Development and Validation of a Risk Prediction Model for 5-year Risk of Hypertension in Women of Reproductive Age
Bibliographic record
Abstract
Introduction: The rate of pregnancy complications has been steadily increasing over the past decade. These complications include hypertensive disorders during pregnancy (HDP), gestational diabetes, and preterm birth. Previous studies have shown an association between pregnancy complications and the development of cardiovascular disease (CVD). Hypertension has been established as a precursor to CVD, implying that predicting the incidence of hypertension could help reduce the overall prevalence of CVD. The objective of this study is to provide clinicians with a validated tool to identify women in the post-partum period with increased risk of hypertension. Methods: A risk prediction model was developed to estimate the 5-year risk of incident hypertension among nulliparous women, accounting for obstetrical history and complications of pregnancy. Variables with low prevalence (<1%) were excluded and lasso-regularized Cox regression was used to further exclude variables with negligible effects on the predictions. A baseline analysis was conducted using an extended Cox model. A proportional hazards assumption violation for HDP was dealt with by allowing the associated risk to vary over time. Harrell's concordance (C)-index was used to measure discrimination ability and internal validation was performed using bootstrap resampling (n=500 replicates). Results: Eleven variables were included in the final model, with an HDP diagnosis having the largest estimated effect on the risk of hypertension. The predicted 5-year probability of hypertension diagnosis for a subject with median attributes was 7% (95% CI: 6.5%, 7.5%). Discussion: HDP alongside other risk factors can provide insight into identifying women in the post-partum period who would benefit from the early initiation of cardiovascular prevention strategies and treatment.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".