Evidence map›Paper›PMID 38485244›Full record

ArticleJournal of magnetic resonance imaging : JMRI2024

Quantitative Characterization of Respiratory Patterns on Dynamic Higher Temporal Resolution MRI to Stratify Postacute Covid-19 Patients by Cardiopulmonary Symptom Burden.

Lea Azour, Henry Rusinek, Artem Mikheev, Nicholas Landini, Mahesh Bharath Keerthivasan, Christoph Maier, Barun Bagga, Mary Bruno, Rany Condos, William H Moore and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of magnetic resonance imaging : JMRI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.4field-weighted citation impact, top 22% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Review
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

11 authors at 5 institutions in 2 countries.

Lea AzourDepartment of Radiology, New York University Grossman School of Medicine, NYU Langone Health, New York, New York, USA.ORCID 0000-0002-3658-8956
Henry RusinekDepartment of Radiology, New York University Grossman School of Medicine, NYU Langone Health, New York, New York, USA.
Artem MikheevDepartment of Radiology, New York University Grossman School of Medicine, NYU Langone Health, New York, New York, USA.
Nicholas LandiniDepartment of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I Hospital, Sapienza Rome University, Rome, Italy.
Mahesh Bharath KeerthivasanSiemens Medical Solutions USA, Inc., New York, New York, USA.
Christoph MaierDepartment of Radiology, New York University Grossman School of Medicine, NYU Langone Health, New York, New York, USA.ORCID 0000-0001-7354-4795
Barun BaggaDepartment of Radiology, New York University Grossman Long Island School of Medicine, NYU Langone Health, New York, New York, USA.
Mary BrunoDepartment of Radiology, New York University Grossman School of Medicine, NYU Langone Health, New York, New York, USA.
Rany CondosDepartment of Medicine, New York University Grossman School of Medicine, NYU Langone Health, New York, New York, USA.
William H MooreDepartment of Radiology, New York University Grossman School of Medicine, NYU Langone Health, New York, New York, USA.
Hersh ChandaranaDepartment of Radiology, New York University Grossman School of Medicine, NYU Langone Health, New York, New York, USA.
NYU Langone Health · USLong Island University · USPoliclinico Umberto I · ITSiemens (United States) · USUniversity of California, Los Angeles · US

Funding

TR&D 4: Revealing Microstructure: Biophysical modeling and validation for discovery and clinical careP41EB017183 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Daniel K Sodickson · 2014 to 2026
$19.3M
Genetic and Immuno-inflammatory Drivers of Post-acute Pulmonary Sequelae of SARS-CoV-2R01HL163604 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Steven B Abramson, RANY CONDOS · 2023 to 2026
$3.4M
Advanced Software for MRI, PET, SPECT and CT Image AnalysisU24EB028980 · NIBIB · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI RUSINEK, HENRY · 2019 to 2021
$1.2M
German Research Foundation 512359237NHLBI NIH HHS 1R0 HL163604-01A1NHLBI NIH HHS R01 HL163604NIBIB/NIH P41EB017183NIBIB NIH HHS P41 EB017183NIBIB NIH HHS U24 EB028980
6 · The paper itself

Abstract

backgroundPostacute Covid-19 patients commonly present with respiratory symptoms; however, a noninvasive imaging method for quantitative characterization of respiratory patterns is lacking. PURPOSE: To evaluate if quantitative characterization of respiratory pattern on free-breathing higher temporal resolution MRI stratifies patients by cardiopulmonary symptom burden. STUDY TYPE: Prospective analysis of retrospectively acquired data. SUBJECTS: A total of 37 postacute Covid-19 patients (25 male; median [interquartile range (IQR)] age: 58 [42-64] years; median [IQR] days from acute infection: 335 [186-449]). FIELD STRENGTH/SEQUENCE: 0.55 T/two-dimensional coronal true fast imaging with steady-state free precession (trueFISP) at higher temporal resolution. ASSESSMENT: Patients were stratified into three groups based on presence of no (N = 11), 1 (N = 14), or ≥2 (N = 14) cardiopulmonary symptoms, assessed using a standardized symptom inventory within 1 month of MRI. An automated lung postprocessing workflow segmented each lung in each trueFISP image (temporal resolution 0.2 seconds) and respiratory curves were generated. Quantitative parameters were derived including tidal lung area, rates of inspiration and expiration, lung area coefficient of variability (CV), and respiratory incoherence (departure from sinusoidal pattern) were. Pulmonary function tests were recorded if within 1 month of MRI. Qualitative assessment of respiratory pattern and lung opacity was performed by three independent readers with 6, 9, and 23 years of experience. STATISTICAL TESTS: Analysis of variance to assess differences in demographic, clinical, and quantitative MRI parameters among groups; univariable analysis and multinomial logistic regression modeling to determine features predictive of patient symptom status; Akaike information criterion to compare the quality of regression models; Cohen and Fleiss kappa (κ) to quantify inter-reader reliability. Two-sided 5% significance level was used.

resultsTidal area and lung area CV were significantly higher in patients with two or more symptoms than in those with one or no symptoms (area: 15.4 cm DATA

conclusionQuantitative respiratory pattern measures derived from dynamic higher-temporal resolution MRI have potential to stratify patients by symptom burden in a postacute Covid-19 cohort. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Indexed as

COVID-19LungMagnetic Resonance ImagingAdultFemaleHumansMaleMiddle AgedPost-Acute COVID-19 SyndromeProspective StudiesRespirationRetrospective StudiesSARS-CoV-2Symptom Burden0.55 Tfree‐breathinghigher‐temporal resolutionlow‐fieldrespiratory pattern quantification

Identifiers

PMID38485244
PMCPMC11399317
OpenAlexW4394765648

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.