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
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,006 | 0,007 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».