Predictive and Diagnostic Value of the Angiogenic Proteins in Patients With Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: Our objective was to investigate the predictive and diagnostic accuracy of the angiogenic proteins sFlt-1 (soluble fms-like tyrosine kinase-1) and PlGF (placental growth factor) for preterm preeclampsia and explore the relationship between renal function and these proteins. METHODS: We completed a blinded, prospective, longitudinal, observational study of patients with chronic kidney disease followed at a tertiary center (2018-2023). Serum samples were obtained at 3 time points along gestation (planned sampling): 12-16, 18-22, and 28-32 weeks. In addition, samples were obtained whenever preeclampsia was suspected (indicated sampling). sFlt-1 and PlGF levels remained concealed until the study ended. The primary outcome was preterm preeclampsia. The planned and indicated samples were used to estimate the predictive and diagnostic accuracy of the angiogenic proteins, respectively. RESULTS: Of the 97 participants, 21 (21.6%) experienced preterm preeclampsia. In asymptomatic patients with chronic kidney disease, the angiogenic proteins were predictive of preterm preeclampsia only when sampled in the third trimester, in which case the sFlt-1/PlGF ratio (false positive rate of 37% for a detection rate of 80%) was more predictive than either sFlt-1 or PlGF in isolation. In patients with suspected preeclampsia, the diagnostic accuracy of the sFlt-1/PlGF ratio (false positive rate of 26% for a detection rate of 80%) was higher than that of sFlt-1 and PlGF in isolation. Diminished renal function was associated with increased levels of PlGF. CONCLUSIONS: sFlt-1 and PlGF can effectively predict and improve the diagnostic accuracy for preterm preeclampsia among patients with chronic kidney disease. The optimal sFlt-1/PlGF ratio cutoff to rule out preeclampsia may need to be lower in patients with impaired renal function.
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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 | 0.008 |
| 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 | 0.000 | 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".