Antithrombin deficiency, venous thromboembolism risk, and peripartum antithrombin supplementation: A real-world analysis of delivery encounters from 2015–2024
Notice bibliographique
Résumé
Abstract Background Pregnant individuals with antithrombin (AT) deficiency have an increased risk for thromboembolic complications during pregnancy, delivery, and postpartum. Overall, the risk of venous thromboembolism (VTE) is approximately five times higher in pregnant women than in non-pregnant women. VTE, including pulmonary embolism, is a leading cause of pregnancy-related mortality in the US and is associated with significant long-term morbidity. However, optimal management strategies, including the role of antithrombin concentrate (ATc) in this high-risk population, remain unclear, with current recommendations based primarily on expert consensus and case reports. We conducted a retrospective cohort analysis to describe real-world ATc use and assess thromboembolic outcomes in pregnant individuals with AT deficiency, including those with persistently low AT levels at delivery. Methods We utilized electronic health record data from the TriNetX Dataworks US network to identify a study population comprising pregnant individuals meeting the following inclusion criteria: (1) presence of an inpatient delivery encounter between Jan 1, 2015—Dec 31, 2024; (2) history of an AT level ≤80% within the 9 months leading up to the delivery encounter; and (3) history of diagnosed primary thrombophilia, as determined by International Classification of Diseases codes. We evaluated demographic and clinical characteristics, including maternal age and obstetric comorbidity burden, using the Obstetric Comorbidity Index (OBCMI), a validated scoring system that estimates the risk of maternal morbidity based on pre-existing and pregnancy-related conditions. We also examined the association between peripartum ATc administration and venous thromboembolism (VTE), with descriptive statistics used to compare clinical and outcome variables between ATc and non-ATc encounters. Results We identified a total of 154 delivery encounters from 114 patients impacted by AT deficiency, corresponding to approximately 1.5 deliveries per year, among which 13.6% (n=21) involved ATc administration. The median maternal age at delivery was 31 years [25th, 75th percentiles: 27, 35]. OBCMI scores were similar between groups, with an overall median of 26 [17, 40]. Deliveries involving ATc use had higher rates of VTE history (57.1% vs. 39.1%). To draw clinically relevant comparisons, we focused on a subgroup of delivery encounters with AT levels measured during the hospitalization. Among encounters with peripartum AT levels ≤80% (n=14), 71.4% (n=10) involved ATc use. In this subgroup, peripartum VTE occurred in 0% of ATc-treated encounters (n=0/10) compared to 25.0% (n=1/4) in those without ATc. Restricting encounters with peripartum AT levels ≤60% (n=9), an even higher proportion received ATc (77.8%, n=7). In this subgroup, peripartum VTE occurred in 0% (n=0/7) of ATc-treated encounters compared to 50.0% (n=1/2) in those without ATc. Of note, among encounters with AT levels ≤60% at delivery, LMWH was also used at some point during the hospitalization in 100% (n=7) of ATc-administered cases and 50.0% (n=1) of non-ATc cases. Conclusions In a sample of delivery encounters for obstetric patients with AT deficiency, ourfindings demonstrate that in concert with peripartum LMWH, ATc use may be associated with lower rates of VTE and related complications, particularly in those with persistently low peripartum AT levels. ATc-treated encounters more often included a history of VTE and lower baseline AT levels. While these findings are not definitive due to the small sample size and non-randomized nature of the comparison, these real-world data support the safety and efficacy of ATc in obstetric patients with AT deficiency, consistent with current expert guidelines for selected cases.
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 | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».