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Enregistrement W3106406630 · doi:10.7759/cureus.11390

Intraoperative Periprosthetic Fractures in Total Hip Arthroplasty in Patients With Sickle Cell Disease at King Fahad Hospital Hofuf: A Cross-Sectional Study

2020· article· en· W3106406630 sur OpenAlexaboutno aff
Mohammad Alsaleem, Hassan A Alalwan, Abdullah M Alkhars, Abdullah H Al Huwaiyshil, Wejdan M Alamri

Notice bibliographique

RevueCureus · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueBone and Joint Diseases
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicinePeriprostheticAvascular necrosisSurgeryArthroplastyFemoral head

Résumé

récupéré en direct d'OpenAlex

Background Patients with avascular necrosis related to sickle cell disease (SCD) can be severely disabled by the severe degenerative changes of their hip. Total hip arthroplasty (THA) remains the only surgical option for some of these patients. Total hip arthroplasty can be a challenging procedure, and SCD patients demonstrate high percentages of medical, intraoperative, and postoperative complications and implant failure. Furthermore, the need for THA following avascular necrosis in the Eastern Province of Saudi Arabia is high, and the subsequent risk of periprosthetic fracture is prevalent. Therefore, it is crucial to conduct such a study. Aim of the study This cross-sectional retrospective study aimed to assess the prevalence and associated risk factors for periprosthetic fractures during total hip arthroplasty in sickle cell disease patients at King Fahad Hospital Hofuf, Saudi Arabia. Methods We collected the data of all SCD patients who had undergone THA during the study period, January 2015 to September 2020. Forty-nine SCD patients who had undergone THA during the study period were included. Patients who had undergone hip hemiarthroplasty, postoperative fractures, or had an indication of THA other than avascular necrosis were excluded. Surgeon factors, assistant factors, and surgical technique were also excluded. We then analyzed the data according to gender, age, BMI, American Society of Anesthesiologists classification, implant fixation type, avascular necrosis stage, proximal femoral morphology, Vancouver classification type, sickle cell type, preoperative hemoglobin (Hb) level, and the risk of periprosthetic fractures. Descriptive statistics were presented using frequency and percentages for categorical variables, and continuous variables were summarized using means ± standard deviations. Independent t-tests and chi-square tests were used to test for associations between categorical variables. At 0.05, the significance level was set. Results Of the patients, 32.7% were male and 67.3% were female. 32.7% of the patients had advanced degenerative changes due to avascular necrosis. Among the patients, 20.4% had an intraoperative periprosthetic femoral fracture, 90% had a Vancouver classification class A, and 10% had a Vancouver classification class B1. According to Dorr classification, 75.5% were classified as Dorr A and 24.5% as Dorr B. Of the patients, 48 had an uncemented implant, and only 1 had cemented. The mean perioperative Hb was 9.02 + 2.02, with a minimum of 6 and a maximum of 14. No significant associations were found between the incidence of intraoperative femoral fracture and the demographic variables and the operative profile characteristics. However, a significantly higher rate of fracture was observed in patients operated on the right side compared to patients operated on the left side. Conclusion The prevalence of periprosthetic intraoperative fracture among SCD patients at King Fahad Hospital Hofuf was 20.4% during the study period. Even with adequate perioperative management, orthopedic surgeons must be prepared to deal with high rates of intraoperative fracture. No significant association was found between the incidence of intraoperative femoral fracture in SCD patients and the demographic variables and the operative profiles. However, a significantly higher rate of fracture was observed in patients operated on the right side compared to patients operated on the left side.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,680

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,009
Tête enseignante GPT0,253
Écart entre enseignants0,244 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2020
Routes d'admission1
Résumé présentoui

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