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Enregistrement W2588684606 · doi:10.1182/blood.v126.23.890.890

Low Molecular Weight Heparin for Prevention of Placenta-Mediated Pregnancy Complications: An Individual Patient Data Meta-Analysis

2015· article· en· W2588684606 sur OpenAlexaffabout
Marc Rodger, Johanna I. de Vries, Évelyne Rey, Jean‐Christophe Gris, Ida Martinelli, E Schleußner, Saskia Middeldorp, Shannon M. Bates, Paulien de Jong, Nicole Langlois, Ranjeeta Mallick, Tim Ramsay, David Petroff, Dick Bezemer, Marion van Hoorn, Carolien N. H. Abheiden, Risto Kaaja, Annalisa Perna, Alain Mayhew

Notice bibliographique

RevueBlood · 2015
Typearticle
Langueen
DomaineMedicine
ThématiqueBlood Coagulation and Thrombosis Mechanisms
Établissements canadiensMcMaster UniversityCentre Hospitalier Universitaire Sainte-JustineOttawa HospitalUniversity of Ottawa
Organismes subventionnairesnon disponible
Mots-clésMedicinePlacental abruptionPregnancyMeta-analysisObstetricsLow molecular weight heparinSmall for gestational ageGestational ageInternal medicineFetusHeparin

Résumé

récupéré en direct d'OpenAlex

Abstract Introduction Placenta-mediated pregnancy complications (PMPC) include pre-eclampsia, late pregnancy loss, placental abruption, and the small-for-gestational age (SGA) newborn. They are leading causes of maternal, fetal, and neonatal morbidity and mortality. Affected women are at an elevated risk of recurrence in subsequent pregnancies. We completed a pooled summary-based (i.e. study level) meta-analysis that strongly suggests that low-molecular-weight heparin (LMWH) reduces the risk of recurrent PMPCs. However, our study-level meta-analysis was limited by high clinical and statistical heterogeneity likely due to the inclusion of women with heterogeneous prior PMPCs and trial designs (e.g. single vs multi-center trials). To address these limitations, the trialists agreed to conduct an individual patient data meta-analysis to identify sources of heterogeneity including exploring which patients benefit from LMWH and which outcomes are prevented. Methods We conducted a systematic review to identify randomised controlled trials that were eligible to contribute individual patient data to a meta-analysis to evaluate the effectiveness of LMWH for reducing the risk of PMPC in women with prior PMPCs. The primary outcome was a composite of early-onset or severe pre-eclampsia, birth of an SGA newborn < 5th percentile, late pregnancy loss (> 20 weeks), or placental abruption leading to delivery. Individual patient data from eligible women were re-coded in a prescribed format and combined in a common dataset for analysis. All studies were assessed for risk of bias. Results Data from 1049 women in nine trials were analysed; Participants were mostly Caucasian (88%) with a mean age of 31.518 had thrombophilia. 525 women were randomised to LMWH and 524 to no LMWH. In our primary outcome analysis, LMWH did not significantly reduce the risk of recurrent PMPCs (LMWH 60/459 (13.1%) vs. no LMWH 92/449 (20.5%) p=0.1). Significant heterogeneity was noted between single center and multi-center trials. In multi-center trials, LMWH reduced HELLP (p=0.03) but none of the other secondary outcomes, whereas in single center trials LMWH reduced all of the secondary outcomes. In sub-group analysis, in multi-center trials LMWH reduced the primary outcome in women with prior abruption (p<0.01) but none of the other sub-groups, whereas in single center trials LMWH was beneficial in all the sub-groups (prior pre-eclampsia, prior severe pre-eclampsia, prior early onset pre-eclampsia, prior SGA <10th, prior SGA < 5th and prior abruption). Conclusions In this individual patient data meta-analysis, LMWH does not appear to reduce the risk of recurrent PMPC in women with prior PMPC. Promising results suggest that women with prior abruption may benefit from LMWH but this should be replicated in future multi-center trials. PROSPERO registration:CRD42013006249 Table. Primary Analysis All Studies Multi-Center Studies Single Center Studies Composite outcome Risk difference (95% CI) N=908 -0.07 (-0.16, 0.01)p = 0.10 N=524 -0.01 (-0.11, 0.09) p = 0.89 N=384 -0.17 (-0.21, -0.13) p < .0001 Secondary Outcome Analyses Severe or Early Preeclampsia Risk difference (95% CI) N=946 -0.04 (-0.10, 0.02) p = 0.20 N=562 0.01 (-0.06, 0.07) p = 0.81 N=384 -0.11 (-0.16, -0.07) p <.0001 HELLP Risk difference (95% CI) N=813 -0.02 (-0.04, -0.004) p = 0.01 N=429 -0.01 (-0.02, -0.001) p = 0.03 N=384 -0.04 (-0.07, -0.01) p = 0.02 SGA <10 Risk difference (95% CI) N=913 -0.08 (-0.14, -0.02) p = 0.01 N=529 -0.03 (-0.10, 0.03) p = 0.32 N=384 -0.14 (-0.18, -0.10) p <0.0001 Abruption leading to delivery Risk difference (95% CI) N=945 -0.01 (-0.02, 0.003) p = 0.14 N=561 -0.01 (-0.03, 0.01) p = 0.53 N=384 -0.016 (-0.027, -0.005) p = 0.005 Subgroup Analyses Prior preeclampsia Risk difference (95% CI) N=583 -0.12 (-0.19, -0.04) p = 0.002 N=288 -0.06 (-0.19, 0.06) p = 0.34 N=295 -0.17 (-0.24, -0.11) p <.0001 Prior severe or early onset Preeclampsia Risk difference (95% CI) N=487 -0.10 (-0.19, -0.02) p = 0.02 N=236 -0.04 (-0.19, 0.12) p = 0.65 N=251 -0.17 (-0.23, -0.11) p <.0001 Any prior late loss (2 >12 weeks or 1 >16 weeks) Risk difference (95% CI) N=245 0.001 (-0.11, 0.12) p = 0.98 N=0 Prior SGA < 10 Risk difference (95% CI) N=305 -0.12 (-0.25, 0.01) p = 0.08 N=203 -0.03 (-0.17, 0.10) p = 0.64 N=102 -0.29 (-0.38, -0.20) p <.0001 Prior abruption Risk difference (95% CI) N=281 -0.16 (-0.22, -0.11) p <.0001 N=95 -0.13 (-0.22, -0.04) p = 0.01 N=186 -0.18 (-0.22, -0.14) p <.0001 Disclosures Rodger: Biomerieux: Honoraria, Research Funding. Off Label Use: Low Molecular Weight Heparin to prevent pregnancy complications. de Vries:Pfizer: Research Funding. Rey:Leo Pharma: Other: Travel Grant. Gris:Sanofi: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Stago: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Leo Pharma: Consultancy, Speakers Bureau; LFB: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Baxter: Research Funding; BI: Speakers Bureau; Bayer: Speakers Bureau; BMS: Speakers Bureau. Schleussner:Bayer: Speakers Bureau; Pfizer: Research Funding, Speakers Bureau; Merck: Research Funding, Speakers Bureau. Middeldorp:GSK/Aspen: Research Funding; Bayer: Consultancy, Speakers Bureau; BI: Consultancy, Speakers Bureau; BMS: Consultancy, Research Funding, Speakers Bureau; Pfizer: Consultancy, Research Funding, Speakers Bureau; Daiichi-Sankyo: Consultancy, Speakers Bureau. Bates:Eli Lilly Canada: Other: I hold the Eli Lilly Canada/May Cohen Chair in Women's Health. Eli Lilly Canada provides unrestricted funding for partial salary support through this Chair. Eli Lilly Canada does not manufacture/distribute drugs relevant to the topic to be discussed..

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,018
score de la tête « metaresearch » (Gemma)0,033
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: Méta-analyse
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,018
Score d'incertitude au seuil0,097

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0180,033
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0170,056
Bibliométrie0,0040,004
Études des sciences et des technologies0,0000,001
Communication savante0,0040,002
Science ouverte0,0020,001
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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.

Tête enseignante Opus0,203
Tête enseignante GPT0,357
Écart entre enseignants0,154 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeMéta-analyse
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2015
Routes d'admission2
Résumé présentoui

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