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Abstract 4143538: A Predictive Tool and Diagnostic Screening Algorithm for the Identification of Transthyretin Amyloid Cardiomyopathy in High-Risk Patient Populations

2024· article· en· W4404359666 on OpenAlexaffabout
Jocelyn Chai, Andrew Starovoytov, Christine M. Pierce, Nathaniel M. Hawkins, Sean Virani, Michael Luong, Lynn Straatman, Marla Kiess, Daniel F. Worsley, Janarthanan Sathananthan, Nowell M. Fine, Margot K. Davis

Bibliographic record

VenueCirculation · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmyloidosis: Diagnosis, Treatment, Outcomes
Canadian institutionsUniversity of CalgaryUniversity of British Columbia
Fundersnot available
KeywordsMedicineTransthyretinCardiomyopathyIdentification (biology)Amyloid (mycology)Internal medicineAlgorithmCardiologyBioinformaticsPathologyHeart failure

Abstract

fetched live from OpenAlex

Introduction: Transthyretin amyloid cardiomyopathy (ATTR-CM) is an underdiagnosed disease that may result in heart failure (HF), arrhythmias, and valvular disease. Our aim was to develop (1) screening criteria to identify high-risk patients for ATTR-CM and (2) our own predictive tool of ATTR-CM. Methods: This was a prospective observational registry at 2 academic sites in Canada. We designed screening criteria to identify high-risk patients in HF, atrial fibrillation, transcatheter valve clinics, and in cardiologist’s offices from January 2019-December 2022. Patients >60 years were included if one of several screening criteria was met and they were referred for pyrophosphate scan by the cardiologist. Univariate and multivariate logistic regression were used to identify predictive clinical, imaging, and biochemical characteristics. Results: In total, 2500 patients were screened, and 200 patients were enrolled with a follow-up duration of 3 years. The mean age was 78 years and 65% were male. Forty-six (23%) had a diagnosis of ATTR-CM and 7 (4%) were diagnosed with AL-amyloidosis. ATTR-CM patients were older (83±7 vs. 77±8; p<0.001), predominantly male (80 vs. 60%, p=0.01), symptomatic (NYHA III-IV) (46 vs. 19%; p<0.001) and had higher NT-proBNP (3633 vs. 2018; p=0.01). Fewer were on beta-blocker (p<0.001) and renin-angiotensin inhibitors (p=0.01), and more were on amiodarone (p=0.008). They had larger left ventricular posterior wall diameters (14±3 vs. 11±2; p<0.001). On electrocardiogram, ATTR-CM patients had lower voltages (37 vs. 4%; p<0.001) and more atrioventricular blocks (39 vs. 19%; p=0.02). Tissue doppler (E/e’) was higher in ATTR-CM patients (18±7 vs. 14±6; p=0.001). The positive predictive value (PPV) for our screening criteria ranged from 16-38%, with the highest PPV and negative predictive value (NPV) for age ≥70 years and new HF (PPV 38%, NPV 95%). We identified 5 key predictors of ATTR-CM to develop a practical tool for clinicians with a score of ≥7 meeting criteria for further testing (Figure 1). This tool had a sensitivity of 89%, specificity of 85%, PPV of 64%, NPV of 96%, and an area under receiver operating characteristic curve of 0.9. Conclusion: Broad screening criteria applied to high-risk patient populations yielded new ATTR-CM diagnoses in 23% of patients. Screening tools for ATTR-CM can be used to help clinicians identify patients who should undergo further testing. Further studies are needed to validate our predictive tool.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.255
Teacher spread0.243 · 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 designSimulation or modeling
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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Citations0
Published2024
Admission routes2
Has abstractyes

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