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

Abstract

BackgroundHeart 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.MethodsWithin 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.ResultsSeveral 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.ConclusionThe 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.

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

“…The method of the Gemelli Heart Failure Data Mart has been previously described. 9 It is an evidence‐focused project to develop and maintain this big data repository with a retrospective design, as a result of a collaborative effort among the clinical staff and Gemelli Data Science and Artificial Intelligence Laboratory Generator Real‐World Data. 10 Similarly, our institutional facility has realized other disease‐specific data marts, as previously published elsewhere.…”

Machine learning in heart failure diagnosis, prediction, and prognosis: review

“…To offer a potential solution to this, D’Amario et al . 44 described the GENERATOR HF DataMart, an AI laboratory that generates real-world evidence for HF patients using real-world data (RWD). They highlighted the need for big data analytics and AI to manage the volume, velocity, variety, veracity, and value of cardiovascular data.…”

“…The use of RWD and AI is an exciting area of research that has the potential to overcome the limitations of traditional clinical trials and improve the generalizability of study findings. However, careful consideration of data quality, accuracy, and reliability is necessary to ensure that the generated evidence is robust and reliable 44 .…”

Comorbidities and cardiac resynchronization therapy: Do we talk the talk or should we walk the walk?

“…Although RCTs have represented the gold standard for generating evidence in all areas of medicine, they are often designed on narrow and well-selected populations, precluding generalizability. 5 Nowadays, both the Food and Drug Administration and European Medicines Agency make extensive use of real-world evidence in their regulatory decision-making, in most cases as supplemental evidence (to the gold standard of RCT), but also, in about 10% of submissions, as a substantial or primary evidence.…”

“…4 Randomized controlled trials (RCTs), indeed, are limited in addressing this issue: specific comorbidities are often presented as exclusion criteria or, at best, reported in a very limited proportion of patients, resulting in underpowered RCTs for certain subgroups. 5 Even the impact of CRT in patients with atrial fibrillation (AF), alone or in combination with other comorbidities, remains a matter of debate 6,7 : most of the landmark trials on this topic, apart from the Resynchronization-Defibrillation for Ambulatory Heart Failure' (RAFT) and Biventricular versus Right Ventricular Pacing in Heart Failure Patients with Atrioventricular Block (BLOCK HF) trials, excluded patients with permanent AF and enrolled only individuals with sinus rhythm at randomization, who experienced intermittent (paroxysmal or persistent) episodes of AF, prior and/or during the investigation period. 8,9…”