Prediction Model Based on Easily Available Markers for Aberrant Cardiac Remodeling in Women After Pregnancy
Bibliographic record
Abstract
BACKGROUND: Preeclampsia is strongly associated with left ventricular concentric remodeling (LVCR) and left ventricular hypertrophy (LVH) up to 10 years after delivery. This predisposes to heart failure later in life. Adequate detection and prediction of LVCR or LVH is expected to decrease the risk of developing clinical heart failure within this high-risk female population. Therefore, we developed and internally validated a prediction model for aberrant cardiac remodeling in formerly pregnant women. METHODS: This large cohort study included women with a history of preeclampsia or normotensive pregnancy within a postpartum interval of 6 months to 30 years. Cardiovascular assessment was performed, including echocardiography, 30-minute blood pressure measurements, and circulating biomarkers. Aberrant cardiac remodeling based on echocardiographic findings was defined as either LVCR or LVH. Discriminative performance was evaluated by the area under the receiver operating characteristic curve. RESULTS: A total of 1397 women were included, of which 139 (10%) with LVCR or LVH (mean±SD age, 43±9 years) and 1258 (90%) without LVCR or LVH (40±8 years). The final prediction model was established based on the predictors age, waist circumference, systolic blood pressure, glycated hemoglobin, antihypertensive medication use, and early onset preeclampsia (yes/no). After internal validation, the prediction model showed accurate discriminative ability with an area under the receiver operating characteristic curve of 0.702 (95% CI, 0.657-0.756). CONCLUSIONS: Based on the conventional predictors, we developed a prediction model for women who are on average 8 to 12 years postpartum. Internal validation showed accurate discriminative ability. Upon external validation, this model may aid clinicians to initiate further diagnostic testing or clinical follow-up. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT02347540.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".