Cardiovascular magnetic resonance reference ranges for cardiac function and structure and recommendations for grading severity: the Healthy Hearts Consortium
Bibliographic record
Abstract
Abstract Introduction Cardiovascular magnetic resonance (CMR) imaging offers precise quantification of cardiac structure and function. However, its clinical utility is often limited by the absence of robust, standardized reference ranges and severity grading thresholds. Aims The aim of this study was to establish age-, sex-, and ethnicity-specific reference ranges and severity grading criteria for CMR-derived ventricular and atrial parameters in healthy adults, accounting for variations between two post-processing software tools. Methods and results We analyzed CMR scans from the Healthy Hearts Consortium (HHC), which includes six multi-ethnic international cohorts. Images were automatically segmented using cvi42 (Circle Cardiovascular Imaging) and suiteHEART (Neosoft), with visual and statistical quality control. Ventricular and atrial volumes, myocardial mass, and ejection fractions were derived using short- and long-axis protocols; parameters were indexed to body surface area and height. We defined reference ranges as normal up to the 95% of the prediction interval (PI), and abnormalities as mild up to 99.73%, moderate at 99.73%, and severe at 99.99%, respectively. The final dataset included 4,624 women (51.0%) and 4,435 men (49.0%), with a mean age of 61 ± 13 years (range 18–83), and a multi-ethnic population (81.6% White, 5.6% South Asian, 5.3% Mixed/Other, 3.8% Black, 3.7% Chinese). Minor systematic differences were observed between cvi42 and suiteHEART, particularly in atrial parameters. Conclusions Our work provides an evidence-based framework for CMR severity grading, offering age-, sex-, and ethnicity-stratified thresholds for mild, moderate, and severe deviations from the reference. These reference values support improved diagnostic accuracy, better risk stratification, and enhanced comparability of CMR findings worldwide. Graphical abstract Footnote: This graphical abstract summarises the methodology and findings of our study on severity grading using cardiovascular magnetic resonance (CMR). It illustrates the dataset, quality control steps, software tools used and the derivation of population-specific reference ranges and severity grading classification. All reference ranges are available on the Healthy Hearts Consortium website ( www.healthy-hearts.org.uk ). Abbreviations: CMR: cardiovascular magnetic resonance; QC: quality control; EDV: end-diastolic volume; ESV: end-systolic volume; SV: stroke volume; EF: ejection fraction; LV: left ventricle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".