Peripartum Cardiomyopathy is Associated With Abnormalities of Myocardial Deformation and Late Gadolinium Enhancement
Bibliographic record
Abstract
Purpose: Peripartum cardiomyopathy (PPCM) affects women in late pregnancy and postpartum. Cardiovascular magnetic resonance (CMR) can contribute to PPCM diagnosis and management. We explored CMR findings in PPCM, including myocardial strain and late gadolinium enhancement (LGE) patterns. Materials and Methods: This retrospective single-centre study included patients with PPCM who underwent CMR from 2010 to 2018. Exclusions were other cardiomyopathy causes. CMR parameters, including ventricular function, LGE, and myocardial strain, were compared between the PPCM group and healthy controls. Transthoracic echocardiographic data were reviewed to assess functional improvement in PPCM patients. Results: Thirty-two women with PPCM (mean age 42 ± 6 years) and 26 controls (mean age 43 ± 14 years) were included. PPCM patients had significantly lower left ventricular (LV) ejection fractions (median 37.5% vs 60.5%, P < .001), higher LV end-diastolic volumes (median 108 ml/m² vs 76 ml/m², P < .001), and reduced global LV strain compared to controls. Eighteen PPCM patients (58%) had non-ischaemic pattern LGE, with no LGE in controls besides hingepoint LGE (23%). LGE was most prevalent in the basal and mid anteroseptum. LGE patterns included linear mid-wall, subepicardial, and right ventricular side of the septum. Twenty-four patients (92%) showed improvement in LVEF at follow-up echocardiogram (mean LVEF 28% ± 1.9% at diagnosis and 45% ± 3% at follow-up, P < .001). Conclusion: We identified a non-ischaemic pattern LGE that is nonspecific in isolation but could suggest PPCM in the correct clinical context along with abnormal CMR strain values. Future studies should evaluate the clinical application of these findings to facilitate earlier diagnosis and enhance management.
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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 | 0.001 | 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".