The next generation of evidence-based medicine

Subbiah, Vivek

doi:10.1038/s41591-022-02160-z

Cited by 212 publications

(137 citation statements)

References 77 publications

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“…Given the trends noticed in medicine concerning the use of AI, and in particular in the cardiovascular field and in the CA diagnosis and detection, it is hypothesized that the number of scientific products related to this subject is probably destined to grow in the next few years. However, aside to the important scaling up of AI-related research, which is particularly useful to lead to new discoveries and important novelties and insights about physiopathological and etiopathological signs of the disease, important gaps are still present in the translation from bench to bedside of AI and technological advancements in general in every sector of medicine [ 176 ]. However, with the advent of new AI-based tools, which are pervasively entering everyday life (e.g., ChatGPT or similar programs), it is predictable that such novel approaches will be more often scaled into the clinical practice in the next few years, which will have significant benefits for the clinical community.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis

Allegra

Mirabile

Tonacci

et al. 2023

IJMS

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Cardiac amyloidosis is an uncommon restrictive cardiomyopathy featuring an unregulated amyloid protein deposition that impairs organic function. Early cardiac amyloidosis diagnosis is generally delayed by indistinguishable clinical findings of more frequent hypertrophic diseases. Furthermore, amyloidosis is divided into various groups, according to a generally accepted taxonomy, based on the proteins that make up the amyloid deposits; a careful differentiation between the various forms of amyloidosis is necessary to undertake an adequate therapeutic treatment. Thus, cardiac amyloidosis is thought to be underdiagnosed, which delays necessary therapeutic procedures, diminishing quality of life and impairing clinical prognosis. The diagnostic work-up for cardiac amyloidosis begins with the identification of clinical features, electrocardiographic and imaging findings suggestive or compatible with cardiac amyloidosis, and often requires the histological demonstration of amyloid deposition. One approach to overcome the difficulty of an early diagnosis is the use of automated diagnostic algorithms. Machine learning enables the automatic extraction of salient information from “raw data” without the need for pre-processing methods based on the a priori knowledge of the human operator. This review attempts to assess the various diagnostic approaches and artificial intelligence computational techniques in the detection of cardiac amyloidosis.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis

Allegra

Mirabile

Tonacci

et al. 2023

IJMS

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“…To ensure their clarity and transparency, we have described, in advance, the methodology of our project, aiming at transforming RWD into RWE in the HF field. Unquestionably, one of the major pitfalls of “traditional” clinical trials, conducted with specific populations and in specialized environments, is the lack of generalizability ( 17 ). They rely upon long lists of eligibility criteria, detailed case reporting forms that exist separately from standard medical records, accurate monitoring, and specialized research staff to ensure adherence to a well-characterized protocol ( 18 ).…”

Section: Discussionmentioning

confidence: 99%

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

D’Amario

Laborante

Delvinioti

et al. 2023

Front. Cardiovasc. Med.

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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.

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“…There is a need for evolution rather than an adaptation of EBM due to the introduction of artificial intelligence (AI) and technology in medicine. To summarize the research question asked at the beginning of this article, there is a need for a merger of EBM towards personalized medicine through methodological advances and future AI-based data analyses of all data to offer the right treatment to the right patient at the right time [ 42 ].…”

Section: Reviewmentioning

confidence: 99%

Evidence-Based Medicine: History, Review, Criticisms, and Pitfalls

Ratnani¹,

Fatima²,

Abid³

et al. 2023

Cureus

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Evidence-based medicine (EBM) is the use of high-quality clinical research in making decisions about the care of patients. Its formal origin dates back to the mid-nineteenth century, and since then, it has continued to evolve. The best research evidence, clinical expertise, and patient values are described as the foundations of EBM. However, several tools and skills have been developed and added over time. EBM has faced a lot of criticism, and the pitfalls are widely discussed and published in the medical literature. The biggest challenge is the changing paradigm of healthcare, cost-effectiveness, and changing evidence which has led to controversies and challenges in the rapid adaptation of the EBM. This review article discusses the history, conception, and evolution of modern-day EBM. In addition, we discuss why EBM has been criticized and highlight the pitfalls.

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The next generation of evidence-based medicine

Cited by 212 publications

References 77 publications

Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis

Machine Learning Approaches in Diagnosis, Prognosis and Treatment Selection of Cardiac Amyloidosis

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

Evidence-Based Medicine: History, Review, Criticisms, and Pitfalls

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