Evidence map›Paper›PMID 34774527›Full record

ArticleChest2022

The Right Heart Network and Risk Stratification in Pulmonary Arterial Hypertension.

Francois Haddad, Kevin Contrepois, Myriam Amsallem, Andre Y Denault, Roberto J Bernardo, Alokkumar Jha, Shalina Taylor, Jennifer Arthur Ataam, Olaf Mercier, Tatiana Kuznetsova and 3 more

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Chest, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
2.5field-weighted citation impact, top 10% of its field
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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Review
  12. Article
  13. Review
  14. Observational
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 5 institutions in 4 countries.

Francois HaddadDivision of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA; Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA; Vera Moulton Wall Center for Pulmonary Disease at Stanford University, Stanford, CA. Electronic address: fhaddad@stanford.edu.
Kevin ContrepoisStanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA; Department of Genetics, Stanford University School of Medicine, Stanford, CA.
Myriam AmsallemDivision of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA; Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA; Vera Moulton Wall Center for Pulmonary Disease at Stanford University, Stanford, CA.
Andre Y DenaultDepartment of Anesthesiology and Division of Critical Care, Montreal Heart Institute, University of Montréal, Montréal, QC, Canada.
Roberto J BernardoVera Moulton Wall Center for Pulmonary Disease at Stanford University, Stanford, CA; Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA.
Alokkumar JhaStanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA.
Shalina TaylorStanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA.
Jennifer Arthur AtaamStanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA.
Olaf MercierDepartment of Cardiovascular and Thoracic Surgery, Centre Chirurgical Marie-Lannelongue, Paris-South University, Paris, France.
Tatiana KuznetsovaDivision of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA; Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium.
Anton Vonk NoordegraafDepartment of Pulmonary Medicine, VU University Medical Center, Amsterdam, The Netherlands.
Roham T ZamanianStanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA; Vera Moulton Wall Center for Pulmonary Disease at Stanford University, Stanford, CA; Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA.
Andrew J SweattStanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA; Vera Moulton Wall Center for Pulmonary Disease at Stanford University, Stanford, CA; Division of Pulmonary, Allergy, and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA.
Cardiovascular Institute of the South · USStanford University · USAmsterdam UMC Location VUmc · NLCentre Chirurgical Marie Lannelongue · FRUniversité de Montréal · CA

Funding

The Cellular Response to Elafin in PAHP01HL108797 · NHLBI · STANFORD UNIVERSITY · PI NOLAN, GARRY P · 2011 to 2021
$19.7M
Reclassifying Pulmonary Arterial Hypertension Into Immune Phenotypes Using Machine LearningK23HL151892 · NHLBI · STANFORD UNIVERSITY · PI SWEATT, ANDREW JOHN · 2020 to 2024
$942k
NHLBI NIH HHS K23 HL151892NHLBI NIH HHS P01 HL108797
6 · The paper itself

Abstract

backgroundPrognosis in pulmonary arterial hypertension (PAH) is closely related to indexes of right ventricular function. A better understanding of their relationship may provide important implications for risk stratification in PAH. RESEARCH QUESTION: Can clinical network graphs inform risk stratification in PAH? STUDY DESIGN AND

methodsThe study cohort consisted of 231 patients with PAH followed up for a median of 7.1 years. An undirected, correlation network was used to visualize the relationship between clinical features in PAH. This network was enriched for right heart parameters and included N-terminal pro-hormone B-type natriuretic peptide (NT-proBNP), comprehensive echocardiographic parameters, and hemodynamics, as well as 6-min walk distance (6MWD), vital signs, laboratory data, and diffusing capacity for carbon monoxide (Dlco). Connectivity was assessed by using eigenvector and betweenness centrality to reflect global and regional connectivity, respectively. Cox proportional hazards regression was used to model event-free survival for the combined end point of death or lung transplantation.

resultsA network of closely intertwined features centered around NT-proBNP with 6MWD emerging as a secondary hub were identified. Less connected nodes included Dlco, systolic BP, albumin, and sodium. Over the follow-up period, death or transplantation occurred in 92 patients (39.8%). A strong prognostic model was achieved with a Harrell's C-index of 0.81 (0.77-0.85) when combining central right heart features (NT-proBNP and right ventricular end-systolic remodeling index) with 6MWD and less connected nodes (Dlco, systolic BP, albumin, sodium, sex, connective tissue disease etiology, and prostanoid therapy). When added to the baseline risk model, serial change in NT-proBNP significantly improved outcome prediction at 5 years (increase in C-statistic of 0.071 ± 0.024; P = .003).

interpretationNT-proBNP emerged as a central hub in the intertwined PAH network. Connectivity analysis provides explainability for feature selection and combination in outcome models.

Indexed as

Pulmonary Arterial HypertensionAlbuminsBiomarkersFamilial Primary Pulmonary HypertensionHumansNatriuretic Peptide, BrainPeptide FragmentsPrognosisRisk AssessmentSodiumVentricular RemodelingAlbuminsBiomarkersNatriuretic Peptide, BrainPeptide FragmentsSodiumcardiovascular imaginggraph theorypulmonary arterial hypertensionpulmonary hypertensionright heart failurerisk stratificationunsupervised learning

Identifiers

PMID34774527
PMCPMC9131051
OpenAlexW3213760928

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.