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Record W4323348077 · doi:10.18087/cardio.2023.2.n2380

The association between cardiac mr feature tracking strain and myocardial late gadolinium enhancement in patients with hypertrophic cardiomyopathy

2023· article· en· W4323348077 on OpenAlexaboutno aff
Özge Özden Tok, Serkan Ünlü, İsmail Doğu Kılıç, Sara Abu Sherif, Selçuk Opan, Hatice Kemal, Emre Özmen, Haşim Tüner, Gülsüm Bingöl, Ahmet Barutçu, Muharrem Nasıfov, Selim Bakan, Ömer Göktekín

Bibliographic record

VenueKardiologiia · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHypertrophic cardiomyopathyCardiologyFeature trackingInternal medicineMedicineStrain (injury)CardiomyopathyGadoliniumFeature (linguistics)Tracking (education)Artificial intelligenceHeart failurePattern recognition (psychology)Computer sciencePsychologyMaterials science

Abstract

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Aim Hypertrophic cardiomyopathy (HCM) is a relatively common, heritable cardiomyopathy, and cardiac magnetic resonance (CMR) studies have been performed previously to evaluate different aspects of the disease. However, a comprehensive study, including all four cardiac chambers and analysis of left atrial (LA) function, is missing in the literature. The aim of this retrospective study was to analyze CMR-feature tracking (CMR-FT) strain parameters and atrial function of HCM patients and to investigate the association of these parameters with the amount of myocardial late gadolinium enhancement (LGE).Material and Methods In this retrospective, cross-sectional study, we analyzed the CMR images (CMRI) of 58 consecutive patients, who from February 2020 to September 2022 were diagnosed with HCM at our tertiary cardiovascular center. Patients who were younger than 18 yrs or who had moderate or severe valvular heart disease, significant coronary artery disease, previous myocardial infarction, suboptimal image quality, or with contraindication to CMR were excluded. CMRI was performed at 1.5 T with a scanner, and all scans were assessed by an experienced cardiologist and then re-assessed by an experienced radiologist. SSFP 2-, 3- and 4‑chamber, short axis views were obtained and left ventricular (LV) end-diastolic volume (EDV), end-systolic volume (ESV), ejection fraction (EF), and mass were measured. LGE images were obtained using a PSIR sequence. Native T1 and T2 mapping and post-contrast T1 map sequences were performed and each patient's myocardial extracellular volume (ECV) was calculated. LA volume index (LAVI), LA ejection fraction (LAEF), LA coupling index (LACI) were calculated. The complete CMR analysis of each patient was performed with CVI 42 software (Circle CVi, Calgary, Canada), off-line.Results The patients were divided into two groups, HCM with LGE (n=37, 64 %) and HCM without LGE (n=21, 36 %). The average patient age in the HCM patients with LGE was 50.8±14 yrs and 47±12.9 yrs in the HCM patients without LGE. Maximum LV wall thickness and basal antero-septum thickness were significantly higher in the HCM with LGE group compared to the HCM without LGE group (14.8±3.5 mm vs 20.3±6.5 mm (p<0.001), 14.2±3.2 mm vs 17.3±6.1 mm (p=0.015), respectively). LGE was 21.9±31.7 g and 15.7±13.4 % in the HCM with LGE group. LA area (22.2±6.1 vs 28.8±11.2 cm2; p=0.015) and LAVI (28.9±10.2 vs 45.6±23.1; p-0.004) were significantly higher in the HCM with LGE group. LACI was doubled in the HCM with LGE group (0.2±0.1 vs 0.4±0.2; p<0.001). LA strain (30.4±13.2 vs 21.3±16.2; p-0.04) and LV strain (15.2±3 vs 12.2±4.5; p=0.012) were significantly decreased in the HCM with LGE group.Conclusion This study sheds light on the CMR-FT differences between HCM with and without LGE. We found a greater burden of LA volume but significantly lower LA and LV strain in the LGE patients. These findings highlight further the LA and LV remodeling in HCM. Impaired LA function appears to have physiological significance, being associated with greater LGE. While our CMR-FT findings support the progressive nature of HCM, beginning with sarcomere dysfunction to eventual fibrosis, further studies are needed to validate these results in larger cohorts and to evaluate their clinical relevance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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 teacher head, 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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Citations1
Published2023
Admission routes1
Has abstractyes

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