Midpregnancy Placental Growth Factor Screening and Early Preterm Birth
Bibliographic record
Abstract
Importance: Early preterm birth (ie, at less than 34 weeks' gestation) confers a high risk for adverse health outcomes, yet no universal screening strategy exists, preventing targeted delivery of effective interventions. Objective: To evaluate the ability of midpregnancy placental growth factor (PlGF) screening to identify pregnancies at highest risk for early preterm birth. Design, Setting, and Participants: This prospective cohort study was conducted at an urban, tertiary care center from 2020 to 2023. Participants were unselected, pregnant people with singleton pregnancies, receiving universal-access prenatal care from obstetricians, family physicians, or midwives, who underwent a PlGF test at the time of routine gestational diabetes screening, typically at 24 to 28 weeks' gestation. Data were analyzed from January to May 2024. Exposure: PlGF level less than 100 pg/mL at the time of gestational diabetes screen. Main Outcomes and Measures: The primary outcome was all early preterm birth, defined as less than 34 weeks' gestation. Secondary outcomes included iatrogenic preterm birth, spontaneous preterm birth, preeclampsia, stillbirth, and small-for-gestational-age birth weight. Results: Among 9037 unique pregnant individuals, 156 (1.7%) experienced early preterm birth (52 spontaneous births; 104 iatrogenic births). The area under the curve (AUC) for PlGF and early preterm birth was 0.80 (95% CI, 0.75-0.85). Low PlGF level was associated with early preterm birth (positive likelihood ratio [LR], 79.400 [95% CI, 53.434-115.137]; negative LR, 0.606 [95% CI, 0.494-0.742]; specificity, 99.5% [95% CI, 99.3%-99.6%]; negative predictive value, 98.9% [95% CI, 98.8%-99.1%]). Time to birth from PlGF test was significantly reduced among patients with a PlGF level less than 100 pg/mL, among whom more than 50% delivered within 50 days of testing. Individuals with a low PlGF level made up more than 30% of subsequent stillbirths (aRR, 36.78 [95% CI, 18.63-72.60]) and more than half of patients requiring iatrogenic early preterm birth (aRR, 92.11 [95% CI, 64.83-130.87]). The AUC for iatrogenic early preterm birth was 0.90 (95% CI, 0.85-0.94). Conclusions and Relevance: These findings suggest that low PlGF level (<100 pg/mL), identified at the time of routine gestational diabetes screening, may be a powerful clinical tool to identify pregnant people at risk of early preterm birth, especially in iatrogenic births. Strategic redirection of tertiary health care resources to this high-risk group could improve maternal and perinatal outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".