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Record W4401523326 · doi:10.21037/cdt-23-466

Quadratic stratification of left ventricular hypertrophy and association with mitral insufficiency grading: a retrospective study using cardiac magnetic resonance

2024· article· en· W4401523326 on OpenAlexafffundabout
Monisha Ghosh Srabanti, Julio García

Bibliographic record

VenueCardiovascular Diagnosis and Therapy · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsAlberta Children's HospitalUniversity of CalgaryLibin Cardiovascular Institute of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchAlberta InnovatesAlberta Innovates - Health Solutions
KeywordsMedicineCardiologyLeft ventricular hypertrophyInternal medicineGrading (engineering)Cardiac magnetic resonanceMagnetic resonance imagingRetrospective cohort studyRadiology

Abstract

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Background: Chronic primary mitral regurgitation (MR) is caused by the defect in >1 component of the mitral valve, potentially leading to left ventricular hypertrophy (LVH). The relationship between LVH subtypes and the insufficiency grading of chronic MR remains unclear. Thus, we aimed to investigate this association and explore the impact of unhealthy habits on LVH development in patients with chronic primary MR through a cross-sectional study. Methods: Cardiac magnetic resonance (CMR) data was retrospectively collected from 3T magnetic resonance imaging (MRI) scanners in 71 patients with chronic primary MR (range, 20-84 years, 52% men). Considered patients (with mild-to-severe MR) were enrolled between March 2015 and September 2022 from the Cardiovascular Imaging Registry of Calgary (CIROC) database. Left ventricle (LV) function was assessed using cvi42 v5.11.5. Patients were categorized into 'mild-to-severe' MR using regurgitation fraction (RF), according to the current imaging guidelines. LVH subtypes were determined using mass-to-volume (M/V) calculations. IBM SPSS was used to run all the statistical analyses. This study employed normality checks by using the Shapiro-Wilk test; one-way analysis of variance (ANOVA) and Kruskal-Wallis tests with post-hoc pairwise comparisons; Chi-squared tests, Fisher's Exact test, crosstabulation analysis, and multinomial logistic regression to examine relationships between MR severity, LVH types, and impact of lifestyle factors, significance at P<0.05. Results: 17.2%, P=0.26). No differences were observed between age groups ('Young-Middle' = under 60 years, and 'Middle-Old' = over 60 years). Still, there were notable differences in LVH prevalence within the 'Young-Middle' age group for mild-moderate (P=0.01) and moderate-severe MR (P=0.02). Eccentric LVH was associated with higher body mass index (BMI), smoking, and frequent alcohol consumption [odds ratio (OR) 1.02, 95% confidence interval (CI): 0.56-1.26; OR 1.65, 95% CI: 1.31-6.52; OR 1.15, 95% CI: 0.26-1.34], while CR was solely associated with increased BMI (smokers OR =1.84, 95% CI: 1.25-3.91 and alcohol consumers OR =1.32, 95% CI: 0.86-2.48). Nicotine and caffeine consumption did not appear to be a risk factor for LVH (nicotine: eccentric, OR =0.99, 95% CI: 0.65-1.86; CR, OR =0.97, 95% CI: 0.69-2.39 and caffeine: eccentric, OR =0.69, 95% CI: 0.48-1.61; CR, OR =0.97, 95% CI: 0.78-4.01). Conclusions: This study reveals sex-based associations between LVH subtypes and severity of chronic primary MR. Lifestyle factors such as cigarette smoking, alcohol consumption, and elevated BMI influence LVH risk, while nicotine and caffeine consumption exhibit minimal effects.

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)0.000
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 score0.288
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.014
GPT teacher head0.277
Teacher spread0.262 · 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".

Quick stats

Citations2
Published2024
Admission routes3
Has abstractyes

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