Low Prevalence of Late Myocardial Injury on Cardiac <scp>MRI</scp> Following <scp>COVID</scp>‐19 Infection
Bibliographic record
Abstract
BACKGROUND: The prevalence of abnormal cardiac magnetic resonance imaging (MRI) findings indicative of myocardial injury in patients who recovered from coronavirus disease 2019 (COVID-19) is currently unclear, with a high variability in the reported prevalence. PURPOSE: To assess the prevalence of myocardial injury after a COVID-19 infection. STUDY TYPE: Prospective, bicentric study. SUBJECTS: Seventy consecutive patients who recovered from COVID-19 and were previously hospitalized. Mean age was 57 years and 39% of the patients were female. Ten healthy controls and a comparator group of 75 nonischemic cardiomyopathy (NICM) patients were employed. FIELD STRENGTH/SEQUENCE: 1.5-T, steady-state free precession (SSFP) gradient-echo sequence, modified Look-Locker inversion recovery sequence with balanced SSFP readout, T2-prepared spiral readout sequence and a T1-weighted inversion recovery fast gradient-echo sequence was acquired ~4-5 months after recovery from COVID-19. ASSESSMENT: The SSFP sequence was utilized for the calculation of left and right ventricular volumes and ejection fractions (LVEF and RVEF) following manual endocardial contouring. T1 and T2 mapping was performed by pixel-wise exponential fitting, and T1 and T2 values were computed by manual contouring of the left ventricular endocardial and epicardial walls. Late gadolinium enhancement (LGE) images were graded qualitatively as LGE present or absent. STATISTICAL TESTS: or Fisher's exact tests were used to compare continuous and categorical variables respectively between the COVID-19 and NICM groups. Inter-rater agreement was evaluated by the intraclass correlation coefficient for continuous variables and Cohen's kappa test for LGE. RESULTS: Reduced RVEF occurred in 10%, LGE and elevated native T1 in 9%, reduced LVEF in 4%, and elevated T2 in 3% of COVID-19 patients, respectively. Patients with NICM had lower mean LVEF (41.6% ± 6% vs. 60% ± 7%), RVEF (46% ± 5% vs. 61% ± 9%), and a significantly higher prevalence of LGE (27% vs. 9%) when compared to those post-COVID-19. DATA CONCLUSION: Abnormal cardiac MRI findings may show a low prevalence in patients who recovered from COVID-19 and were previously hospitalized. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".