Abstract 4143538: A Predictive Tool and Diagnostic Screening Algorithm for the Identification of Transthyretin Amyloid Cardiomyopathy in High-Risk Patient Populations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".