Factors Predictors of Vascular Events with Antiphospholipids Antibodies.
Notice bibliographique
Résumé
Abstract Background. The Montreal Antiphospholipid Study is an ongoing cohort study that began in 1997 that includes 415 persons followed prospectively for arterial (AE) and venous (VE) events. Objectives. To determine predictors of new thrombotic events in the presence of antiphospholipid antibodies. Methods. Blood samples were collected at baseline and annually for four years. Baseline assays included: IgG/IgM anticardiolipin antibodies (aCL), lupus anticoagulant (LA), and IgG/IgM anti-ß2-glycoprotein I antibodies (β2-GPI), activated protein C resistance (APCR), hyperhomocysteinemia (Hhcy), DDimer (DD), Factor VIII (FVIII), von Willebrand Factor (vWF), fibrinogen (FIB), high sensitivity C reactive Protein (hsCRP) levels were performed on the plasma or sera available at the visit closest to and prior to the date of new AE or VE. Demographic and clinical data were obtained at baseline and by telephone interview semi-annually. All events were confirmed by a panel of physicians. A nested case-control study was performed with 45 cases with new AE and VE during a mean follow-up period of 7.4 years [IQR = 0.5, 8.5] and 170 controls without new AE or VE matched for age, gender and visit date in a ratio of 4:1. Statistical analysis. Univariate regression models for case-control study were performed using new AE or VE as the outcome variable and aPL positivity (defined as either/or aCL IgG/IgM >40 PL units, LA or β2GPI positivity as predictor variable and DD, FVIII, vWF, FIB, hsCRP, APCR, Hhcy, family history of CVD (FMH), smoking, SLE or diabetes mellitus (DM) as covariables. Variables found to be predictors in the univariate analyses were then evaluated in multivariate regression models. Results. Mean age was 51.0; 77.7% female, 59.6% FMH, 25.1% SLE, 26.1% smokers, 5.1% DM. Thirty six (16.7%) individuals were aPL positive and 37 (17.2%) had abnormal APCR. A high degree of correlation between acquired APCR and aPL was observed. aPL positivity, smoking, DM and previous AE and VE were more frequent in cases than controls: 33% vs 12.3%, 42.2% vs 21.8%, 17.8% vs 1.8% and 53.3% vs 12.4%, respectively. Multivariate regression analyses revealed aPL positivity, previous AE, DM and smoking were predictors of new AE while predictors for new VE were APCR and vWF. Conclusions. Our findings in this nested-case control study demonstrate that aPL positivity predicts new AE. aPL positivity predicted new VE in the univariate analysis, but not in the multivariate model where APCR and vWF were the only factors retained. It is possible that APCR captures the effect of aPL as acquired APCR may be attributable to the presence of aPL. Besides vWF, APCR may be a better risk factor for VE than aPL, however more research is needed to determine their relationship. aPL is an important risk factor in AE, along with previous AE, smoking and DM. Predictor AE Univariate HR (95% CI) AE Multivariate HR (95% CI) VE Univariate HR (95% CI) VE Multivariate HR (95% CI) aPL 3.6(1.4,8.8) 3.8(1.0,14.7) 4.8(1.4,17.2) 2.2(0.3,15.0) APCR 1.8(0.8,3.9) - 5.7(1.7,18.6) 5.5(1.1,26.6) vWF 1.5(0.9,2.4) 1.0(0.4,2.6) 6.4(1.8,21.8) 5.0(1.2,19.8) DM 7.8(2.0,30.5) 9.9(1.5,64.6) - - Previous AE 7.2(2.7,19.1) 10.4(2.8,39.0) - - Previous VE - - 11.1(2.3,54.1) 1.8(0.2,16.4)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».