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.
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.
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.
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.
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.
There is, so far, no universal definition of severe asthma. This definition usually relies on: number of exacerbations, inhaled therapy, need for oral corticosteroids, and respiratory function. The ...use of such parameters varies in the different definitions used. Thus, according to the parameters chosen, each patient may result in having severe asthma or not. The aim of this study was to evaluate how the choice of a specific definition of severe asthma can change the allocation of patients.
Data collected from the Severe Asthma Network Italy (SANI) registry were analyzed. All the patients included were then reclassified according to the definitions of U-BIOPRED, NICE, WHO, ATS/ERS, GINA, ENFUMOSA, and TENOR.
540 patients, were extracted from the SANI database. We observed that 462 (86%) met the ATS/ERS criteria as well as the GINA criteria, 259 (48%) the U-Biopred, 222 (41%) the NICE, 125 (23%) the WHO, 313 (58%) the Enfumosa, and 251 (46%) the TENOR criteria. The mean eosinophil value were similar in the ATS/ERS, U-Biopred, and Enfumosa (528, 532 and 516 cells/mcl), higher in WHO and Tenor (567 and 570 cells/mcl) and much higher in the NICE classification (624 cells/mcl). Lung function tests resulted similarly in all groups, with WHO (67%) and ATS/ERS-GINA (73%), respectively, showing the lower and upper mean FEV1 values.
The present observations clearly evidence the heterogeneity in the distribution of patients when different definitions of severe asthma are used. However, the recent definition of severe asthma, provided by the GINA document, is similar to that indicated in 2014 by ATS/ERS, allowing mirror reclassification of the patients examined. This lack of homogeneity could complicate the access to biological therapies. The definition provided by the GINA document, which reflects what suggested by ATS/ERS, could partially overcome the problem.
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