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Record W4414740562 · doi:10.1093/clinchem/hvaf086.345

A-361 Long-term analytical variation of placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) for preeclampsia risk assessment: a 5-year review

2025· article· en· W4414740562 on OpenAlexaff
Mary Kathryn Bohn, Meshach Asare-Werehene, Liyan Ma, Tanya Jorden, Bonny Lem Ragosnig, Paul S. F. Yip, Lei Fu

Bibliographic record

VenueClinical Chemistry · 2025
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreSunnybrook HospitalUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsPlacental growth factorPreeclampsiaRetrospective cohort studyExternal quality assessmentConcordanceRisk factor

Abstract

fetched live from OpenAlex

Abstract Background Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Dysregulation of pro-angiogenic [placental growth factor (PlGF)] and anti-angiogenic [soluble fms-like tyrosine kinase-1 (sFlt-1)] mediators represents contributing factors to multi-system disease pathogenesis. An increased ratio of sFlt-1 to PlGF is associated with preeclampsia risk. Within the laboratory, long-term analytical variation of both assays has not been formally assessed. The objective of this study was to evaluate analytical variations in PlGF and sFlt-1 assays and their impact on the clinical interpretation of test results. Methods Five years of retrospective patient results for sFlt-1, PlGF and sFlt-1:PlGF ratio were extracted since clinical implementation at a tertiary hospital with a high-risk obstetrical unit (N=1958, Roche cobas 8000 e602 and cobas Pro e802). Descriptive statistics were determined across unique reagent lots. sFlt-1:PlGF results were classified according to preeclampsia risk based on the landmark PROGNOSIS study as low (<39), moderate (39 to 85), or high (>85). The percentage of results in each risk category were compared across unique sFlt-1 and PlGF reagent lot combinations. In addition to retrospective patient data, aggregate results from two external quality assurance (EQA) programs for sFlt-1 and PlGF were reviewed. The first EQA program (Weqas) included four years of monthly EQA survey data on one instrumentation (Roche cobas, N=15-22 participant laboratories). The second EQA program (RIQAS) consisted of a one-time pilot survey and included four different assays (Roche cobas, Brahms KRYPTOR, DELFIA Xpress, SNIBE Maglumi, N=89 participant laboratories). Results In retrospective patient data, the percentage of sFlt-1:PlGF results classified as high risk varied between 12% to 30% across 11 unique PlGF and sFLt-1 lot combinations (N=59 to 587 per lot). PlGF results varied with reagent lot with medians ranging from 183 ng/L (IQR: 83-299 ng/L) to 231.5 ng/L (96-330 ng/L). sFlt-1 also demonstrated variation in lot-specific patient result medians ranging from 176 ng/L (IQR: 89-325 ng/L) to 212 ng/L (103-293 ng/L). Shifts in sFlt-1 and PlGF distribution across lots were not statistically significant and did not correlate to any change observed in sFlt-1:PlGf ratio classification. Based on review of four years of EQA data (Weqas), coefficient of variation (CV) across participating laboratories was higher for PlGF (median: 8.4%, IQR: 6.3-10.6%) relative to sFlt-1 (median: 4.7%, IQR: 4.0-5.4%). Pilot EQA survey (RIQAS) that included different instrumentation demonstrated assay-specific differences in observed CV and was dependent on the target concentration. Conclusion This study evaluates a comprehensive dataset of sFlt-1:PlGF results from patients assessed for preeclampsia risk. These data were linked with laboratory information, including reagent lot and EQA results, to assess long-term variations in analytical performance. Our findings suggest that observed variations in sFlt-1:PlGF risk classifications with reagent lot are likely due to patient-specific factors as opposed to changes in analytical performance. EQA data also support robust long-term performance; however, higher CVs were observed for PlGF relative to sFlt-1 and demonstrated dependence on assay platforms. These findings contribute to our understanding of analytical considerations for preeclampsia testing and may serve as a resource of laboratories considering implementation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.381
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
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