Knee dislocation and associated injuries: an analysis of the American College of Surgeons National Trauma Data Bank
Notice bibliographique
Résumé
PURPOSE: Knee dislocations (KDs) are potentially devastating injuries, leading to loss of function or limb in often young patients. This retrospective database review aims to determine the relative incidence and risk factors for KDs presenting to North American Level I and II trauma centers. METHODS: The National Trauma Data Bank (NTDB) was retrospectively interrogated using ICD-9-CM codes to identify KDs between 2010 and 2014 to derive KD incidence. KDs were stratified by age, sex, Injury Severity Score (ISS), Glasgow Coma Scale (GCS), drug and alcohol use, injury mechanism, open vs. closed KD, vascular injury and fracture. Each co-variate was tested against different mechanisms of injury, using Chi-squared tests and risk adjusted analyses to derive risk factors for KD. The same calculations were done for secondary outcomes (vascular and neurological injuries, compartment syndrome, amputation, and mortality). RESULTS: A total of 6454 KDs met the inclusion criteria (18/10,000 admissions). KDs occurred most commonly amongst men, aged 20-39, with an ISS score 1-14 and following motor vehicle collision (MVC). A vascular investigation was performed in 29%, with injury documented in 15% of KDs and 10.8% receiving a vascular procedure. Associated fractures were observed in 41.4% of KDs. Open injuries in 13.6%. Neurological injury documented in 6.2%, compartment syndrome in 2.7%, amputation in 3.8% (> 50% had vascular injury) and 2.8% died. MVC was the most common mechanism of injury (p < 0.001), significantly more common in young, male patients, associated with higher ISS and lower GCS, especially when drugs or alcohol were involved (p < 0.0001). Being male, having a vascular injury or open KD were all risk factors for compartment syndrome, amputation and neurological injuries. CONCLUSIONS: KDs are rare injuries, but their relative incidence may be increasing. Young, male patients involved in MVCs are risk factors for KDs and their associated injuries, such as neurological injuries, amputations and compartment syndrome. Vascular injury occurs at a frequency of around 15%. The findings of the current study may guide future research and help to inform clinicians on the expected rates of associated injuries in patients identified to have KD in a trauma center population. It informs regarding risk factors for KD, which may improve diagnosis rates of spontaneously reduced knee dislocations by increasing index of suspicion in high-risk patients and identifies specific links with impaired driving. LEVEL OF EVIDENCE: IV.
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 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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, 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 ».