Enhancing trauma laparotomy patient outcomes: Comprehensive approaches to care improvement
Notice bibliographique
Résumé
This thesis aims to enhance patient care for trauma laparotomy patients by focusing specifically on hospital length of stay (HLOS), a critical metric in resource-constrained healthcare systems. Although trauma laparotomy patients represent a relatively homogenous trauma cohort, they exhibit diverse needs and varied outcomes, necessitating targeted interventions. A series of studies was conducted to explore strategies, particularly the implementation of enhanced recovery protocols (ERPs), to optimize HLOS and improve patient care. Chapter 2 analyzed data from 27,434 trauma laparotomy patients in the National Trauma Data Bank, a large trauma registry in the United States, to provide a clearer understanding of this patient population. The study found an overall median HLOS of 7.0 days, with 77% of patients having an HLOS of less than 11 days, indicating the potential applicability of ERPs for this population. Factors associated with HLOS, when stratified by length of stay, included injury type, complications, comorbidities, and insurance status, underscoring the value of this approach for targeted interventions.Chapter 3 examined unnecessary hospital stays, a distinct type of prolonged HLOS that is particularly impactful in Canada’s universal healthcare system. A retrospective analysis at Montreal General Hospital revealed that approximately 30% of trauma laparotomy patients experienced unnecessary stays, resulting in 513 additional hospital days during the study period. Delays were primarily due to limited availability in rehabilitation (42.2%) and psychiatric department (39.1%). These insights suggest potential interventions, such as improved access to post-acute care and enhanced inter-departmental coordination, to optimize resource efficiency. Chapter 4 details the development and implementation of the Trauma Laparotomy Care Pathway (TLCP), an ERP specifically tailored for trauma laparotomy patients, followed by a prospective pilot study assessing adherence to pathway components and its impact on outcomes. A comprehensive literature review, covering both trauma laparotomy and emergency abdominal surgery due to the limited number of ERP studies specific to trauma laparotomy, was conducted as a foundation for TLCP development. The review identified 39 studies, highlighting an increase in ERP research over the past decade. However, only three studies to date have focused exclusively on trauma laparotomy, and none were conducted in North America, indicating an opportunity for ERP implementation in this context. A consensus-based TLCP was developed and implemented in our clinical setting. In the first six months post-implementation, adherence to pathway components ranged from 54.5% to 67.7%, and TLCP reduced HLOS by two days compared to the historical cohort (4.0 days [3.5, 6.5] vs 6.0 days [4.0, 10.0], p=0.0021) without an increase in complications or readmissions. In conclusion, stratifying trauma laparotomy patients by HLOS effectively identifies subgroups with distinct characteristics and healthcare needs, highlighting the importance of targeted interventions. The newly developed TLCP can be applied to select trauma laparotomy patients, offering the potential for improved outcomes. Addressing factors contributing to unnecessary stays, along with pathway use, may further enhance patient outcomes. These efforts represent initial steps toward improving care for trauma laparotomy patients on a larger scale
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 | 0,012 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,010 | 0,005 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».