The Utility of Maternal Placental Growth Factor (PlGF) for Prediction of Pre-Eclampsia in Pregnancies Complicated By Sickle Cell Disease
Bibliographic record
Abstract
BACKGROUND: Placental Growth Factor (PlGF), a pro-angiogenic factor, belongs to the vascular endothelial growth factor (VEGF) family and is typically expressed at very low or undetectable levels in healthy non-pregnant tissues, but its production is up-regulated in certain pro-inflammatory disease states, such as SCD, where PlGF arguably plays a counter-regulatory role to balance any pro-inflammatory signals. PlGF produced by erythroid cells is expressed at higher levels in individuals with more severe Sickle Cell Disease (SCD) phenotypes and is further increased in the context of hemolysis. In pregnancy, PlGF stimulates neo-angiogenesis, contributing to the transformation of the uteroplacental vasculature into a low-resistance system. During a low-risk pregnancy, PlGF increases as the gestation advances, peaking at 30 weeks, and declining thereafter. In contrast, in pregnancies affected by pre-eclampsia, early pregnancy angiogenesis is sub-optimal, with impaired transformation of the spiral arteries, with low PlGF levels observed in the early second trimester, well ahead of clinical evidence of disease. In non-SCD pregnancies, a PlGF cut-off <100 pg/mL has been shown to be a good predictor of pre-eclampsia. Yet, the capacity of low PlGF to predict pregnancy complications in SCD, where baseline PlGF levels are elevated even in the non-pregnant state, particularly in those with severe disease phenotype in whom adverse pregnancy outcomes such as pre-eclampsia are more frequent, has been unexplored. OBJECTIVE: To assess the distribution of PlGF levels in pregnant individuals with SCD and to investigate its role and most appropriate thresholds in predicting hypertensive complications in pregnancy. POPULATION: Pregnant individuals with SCD who received care at Mount Sinai Hospital in Toronto, Canada, between January 2017 and September 2021 and had at least one PlGF measurement between 20+0 and 35+6 weeks of gestation. DESIGN: Retrospective observational study. METHODS: Data were extracted from inpatient and outpatient records, including maternal demographics, laboratory investigations, placental ultrasound details, and delivery information. Maternal and neonatal outcomes were reviewed. Receiver Operating Characteristic (ROC) curves were constructed to explore optimal PlGF cut-offs, balancing sensitivity and specificity. The positive and negative predictive values of the selected PlGF cut-offs were examined. Given its predictive performance for pre-eclampsia outside of SCD, a cut-off value below 100 pmol/L was assessed for its applicability to individuals with SCD. RESULTS: PlGF data were available for 83 pregnancies, with pre-eclampsia identified in 11 (13%); of which pre-eclampsia was early-onset in 4 (36%) and late-onset in 7 (64%). For early-onset pre-eclampsia, a PlGF cut-off <100 pg/mL at 20-24 weeks' gestation demonstrated 100% sensitivity and specificity, false positive rate of 0%, positive predictive value (PPV) of 100% and negative predictive value of 100%, with the ROC AUC at 1.000 (p<0.00001), indicating perfect discriminatory ability for early-onset pre-eclampsia. In contrast, the same PlGF cut-off <100 pg/mL at 20-24 weeks' gestation showed a markedly lower sensitivity of 20% for predicting late-onset pre-eclampsia. Accurate detection of late-onset pre-eclampsia would necessitate a much higher PlGF threshold for reliable prediction. Specifically, a PlGF level of 832 pg/mL would be required in SCD patients at 20-24 weeks' gestation to achieve 100% sensitivity for the prediction of late-onset pre-eclampsia at a cost of a specificity of only 7% and an FPR of 93%. CONCLUSION: This study is the first to demonstrate the utility of PlGF in predicting early-onset pre-eclampsia in pregnancies complicated by SCD while also demonstrating its lack of discriminatory ability for predicting late-onset pre-eclampsia in this population. Our findings corroborate current understanding derived from non-SCD pregnancies that differing mechanisms are likely involved in the pathogenesis of early and late-onset pre-eclampsia necessitating distinct predictive strategies and underscore the importance of tailoring PlGF thresholds to specific gestational ages and types of hypertensive disorders to achieve optimal predictive performance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".