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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».