Diagnostic Test Accuracy of Cardiac Imaging for AL Amyloidosis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Although endomyocardial biopsy is the gold standard, echocardiography, and Cardiac Magnetic Resonance (CMR) play a pivotal role in the diagnosis of cardiac amyloidosis. In this systematic review, we aim to estimate the diagnostic test accuracy (DTA) of CMR and echocardiography for cardiac amyloidosis. Methods: As part of the American Society of Hematology guidelines on the diagnosis of Amyloidosis, we searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials from inception till January 2024 for relevant studies. Two reviewers independently performed title and abstract screening and full-text article screening on LASER Al and extracted relevant data using a piloted Excel sheet. We performed statistical pooling using Stata 18.0 software. We evaluated risk of bias using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) and used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to assess the certainty of the evidence. We report diagnostic test accuracy as sensitivity (95% confidence interval) and specificity (95% confidence interval). Results: After screening 29,237, we included 9 studies assessing DTA of late gadolinium enhancement (LGE) on cardiac magnetic resonance (CMR) using an endomyocardial biopsy (EMB) as a reference test in patients with amyloidosis (light chain and ATTR). The majority of studies didn't report on the type of amyloidosis where only 2 out of 9 were in patients with light chain amyloidosis. The pooled estimates for sensitivity and specificity of LGE are 0.95 (0.88. 0.98), and 0.81 (0.60, 0.92), respectively. We did a sensitivity analysis and included only LGE suggestive for amyloidosis which didn't change sensitivity but led to an increase in specificity for amyloidosis to 0.87 (0.76, 0.94), respectively. The pooled estimates were based on moderate certainty in the evidence. For 2-dimensional echocardiography, we addressed DTA for different echocardiographic findings and we did not limit the reference to EMB. A panel of experts judged whether the reference was acceptable or not. Acceptable reference was EMB alone or a combination of extracardiac biopsy plus cardiac imaging (CMR or echocardiography) with or without positive cardiac biomarkers (troponin or BNP or NT-proBNP). Six studies (n=2063) addressed interventricular septum thickness (IVS), sensitivity was 0.77 (0.69, 0.84) and specificity was 0.71 (0.60, 0.81). 4 out of 6 studies used 12 mm as a cutoff while the other 2 studies used 13 mm as a cutoff. Only 2 studies (n=1667) assessed the DTA of posterior wall thickness (PWT), with sensitivity ranging from 0.76 to 1.00, and specificity ranging from 0.84 to 0.89. Six studies (n=438) addressed diastolic dysfunction (grade 2,3), with a sensitivity of 0.71 (0.40, 0.90), and specificity of 0.75 (0.64, 0.84). 7 studies (n=472) reported DTA of only diastolic dysfunction grade 3. When compared to diastolic dysfunction (grade 2,3, sensitivity decreases to 0.42 (0 28. 0.58) and specificity increases to 0.89 (0.83. 0.94). For E/A ratio, only 2 studies (n=127) were included. Sensitivity ranged from 0.45 to 0.65, and specificity ranged from 0.85 to 0.98. Four studies (n=1596) yielded a sensitivity of 0.86 (0.65, 0.95) and specificity of 0.76 (0.55, 0.89) of left ventricular global longitudinal strain (GLS). All except one used -17% as a cutoff for GLS. For apical sparing on GLS, pooled estimates from 8 studies (n=2253) led to a sensitivity of 0.72 (0.64, 0.78) and specificity of 0.78 (0.64, 0.88). There was a variation of cutoff across studies. The certainty of evidence for echocardiographic findings was low to moderate. Conclusion: CMR is a more accurate test with better performance compared to echocardiography.
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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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".