Recent advances in cardiac imaging for patients with heart failure
Bibliographic record
Abstract
PURPOSE OF REVIEW: Transthoracic echocardiography (ECHO) and single-photon emission computed tomography (SPECT) are routine in many heart failure patients. Recently, other imaging tests have emerged in heart failure management: cardiovascular magnetic resonance (CMR), positron emission tomography (PET) and computed tomography (CT). This article reviews recent developments in heart failure imaging. RECENT FINDINGS: Longitudinal left ventricular systolic strain on ECHO speckle tracking imaging detects subclinical cardiomyopathy and predicts survival in symptomatic heart failure. Late gadolinium enhancement for myocardial scar is an independent predictor of death or transplantation in ischemic and nonischemic cardiomyopathy. In contrast to earlier reports, both ECHO and CMR contrast have a negligible risk of adverse outcomes. Stress perfusion imaging (SPECT or PET) and PET flow quantification have prognostic value in ischemic cardiomyopathy. F-2-fluoro-2-deoxyglucose (FDG) PET directed management can impact outcome. Abnormal myocardial neuronal activity on I-metaiodobenzylguanidine (MIBG) imaging is associated with increased risk of ventricular arrhythmias and death. Cardiac CT potentially could assess heart failure etiology through coronary angiography and myocardial tissue characterization but its precise role remains undetermined. SUMMARY: There have been several exciting developments in all imaging modalities. Large multicenter trials such as IMAGE heart failure are required to standardize measures and establish benefit before widespread use in heart failure can be recommended.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".