Real-world evidence evaluation of LDL-C in hospitalized patients: a population-based observational study in the timeframe 2021-2022.

AIMS: European registries and retrospective cohort studies have highlighted the failure to achieve low-density lipoprotein-cholesterol (LDL-C) targets in many very high-risk patients. Hospitalized patients are often frail, and frailty is associated with all-cause and cardiovascular mortality. The aim of this study is to evaluate LDL-C levels in a real-world inpatient setting, identifying cardiovascular risk categories and highlighting treatment gaps in the implementation of LDL-C management. METHODS: This retrospective, observational study included all adult patients admitted to an Italian hospital between 2021 and 2022 with available LDL-C values during hospitalization. Disease-related real-world data were collected from Hospital Information System using automated data extraction strategies and through the implementation of a patient-centered data repository (the Dyslipidemia Data Mart). We performed assessment of cardiovascular risk profiles, LDL-C target achievement according to the 2019 ESC/EAS guidelines, and use of lipid-lowering therapies (LLT). RESULTS: 13,834 patients were included: 17.15%, 13.72%, 16.82% and 49.76% were low (L), moderate (M), high (H) and very high-risk (VH) patients, respectively. The percentage of on-target patients was progressively lower towards the worst categories (78.79% in L, 58.38% in M, 33.3% in H and 21.37% in VH). Among LLT treated patients, 28.48% were on-target in VH category, 47.60% in H, 69.12% in M and 68.47% in L. We also analyzed the impact of monotherapies and combination therapies on target achievement. CONCLUSIONS: We found relevant gaps in LDL-C management in the population of inpatients, especially in the VH category. Future efforts should be aimed at reducing cardiovascular risk in these subjects.

Eligibility for the 4 Pharmacological Pillars in Heart Failure With Reduced Ejection Fraction at Discharge.

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, ß-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.

GENERATOR HEART FAILURE DataMart: An integrated framework for heart failure research.

Background: Heart failure (HF) is a multifaceted clinical syndrome characterized by different etiologies, risk factors, comorbidities, and a heterogeneous clinical course. The current model, based on data from clinical trials, is limited by the biases related to a highly-selected sample in a protected environment, constraining the applicability of evidence in the real-world scenario. If properly leveraged, the enormous amount of data from real-world may have a groundbreaking impact on clinical care pathways. We present, here, the development of an HF DataMart framework for the management of clinical and research processes. Methods: Within our institution, Fondazione Policlinico Universitario A. Gemelli in Rome (Italy), a digital platform dedicated to HF patients has been envisioned (GENERATOR HF DataMart), based on two building blocks: 1. All retrospective information has been integrated into a multimodal, longitudinal data repository, providing in one single place the description of individual patients with drill-down functionalities in multiple dimensions. This functionality might allow investigators to dynamically filter subsets of patient populations characterized by demographic characteristics, biomarkers, comorbidities, and clinical events (e.g., re-hospitalization), enabling agile analyses of the outcomes by subsets of patients. 2. With respect to expected long-term health status and response to treatments, the use of the disease trajectory toolset and predictive models for the evolution of HF has been implemented. The methodological scaffolding has been constructed in respect of a set of the preferred standards recommended by the CODE-EHR framework. Results: Several examples of GENERATOR HF DataMart utilization are presented as follows: to select a specific retrospective cohort of HF patients within a particular period, along with their clinical and laboratory data, to explore multiple associations between clinical and laboratory data, as well as to identify a potential cohort for enrollment in future studies; to create a multi-parametric predictive models of early re-hospitalization after discharge; to cluster patients according to their ejection fraction (EF) variation, investigating its potential impact on hospital admissions. Conclusion: The GENERATOR HF DataMart has been developed to exploit a large amount of data from patients with HF from our institution and generate evidence from real-world data. The two components of the HF platform might provide the infrastructural basis for a combined patient support program dedicated to continuous monitoring and remote care, assisting patients, caregivers, and healthcare professionals.

Multidisciplinary Tumor Board Smart Virtual Assistant in Locally Advanced Cervical Cancer: A Proof of Concept.

AIM: The first prototype of the "Multidisciplinary Tumor Board Smart Virtual Assistant" is presented, aimed to (i) Automated classification of clinical stage starting from different free-text diagnostic reports; (ii) Resolution of inconsistencies by identifying controversial cases drawing the clinician's attention to particular cases worthy for multi-disciplinary discussion; (iii) Support environment for education and knowledge transfer to junior staff; (iv) Integrated data-driven decision making and standardized language and interpretation. PATIENTS AND METHOD: Data from patients affected by Locally Advanced Cervical Cancer (LACC), FIGO stage IB2-IVa, treated between 2015 and 2018 were extracted. Magnetic Resonance (MR), Gynecologic examination under general anesthesia (EAU), and Positron Emission Tomography-Computed Tomography (PET-CT) performed at the time of diagnosis were the items from the Electronic Health Records (eHRs) considered for analysis. An automated extraction of eHR that capture the patient's data before the diagnosis and then, through Natural Language Processing (NLP), analysis and categorization of all data to transform source information into structured data has been performed. RESULTS: In the first round, the system has been used to retrieve all the eHR for the 96 patients with LACC. The system has been able to classify all patients belonging to the training set and - through the NLP procedures - the clinical features were analyzed and classified for each patient. A second important result was the setup of a predictive model to evaluate the patient's staging (accuracy of 94%). Lastly, we created a user-oriented operational tool targeting the MTB who are confronted with the challenge of large volumes of patients to be diagnosed in the most accurate way. CONCLUSION: This is the first proof of concept concerning the possibility of creating a smart virtual assistant for the MTB. A significant benefit could come from the integration of these automated methods in the collaborative, crucial decision stages.

GENERATOR Breast DataMart-The Novel Breast Cancer Data Discovery System for Research and Monitoring: Preliminary Results and Future Perspectives.

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.

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19.

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.