Systematic literature review and meta-analysis on the safety of fibrinogen concentrate versus cryoprecipitate in bleeding surgical patients
Notice bibliographique
Résumé
Abstract Background: Fibrinogen concentrate (FC) and cryoprecipitate are plasma-derived products used to manage bleeding in patients with hypofibrinogenemia. While both effectively restore fibrinogen levels, randomized controlled trials (RCTs) have shown no difference in efficacy or safety between products. However, it is hypothesized that cryoprecipitate may be associated with a higher risk of thromboembolic events (TEE) due to the presence of coagulation factors (e.g. factor VIII, von Willebrand factor) and of platelet-derived microparticles generated in its manufacturing process (Cushing MM, et al. Transfusion. 2020 Jun;60 Suppl 3:S17-S23, Hensley NB and Mazzeffi MA. Anesth Analg. 2021;133(1):19-28). The aim of this study was to conduct a systematic literature review (SLR) and meta-analysis (MA) to assess the comparative safety of FC vs. cryoprecipitate in patients with surgical bleeding, focusing on the risk of TEEs. Methods: An SLR was conducted to identify RCTs published from 2009 to February 2025 that co mpared FC with cryoprecipitate for safety outcomes in patients experiencing bleeding during elective surgery. Outcomes assessed included the incidence of TEE, deep venous thrombosis (DVT), pulmonary embolism (PE), any adverse events (AE) and serious AEs (SAE). Identified studies were evaluated for MA feasibility. Despite substantial heterogeneity in study design and population across the studies, all eligible studies were included in the base-case MA due to the limited number of available RCTs. The base-case analysis employed a frequentist approach, with heterogeneity assessed using the I2 statistic. To explore the potential sources of heterogeneity, subgroup analyses by age (adults vs. pediatrics) and type of surgery (cardiac vs. abdominal) were conducted where data allowed. Sensitivity analyses tested the robustness of the base-case findings by excluding studies with high risk of bias, removing an outlier study with disproportionate weight, and analysis based on per-protocol population. Exploratory analyses were performed using an empirical Bayesian (EB) approach for TEE and any AEs. All analyses were conducted in R version 4.4.1 (R Studio), using the “meta” package (version 7.0-0) for frequentist analyses and “metafor” (version 4.8-0) package for EB analyses. Frequentist analyses were performed using both fixed- and random-effects (FE and RE) models. For the EB analyses, only RE model was used. Results were reported as Odds Ratio (OR) and 95% confidence intervals (CI). Results: The SLR yielded 7 unique studies for inclusion in the MA. The base-case MA (RE model) showed a favorable trend for FC over cryoprecipitate in reducing the risk of all TEEs (n=5, OR = 0.55; 95% CI: 0.29-1.07; I² = 32%). In subgroup analyses, FC was associated with a significantly lower risk of TEEs in abdominal surgery (n=2; OR = 0.19; 95% CI: 0.05-0.70; I2 = 7%), and similar, though non-significant, trends were observed in cardiac surgery, adult, and pediatric subgroups and in sensitivity analyses. Exploratory Bayesian approach showed consistent results with the base-case analysis (n=5; OR = 0.49; 95% CI: 0.19-1.28; I² = 43%). Results for specific TEEs (DVT, PE) were consistent with the overall analysis. For DVT, the OR was 0.31 (n=3; 95% CI: 0.12-0.81; I² = 0%), indicating a significantly lower risk in patients treated with FC. For PE, a non-significant trend was observed (n=3; OR = 0.43; 95% CI: 0.06-2.99; I² = 49%). Within the abdominal surgery subgroup, a statistically significant reduction in PE risk was observed in patients treated with FC (n=2; OR = 0.23; 95% CI: 0.06-0.88; I2 = 0%). For any AEs and SAEs, FC showed a safety benefit vs. cryoprecipitate. A statistically significant reduction was observed for any AEs (n=4; OR = 0.70; 95% CI: 0.52-0.94; I² = 0%), while for SAEs, the effect also favored FC but was not statistically significant (n=3; OR = 0.50; 95% CI: 0.18-1.40; I² = 73%). Subgroup and sensitivity analyses showed consistent results for both any AEs and SAEs. The exploratory Bayesian analysis for any AEs matched the results of the base-case analysis. Conclusion: This analysis suggests that FC has a favorable safety profile regarding TEEs, as well as AEs and SAEs, compared to cryoprecipitate in patients with surgical bleeding. Due to the limited number and heterogeneity of the identified RCTs, these results should be interpreted with caution, underscoring the need for additional clinical trials.
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 | 0,013 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,018 | 0,035 |
| Bibliométrie | 0,007 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».