Identification of risk factors by systematic review and development of risk-adjusted models for surgical site infection.
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Notice bibliographique
Résumé
BACKGROUND: Surgical site infections (SSIs) are complications of surgery that cause significant postoperative morbidity. SSI has been proposed as a potential indicator of the quality of care in the context of clinical governance and monitoring of the performance of NHS organisations against targets. OBJECTIVES: We aimed to address a number of objectives. Firstly, identify risk factors for SSI, criteria for stratifying surgical procedures and evidence about the importance of postdischarge surveillance (PDS). Secondly, test the importance of risk factors for SSI in surveillance databases and investigate interactions between risk factors. Thirdly, investigate and validate different definitions of SSI. Lastly, develop models for making risk-adjusted comparisons between hospitals. DATA SOURCES: A single hospital surveillance database was used to address objectives 2 and 3 and the UK Surgical Site Infection Surveillance Service database to address objective 4. STUDY DESIGN: There were four elements to the research: (1) systematic reviews of risk factors for SSI (two reviewers assessed titles and abstracts of studies identified by the search strategy and the quality of studies was assessed using the Newcastle Ottawa Scale); (2) assessment of agreement between four SSI definitions; (3) validation of definitions of SSI, quantifying their ability to predict clinical outcomes; and (4) development of operation-specific risk models for SSI, with hospitals fitted as random effects. RESULTS: Reviews of SSI risk factors other than established SSI risk indices identified other risk; some were operation specific, but others applied to multiple operations. The factor most commonly identified was duration of preoperative hospital stay. The review of PDS for SSI confirmed the need for PDS if SSIs are to be compared meaningfully over time within an institution. There was wide variation in SSI rate (SSI%) using different definitions. Over twice as many wounds were classified as infected by one definition only as were classified as infected by both. Different SSI definitions also classified different wounds as being infected. The two most established SSI definitions had broadly similar ability to predict the chosen clinical outcomes. This finding is paradoxical given the poor agreement between definitions. Elements of each definition not common to both may be important in predicting clinical outcomes or outcomes may depend on only a subset of elements which are common to both. Risk factors fitted in multivariable models and their effects, including age and gender, varied by surgical procedure. Operative duration was an important risk factor for all operations, except for hip replacement. Wound class was included least often because some wound classes were not applicable to all operations or were combined because of small numbers. The American Association of Anesthesiologists class was a consistent risk factor for most operations. CONCLUSIONS: The research literature does not allow surgery-specific or generic risk factors to be defined. SSI definitions varied between surveillance programmes and potentially between hospitals. Different definitions do not have good agreement, but the definitions have similar ability to predict outcomes influenced by SSI. Associations between components of the National Nosocomial Infections Surveillance risk index and odds of SSI varied for different surgical procedures. There was no evidence for effect modification by hospital. Estimates of SSI% should be disseminated within institutions to inform infection control. Estimates of SSI% across institutions or countries should be interpreted cautiously and should not be assumed to reflect quality of medical care. Future research should focus on developing an SSI definition that has satisfactory psychometric properties, that can be applied in everyday clinical settings, includes PDS and is formulated to detect SSIs that are important to patients or health services. FUNDING: The National Institute for Health Research Technology Assessment programme.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle