Evidence map›Paper›PMID 35482840›Full record

ReviewCirculation research2022

Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension.

Christopher J Rhodes, Andrew J Sweatt, Bradley A Maron

Abstract readReview
In one paragraph

Review in Circulation research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  17. Constructing the Framework for Disease Modification in Pulmonary Arterial Hypertension.American journal of respiratory and critical care medicine · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Christopher J RhodesDepartment of Medicine, National Heart and Lung Institute, Imperial College London, United Kingdom (C.J.R.).ORCID 0000-0002-4962-3204
Andrew J SweattDepartment of Medicine, National Heart and Lung Institute, Imperial College London, United Kingdom (C.J.R.).
Bradley A MaronDivision of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA (B.A.M.).ORCID 0000-0002-6784-764X

Funding

Targeting FAK-NEDD9 protein-protein interaction to prevent focal adhesion assembly and vascular proliferation in pulmonary arterial hypertensionR01HL139613 · NHLBI · UNIVERSITY OF MARYLAND BALTIMORE · PI Sirano Dhe-Paganon, Bradley Maron · 2018 to 2026
$4.1M
NEDD9-SMAD3, fibrinolysis, and chronic thromboembolic pulmonary hypertensionR01HL153502 · NHLBI · UNIVERSITY OF MARYLAND BALTIMORE · PI MARON, BRADLEY · 2020 to 2023
$1.7M
Personalized protein-protein interactomes and precision medicine in pulmonary arterial hypertensionR01HL155096 · NHLBI · UNIVERSITY OF MARYLAND BALTIMORE · PI MARON, BRADLEY · 2021 to 2024
$1.6M
Reclassifying Pulmonary Arterial Hypertension Into Immune Phenotypes Using Machine LearningK23HL151892 · NHLBI · STANFORD UNIVERSITY · PI SWEATT, ANDREW JOHN · 2020 to 2024
$942k
British Heart Foundation FS/15/59/31839NHLBI NIH HHS K23 HL151892NHLBI NIH HHS R01 HL139613NHLBI NIH HHS R01 HL153502NHLBI NIH HHS R01 HL155096
6 · The paper itself

Abstract

Pulmonary hypertension is a complex disease with multiple causes, corresponding to phenotypic heterogeneity and variable therapeutic responses. Advancing understanding of pulmonary hypertension pathogenesis is likely to hinge on integrated methods that leverage data from health records, imaging, novel molecular -omics profiling, and other modalities. In this review, we summarize key data sets generated thus far in the field and describe analytical methods that hold promise for deciphering the molecular mechanisms that underpin pulmonary vascular remodeling, including machine learning, network medicine, and functional genetics. We also detail how genetic and subphenotyping approaches enable earlier diagnosis, refined prognostication, and optimized treatment prediction. We propose strategies that identify functionally important molecular pathways, bolstered by findings across multi-omics platforms, which are well-positioned to individualize drug therapy selection and advance precision medicine in this highly morbid disease.

Indexed as

Big DataHypertension, PulmonaryHumansMachine LearningPrecision Medicinebig datainformation disseminationphenotypeprecision medicinepulmonary arterial hypertension

Identifiers

PMID35482840
PMCPMC9070103

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.