Systematic Review and Meta-Analysis of Cardiac MRI T1 and ECV Measurements in Pre-Heart Failure Populations
Bibliographic record
Abstract
Background/Objectives: Heart failure (HF) often develops from a prolonged asymptomatic phase where early detection could prevent progression. Pre-heart failure (pre-HF) populations—those with risk factors (Stage A) or subclinical myocardial changes (Stage B)—are critical for intervention. Cardiac magnetic resonance (CMR) with T1 and extracellular volume (ECV) mapping offers a non-invasive approach to detect early myocardial changes in these groups. This systematic review evaluates the role of T1 and ECV mapping in pre-HF populations, focusing on their diagnostic and prognostic utility. Methods: A systematic search of PubMed, EMBASE, and Cochrane was conducted up to April 2025, identifying 17 studies that met inclusion criteria. Data was extracted directly into Excel, and methodological quality was assessed using the Newcastle–Ottawa Scale (NOS) for cohort and cross-sectional studies and AMSTAR-2 for systematic reviews and meta-analyses. A meta-analysis was performed using Review Manager (RevMan) to compare T1 and ECV values between pre-HF and control groups. Results: Studies consistently reported elevated T1 (989.6–1415.41 milliseconds) and ECV (25.7–42.81%) in pre-HF groups compared to controls (T1: 967–1310.63 ms, ECV: 23.5–29.9%). Meta-analysis showed a significant increase in T1 (MD: 27.62 ms, 95% CI: 8.04–47.19, p < 0.006) and ECV (MD: 2.97%, 95% CI: 1.88–4.06, p < 0.00001) in pre-HF groups. RQS scores ranged from 17.2% to 77.8% (mean: 37.9%), and NOS scores ranged from 5 to 8 (mean: 6.2), reflecting variability in study quality. The AMSTAR-2 rating for the systematic review was moderate. Conclusions: T1 and ECV mapping enhance CMR-based detection of early myocardial changes in pre-HF, offering a promising non-invasive approach to predict HF risk. However, variability in study quality, small sample sizes, and methodological inconsistencies limit generalisability. Future research should focus on standardised protocols, prospective designs, and multi-center studies to integrate these techniques into clinical practice, potentially guiding preventive therapies such as SGLT2is and tafamidis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".