Utilization and impact of cardiovascular magnetic resonance on patient management in heart failure: insights from the SCMR Registry
Bibliographic record
Abstract
Abstract Background Cardiovascular magnetic resonance (CMR) is an important diagnostic test used in the evaluation of patients with heart failure (HF). However, the demographics and clinical characteristics of those undergoing CMR for evaluation of HF are unknown. Further, the impact of CMR on subsequent HF patient care is unclear. The goal of this study was to describe the characteristics of patients undergoing CMR for HF and to determine the extent to which CMR leads to changes in downstream patient management by comparing pre-CMR indications and post-CMR diagnoses. Methods We utilized the Society for Cardiovascular Magnetic Resonance (SCMR) Registry as our data source and abstracted data for patients undergoing CMR scanning for HF indications from 2013 to 2019. Descriptive statistics (percentages, proportions) were performed on key CMR and clinical variables of the patient population. The Fisher’s exact test was used when comparing categorical variables. The Wilcoxon rank sum test was used to compare continuous variables. Results 3,837 patients were included in our study. 94% of the CMRs were performed in the United States with China, South Korea and India also contributing cases. Median age of HF patients was 59.3 years (IQR, 47.1, 68.3 years) with 67% of the scans occurring on women. Almost 2/3 of the patients were scanned on 3T CMR scanners. Overall, 49% of patients who underwent CMR scanning for HF had a change between the pre-test indication and post CMR diagnosis. 53% of patients undergoing scanning on 3T had a change between the pre-test indication and post CMR diagnosis when compared to 44% of patients who were scanned on 1.5T (p < 0.01). Conclusion Our results suggest a potential impact of CMR scanning on downstream diagnosis of patients referred for CMR for HF, with a larger potential impact on those scanned on 3T CMR scanners.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".