Abstract 14532: Myocardial Blood Flow in Patients Recovered From COVID-19 Infection Using Stress Cardiac Magnetic Resonance
Bibliographic record
Abstract
Introduction: Severe COVID-19 infection is known to alter myocardial perfusion through its effects on the endothelium and microvasculature. However, a significant proportion of the world population suffered from only mild COVID-19 symptoms, and it is unknown if their myocardial perfusion is altered following their recovery. Hypothesis: In this study, we aimed to determine if there are detectable abnormalities to myocardial perfusion using cardiac magnetic resonance (CMR) in individuals who have recovered from mild COVID-19 infection. Methods: We conducted a prospective, comparative study of individuals who have recovered from COVID-19 infection (n=33) and risk-factor matched controls (n=27) using regadenoson stress CMR by a 1.5T MR scanner (GE Signa Artist) (figure). Quantitative stress perfusion images were acquired using the dual sequence technique. MBF was measured during rest (rMBF) and stress (sMBF) using Cvi42 software(figure). Myocardial perfusion reserve (MPR) was calculated as sMBF/rMBF. Unpaired t test or the Mann-Whitney U test was used to test differences between the two groups. Results: The median time interval between COVID-19 infection and CMR was 6 (4, 9) months. 31/33 (94%) patients in COVID-19 infection were not hospitalized. Symptoms including chest pain, shortness of breath, syncope, and palpitations were greater in COVID-19 group than in the matched controls (19/33 (58%) vs 2/27 (7%), p<0.001). No differences in rMBF (1.50 ± 0.47 vs 1.36 ± 0.45ml/g/min, p=0.21), sMBF (2.84 ± 0.56 vs 2.75 ± 0.64ml/g/min, p=0.56), or MPR (1.94 (1.48-2.75) vs 2.0 (1.59-3.05), p=0.34) were observed between the groups(figure). Conclusions: No significant abnormalities in myocardial perfusion during rest or stress conditions were seen in individuals who had recovered from mild COVID-19 infection suggesting that microvascular dysfunction is unlikely to be a common sequela in this patient population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".