The Mayo ATTR-CM Score Versus Other Diagnostic Scores and Cardiac Biomarkers in Patients with Suspected Cardiac Amyloidosis
Bibliographic record
Abstract
AIMS: Several scores were developed to help the diagnosis of cardiac amyloidosis (CA). The most recent one, being the Mayo transthyretin amyloidosis cardiomyopathy (ATTR-CM) score, was not externally validated. We compared the diagnostic performance of the ATTR-CM score with previous tools (increased wall thickness [IWT] score, AMYLoidosis Index [AMYLI] score, and cardiac biomarkers) in a cohort of patients evaluated for a suspicion of CA. METHODS AND RESULTS: We analysed 362 consecutive patients referred to a third-level centre for suspected CA. Overall, 132 (36%) had transthyretin CA (ATTR-CA), and 91 (25%) immunoglobulin light chain CA (AL-CA); CA was excluded in 139 (38%). ATTR-CM score had a good diagnostic performance to distinguish ATTR-CA from AL-CA or no CA, with an area under the curve (AUC) of 0.795 (95% confidence interval [CI] 0.747-0.842, p < 0.001), and ATTR-CA from no CA (AUC 0.822, 95% CI 0.774-0.871, p < 0.001). Results were consistent in both patients with preserved (AUC 0.787, 95% CI 0.726-0.848, p < 0.001), and reduced or mildly reduced ejection fraction (AUC 0.790, 95% CI 0.709-0.871, p < 0.001). The ATTR-CM score showed a better discrimination compared to IWT and AMYLI score to distinguish ATTR-CA from AL-CA or no CA (p = 0.002), but not to distinguish ATTR-CA from no CA (p = 0.270). Diagnostic accuracy was significantly higher for the ATTR-CM score as compared to the rule-in cut-off of high-sensitivity troponin T. CONCLUSIONS: The Mayo ATTR-CM score has a good performance in identifying patients with ATTR-CA, with also better discrimination power when compared to other scores and biomarkers.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".