Evaluation of Interventions for Implementation of Thromboprophylaxis in Hospitalized Medical and Surgical Patients At Risk for Venous Thromboembolism: A Systematic Review and Metaanalysis
Notice bibliographique
Résumé
Abstract Abstract 676 Introduction: Venous thromboembolism (VTE) is a leading cause of morbidity and mortality in hospitalized patients. Numerous randomized clinical trials (RCTs) show that the use of thromboprophylaxis in hospitalized patients at risk for VTE is safe, effective and cost-effective. Despite this, prophylactic therapies for VTE are underutilized. System-wide interventions may be more effective to improve the use of VTE prophylaxis than relying on individual providers' prescribing behaviors. Objectives: In this review, we aimed to determine the effectiveness of various system-wide interventions designed to increase the use of thromboprophylaxis in hospitalized medical and surgical patients at risk for VTE. Methods: We searched MEDLINE, EMBASE, and SCOPUS databases to identify studies that assessed an intervention designed to increase use of prophylaxis and/or decrease incidence of VTE. Extracted data included study design, setting, intervention, and outcomes including proportions receiving prophylaxis (RP) and receiving appropriate prophylaxis (RAP). Risk of bias was assessed using Cochrane guidelines. We performed meta-analysis for RCTs and non-randomized studies (NRS) separately. We categorized the interventions into three groups: education (e.g. grand rounds, self-administered course), alerts (e.g. electronic, human), and multifaceted interventions (e.g. combination of education, audit and feedback and alert). We performed a random effects meta-analysis and assessed heterogeneity using the I2 statistic. Results were pooled if three or more studies were available for a particular intervention group. Results: Out of 1802 records included in our primary screen of titles and abstracts, 79 studies were assessed for eligibility. Fifty-six studies were included in our systematic review, including eight RCTs (N=17,601) and 48 NRS (N=62,770). Among the RCTS, 4 studies included medical patients, 2 included medical and surgical patients, 1 included post-acute care patients and 1 did not report the types of patients included. The NRS were primarily before-and-after design. Fourteen included surgical patients, 10 included medical patients, 10 included medical and surgical patients, 8 included patients from other departments and 6 did not report the types of patients included. Our primary outcomes included received prophylaxis (RP) and received appropriate prophylaxis (RAP). Among the RCTs, there was sufficient data to pool one outcome (RP) for one intervention type (alert). Among the NRS, there was sufficient data to pool two outcomes (RP, RAP) for each intervention type (education, alert, multifaceted). I2 results showed substantial statistical heterogeneity among studies. A sensitivity analysis showed that multifaceted interventions which included an alert were more effective at improving rates of RP and RAP than those without an alert. Conclusions: We reviewed a large number of studies which implemented a variety of system-wide strategies aimed to improve thromboprophylaxis rates in many settings and patient populations. We found statistically significant improvements in prescription of prophylaxis associated with alerts and multifaceted interventions, and improvements in prescription of appropriate prophylaxis with the use of education, alerts or multifaceted interventions. Multifaceted interventions with an alert component seem to be the most effective. We chose to pool effect estimates despite significant heterogeneity because the results were generally in the same direction but of different magnitudes. We are continuing to investigate sources of heterogeneity including patient population, setting, baseline prophylaxis rates, and intervention characteristics. The results of our review will help physicians, hospital administrators and policy makers make practical decisions about adoption of specific system-wide measures to improve prevention of VTE. Funded by Canadian Institutes for Health Research Disclosures: Kahn: sanofi aventis: Honoraria, Research Funding; Leo Pharma: Honoraria.
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 | 0,020 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,023 | 0,044 |
| Bibliométrie | 0,007 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| 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 ».