AUTHOR=D’Amario Domenico , Laborante Renzo , Delvinioti Agni , Lenkowicz Jacopo , Iacomini Chiara , Masciocchi Carlotta , Luraschi Alice , Damiani Andrea , Rodolico Daniele , Restivo Attilio , Ciliberti Giuseppe , Paglianiti Donato Antonio , Canonico Francesco , Patarnello Stefano , Cesario Alfredo , Valentini Vincenzo , Scambia Giovanni , Crea Filippo 

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

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2023.1104699

DOI=10.3389/fcvm.2023.1104699

ISSN=2297-055X

ABSTRACT=Background
Heart failure (HF) is a multifaceted clinical syndrome characterized by different etiologies. The current model, based on data from clinical trials, is limited by the biases related to a highly-selected sample. However, if properly leveraged, the enormous amount of data 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 the retrospective informations have beenare 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 allows investigators to dynamically filter subsets of patient populations, enabling them to perform sagile analyses of the outcomes these 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: the selection of a specific retrospective cohort of HF patients within a particular period at our institution, 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; the creation of multi-parametric predictive models of early re-hospitalization after discharge; clustering patients according to their ejection fraction (EF) variation in order toto investigate its potential impact on hospital admissions. 
Conclusion
The GENERATOR HF DataMart has been developed in order to exploit the large amount of data from patients with HF from our institution. 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.