successful, the resulting multidimensional vector of past attributes will act as a digital composite biomarker capable of predicting HIV-related alerts. Design: All PLWH > 18 sears old and stable disease followed at the outpatient services of a university hospital (n = 1500) will be enrolled in the interventional study. The study is ongoing, and patients are currently being recruited. Preliminary results are yielding monthly data to facilitate learning of predictive models for the alerts of interest. Such models are learnt for one or two months of history of the questionnaire data. In this manuscript, the protocol—including the rationale, detailed technical aspects underlying the study, and some preliminary results—are described. Conclusions: The management of HIV-infected patients in the pandemic era represents a challenge for future patient management beyond the pandemic period. The application of artificial intelligence and machine learning systems as described in this study could enable remote patient management that takes into account the real needs of the patient and the monitoring of the most relevant aspects of PLWH management today.

In diabetes, there is a lower concordance between estimated and directly measured low-density lipoprotein cholesterol (LDL-C) values. In previous studies, the Martin-Hopkins (MH) and the modified Sampson (mS) equations emerged as novel LDL-C estimating methods with a higher concordance with direct measurement than the Friedewald (F) equation. Our prior analysis of an entire population of inpatients showed a progressive decline in LDL-C target attainment from low to higher-risk categories, with only 32.5% of patients with diabetes reaching the target. This analysis aimed to compare LDL-C levels calculated using the F, MH, and mS equations in this population, and to evaluate the clinical implications of adopting these approaches on cardiovascular risk categorization - both in the overall cohort and within diabetes and non-diabetes subgroups. Retrospective real-world data were extracted from the Hospital Information System using automated data extraction strategies and stored in a patient-centered repository (the Dyslipidaemia Data Mart). LDL-C was calculated using the F, MH, and mS equations. Goal achievement for LDL-C was evaluated using the 3 equations, overall and for diabetic and nondiabetic subgroups. We then assessed the effect of the switch from the traditional method to these novel approaches on patient risk categorization. A total of 13,834 patients were included. Overall, patients at goal were 35.8% with MH and 32.6% with mS, both lower compared with F (38.9%). The percentage of patients at goal (according to F) who were reclassified as not at goal (according to MH and mS) was significantly higher in diabetes compared with nondiabetes (5.1% vs 2.8% for reclassification from F to MH, P < .001; 8.3% vs 5.3% for reclassification from F to mS, P < .001). Distance to target was also higher with MH and mS, particularly among patients with diabetes. Accurate LDL-C estimation is critical for the cardiovascular risk management of people with diabetes. The choice of calculation method can significantly influence both target achievement and therapeutic decisions, with the MH and mS equations identifying a larger proportion of patients with diabetes as not at goal compared with the F equation.

Background: Diabetes of the exocrine pancreas (DEP) is a form of diabetes that arises in the presence of an exocrine pancreas disease and is mainly associated with acute and chronic pancreatitis. Therapeutic management of DEP has not changed over the past decades, and treatment is mostly insulin based. However, type 2 diabetes (T2D) treatment guidelines have changed with the aim of normalizing glucose levels and preventing diabetes-related comorbidities. Objectives: The aim of this study was to evaluate real-word practice in the treatment of diabetes associated with pancreatitis, with a focus on individuals who had already developed cardiovascular and/or renal diseases. Designs: We conducted a cross-sectional observational study on adult patients admitted to a tertiary-level hospital in Italy between 2018 and 2022 with a prevalent diagnosis of pancreatitis and diabetes. Methods: Disease-related real-world data were collected from Hospital Information Systems using automated data extraction strategies and through the implementation of a patient-centered data repository. A multiple source real-world evidence-based methodology was used to identify patients with diabetes and to investigate changes in glucose-lowering treatments through hospitalizations. Results: We identified 2146 hospitalized patients with pancreatitis from 2018 to 2022, 675 (31.4%) of whom also had diabetes. Data on glucose-lowering therapies were available for 444 of these patients. Individuals with diabetes and pancreatitis were predominantly male (65.2%) and older than those without diabetes ( p < 0.001). At hospital admission, 41.9% were on insulin therapy and 44.4% on other drugs, with metformin being the most prescribed. After hospitalization, insulin use increased significantly (61.2%, p < 0.001) while metformin prescriptions decreased (from 44.7% to 26.2%, p < 0.001). Patients with cardiorenal comorbidities showed similar therapeutic patterns, with a pronounced post-discharge reduction in metformin use. Conclusion: This real-world study provides updated evidence on the treatment of diabetes associated with pancreatitis in a tertiary care setting, showing a predominant use of insulin and metformin regardless of comorbidities. Despite emerging evidence on the benefits of non-insulin therapies in T2D, such approaches are not yet reflected in the management of DEP, underlining the need for specific clinical guidelines and dedicated trials.