Clinical Validation of the sFlt-1:PlGF Ratio as a Biomarker for Preeclampsia Diagnosis in a High-Risk Obstetrics Unit
Bibliographic record
Abstract
BACKGROUND: Preeclampsia is a multisystem disorder defined by new onset of hypertension with proteinuria after 20 weeks gestation. In part due to dysregulation of pro-angiogenic factors (e.g., placental growth factor [PlGF]) and anti-angiogenic factors (e.g., soluble fms-like tyrosine kinase 1 [sFlt-1]), preeclampsia results in decreased placental perfusion. An increased sFlt-1:PlGF ratio is associated with increased risk of preeclampsia. In this study, we evaluated sFlt-1:PlGF cutoffs and evaluated the clinical performance of sFlt-1:PlGF for predicting preeclampsia. METHODS: sFlt-1:PlGF results from 130 pregnant females with clinical suspicion of preeclampsia were used to evaluate the diagnostic accuracy of different sFlt-1:PlGF cutoffs and to compare the clinical performance of sFlt-1:PlGF to traditional preeclampsia markers (proteinuria and hypertension). Serum sFlt-1 and PlGF were measured using Elecsys immunoassays (Roche Diagnostics) and preeclampsia diagnosis was verified by expert chart review. RESULTS: A sFlt-1:PlGF cutoff of >38 yielded the greatest diagnostic accuracy of 90.8% (95% CI, 85.8%-95.7%). Using a cutoff of >38, sFlt-1:PlGF exhibited a greater diagnostic accuracy than traditionally used parameters such as new or worsening proteinuria or hypertension (71.9% and 68.6%, respectively). sFlt-1:PlGF >38 exhibited a negative predictive value (NPV) of 96.4% for rule-out of preeclampsia within 7 days, and a positive predictive value (PPV) of 84.8% for predicting preeclampsia within 28 days. CONCLUSIONS: Our study shows the superior clinical performance of sFlt-1:PlGF over hypertension and proteinuria alone to predict preeclampsia at a high-risk obstetrical unit.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".