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Record W4282947122 · doi:10.3389/fcvm.2022.900301

Characteristics of Right Ventricular Blood Flow in Patients With Chronic Thromboembolic Pulmonary Hypertension: An Analysis With 4-Dimensional Flow Cardiovascular Magnetic Resonance Imaging

2022· article· en· W4282947122 on OpenAlexfundno aff
Wenqing Xu, Xuebiao Sun, Xincao Tao, Dingyi Wang, Yanan Zhen, Xiaopeng Liu, Jing An, Wanmu Xie, Min Liu

Bibliographic record

VenueFrontiers in Cardiovascular Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsnot available
FundersChina-Japan Friendship HospitalNational Natural Science Foundation of ChinaCircle Cardiovascular ImagingChinese Academy of Medical SciencesNational Social Science Fund of ChinaNatural Science Foundation of Beijing Municipality
KeywordsMagnetic resonance imagingMedicineCardiologyInternal medicinePulmonary hypertensionBlood flowChronic thromboembolic pulmonary hypertensionCardiac magnetic resonanceCardiac magnetic resonance imagingRadiology

Abstract

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Background Blood flow is closely related to function, but currently, the relationship of right ventricular (RV) blood flow components with RV function and hemodynamics in patients with chronic thromboembolic pulmonary hypertension (CTEPH) remains unclear. Our objective is to qualify RV function with 4-dimensional flow cardiovascular magnetic resonance (4D-Flow CMR) imaging and to investigate the correlation between RV flow and hemodynamics in patients with CTEPH. Methods Retrospective enrollment included 67 patients with CTEPH (mean age 47.8±14.2 years, 47 men) who underwent CMR and right heart catheterization (RHC) within 2 days. RHC was used to evaluate hemodynamics. RV flow components including the percentages of direct flow (PDF), retained inflow (PRI), delayed ejection flow (PDEF), and residual volume (PRVo) were quantified on 4D-Flow sequence. RV functional metrics were determined with the CINE balanced steady-state free precession sequence. The sum of PDF and PDEF was compared with RV eject fraction (RVEF). The correlation among RV flow components, RV functional metrics and hemodynamics was analyzed with spearman correlation analysis. Results The median (interquartile range) of RVEF, PDF, PDEF, PRI, and PRVo, respectively was 35.5% (18.2, 45.6%), 18% (8.4, 21.4%), 15.1% (13.5, 19.0%), 15.9% (13.8, 20.8%), and 50.6% (35.6, 60.4%). The sum of PDF and PDEF is 35.1% (24.8, 46.6%), which was similar to RVEF (z = 0.58, p = 0.561). PDF negatively correlated with right ventricular end-systolic volume index (RVESVI), right ventricular myocardial mass index (RVMI) and right ventricular global longitudinal strain (r = −0.61, −0.65, −0.64, p < 0.001). PRVo positively correlated with RVESVI and RVMI (r = 0.50, 0.58, p < 0.001). PDF negatively correlated with pulmonary vascular resistance (PVR) (r = −0.72, p < 0.001) while it positively correlated with cardiac output (CO) and cardiac index (CI) (r = 0.64 & 0.52, p < 0.001). PRVo positively correlated with mean pulmonary pressure and PVR (r = 0.57&0.54, p < 0.001). Total five patients died in the perioperative period. RVEF in the deceased patients was similar to survivors (z = −1.163, p = 0.092). In comparison with the survivors, RVPDF in the deceased patients significantly reduced (z = −2.158, p = 0.029) while RVPDEF, RVPRI, and RVPRVo in deceased patients were similar to survivors. Conclusion 4D-Flow CMR can provide simultaneous quantification of RV function and hemodynamics in the assessment of CTEPH without breath-holding. The reduced PDF and increased PRVo were the main characteristics of RV flow in CTEPH.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.196
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations4
Published2022
Admission routes1
Has abstractyes

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