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Record W4391934180 · doi:10.1016/j.ijcard.2024.131892

Tissue mapping by cardiac magnetic resonance imaging for the prognostication of cardiac amyloidosis: A systematic review and meta-analysis

2024· review· en· W4391934180 on OpenAlexaff
Sean Cai, Hourmazd Haghbayan, Kelvin Chan, Djeven P. Deva, Laura Jiménez‐Juan, Kim A. Connelly, Ming‐Yen Ng, Raymond T. Yan, Andrew T. Yan

Bibliographic record

VenueInternational Journal of Cardiology · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmyloidosis: Diagnosis, Treatment, Outcomes
Canadian institutionsSt. Michael's HospitalUniversity Health NetworkSunnybrook HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineCardiac amyloidosisCardiac magnetic resonanceMagnetic resonance imagingMeta-analysisCardiac magnetic resonance imagingAmyloidosisCardiologyRadiologyInternal medicinePathology

Abstract

fetched live from OpenAlex

BackgroundCardiac amyloidosis is increasingly recognized as a significant contributor to cardiovascular morbidity and mortality. With the emergence of novel therapies, there is a growing interest in prognostication of patients with cardiac amyloidosis using cardiac magnetic resonance imaging (CMR). In this systematic review and meta-analysis, we aimed to examine the prognostic significance of myocardial native T1 and T2, and extracellular volume (ECV).MethodsObservational cohort studies or single arms of clinical trials were eligible. MEDLINE, EMBASE and CENTRAL were systematically searched from their respective dates of inception to January 2023. No exclusions were made based on date of publication, study outcomes, or study language. The study populations composed of adult patients (≥18 years old) with amyloid cardiomyopathy. All studies included the use of CMR with and without intravenous gadolinium contrast administration to assess myocardial native T1 mapping, T2 mapping, and ECV in association with the pre-specified primary outcome of all-cause mortality. Data were extracted from eligible primary studies by two independent reviewers and pooled via the inverse variance method using random effects models for meta-analysis.ResultsA total of 3852 citations were reviewed. A final nine studies including a total of 955 patients (mean age 65 ± 10 years old, 32% female, mean left ventricular ejection fraction (LVEF) 59 ± 12% and 24% had NYHA class III or IV symptoms) with cardiac amyloidosis [light chain amyloidosis (AL) 50%, transthyretin amyloidosis (ATTR) 49%, other 1%] were eligible for inclusion and suitable for data extraction. All included studies were single centered (seven with 1.5 T MRI scanners, two with 3.0 T MRI scanners) and non-randomized in design, with follow-up spanning from 8 to 64 months (median follow-up = 25 months); 320 patients died during follow-up, rendering a weighted mortality rate of 33% across studies. Compared with patients with AL amyloid, patients with ATTR amyloid had significantly higher mean left ventricular mass index (LVMi), (102 ± 34 g/m2 vs 127 ± 37 g/m2, p = 0.02). N-terminal pro-brain natriuretic peptide (NT-proBNP), troponin T levels, mean native T1 values, ECV and T2 values did not differ between patients with ATTR amyloid and AL amyloid (all p > 0.25). Overall, the hazard ratios for mortality were 1.33 (95% CI = [1.10, 1.60]; p = 0.003; I2 = 29%,) for every 60 ms higher T1 time, 1.16 (95% CI = [1.09, 1.23], p < 0.0001; I2 = 76%) for every 3% increase in ECV, and 5.23 (95% CI = [2.27, 12.02]; p < 0.0001; I2 = 0%) for myocardial-to-skeletal T2 ratio below the mean (vs above the mean).ConclusionHigher native T1 time and ECV, and lower myocardial to skeletal T2 ratio, on CMR are associated with worse mortality in patients with cardiac amyloidosis. Therefore, tissue mapping using CMR may offer a useful non-invasive technique to monitor disease progression and determine prognosis in patients with cardiac amyloidosis.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.032
GPT teacher head0.350
Teacher spread0.318 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations14
Published2024
Admission routes1
Has abstractyes

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