Total Knee Arthroplasty in the Valgus Knee: Can New Operative Technologies Affect Surgical Technique and Outcomes?
Notice bibliographique
Résumé
INTRODUCTION: Valgus knee deformities can sometimes be challenging to address during total knee arthroplasties (TKAs). While appropriate surgical technique is often debated, the role of new operative technologies in addressing these complex cases has not been clearly established. The purpose of this study was to analyze the usefulness of computed tomography scan (CT)-based three-dimensional (3D) modeling operative technology in assisting with TKA planning, execution of bone cuts, and alignment. Specifically, we evaluated valgus TKAs performed using this CT-based technology for: (1) intraoperative implant plan, number of releases, and surgeon prediction of component size; (2) survivorship and clinical outcomes at a minimum follow up of one year; and (3) radiographic outcomes. MATERIALS AND METHODS: A total of 152 patients who had valgus deformities receiving a CT-based TKA performed by a single surgeon were analyzed. Cases were performed using an enhanced preoperative planning and real-time intraoperative feedback and cutting tool. The surgeon predicted and recorded implant sizes preoperatively and all patients received implants with initial and final implant alignment, flexion/extension gaps, and full or partial soft tissue releases recorded. A modified Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and the Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS, JR.) scores were collected preoperatively and at approximately six months and one year postoperatively. Preoperative coronal alignment ranged from 1 to 13° valgus. Follow-up radiographs were also evaluated for alignments, loosenings, and/or progressive radiolucencies. RESULTS: A total of 96% of cases were corrected to within 3° of mechanical neutral. For outlier cases, initial deformities ranged from valgus 5 to 13°, with final alignment ranging from 4 to 8° valgus (mean 4° correction). Patients had mean femoral internal rotation of 2° and mean femoral flexion of 4°. The surgeon was within one size on the femur and tibia 94 and 100% of the time, respectively. Only one patient required a lateral soft tissue release and one patient had osteophytes removed, which required a medial soft tissue release. Five patients required manipulations under anesthesia. Aside from these, there were no postoperative medical and/or surgical complications and there was 100% survivorship at final follow up. WOMAC and KOOS, JR. scores improved significantly from a mean of 21 ± 9 and 48 ± 10 points preoperatively to 4 ± 6 (p<0.05) and 82 ± 15 (p<0.05) at final follow up, respectively. None of the cases exhibited progressive radiolucencies by final follow up. DISCUSSION: A limitation of this study was not evaluating dynamic kinematics in these patients to determine if rotation had any effects on kinematics. Future studies will evaluate this concern. Nevertheless, the technology successfully assisted with planning, executing bone cuts, and achieving alignment in TKAs complicated by the deformity. This may allow surgeons to predictably avoid soft tissue releases and accurately know component sizes preoperatively, while consistently achieving desired postoperative alignment. CONCLUSIONS: This study demonstrated the utility of CT-based 3D modeling techniques for challenging valgus deformity cases. Use of 3D modeling allowed the TKA components to be positioned according to the patient's anatomy in the coronal, transverse, and sagittal planes. When making these intraoperative implant adjustments, the surgeon may choose to place components outside the preoperative planning guidelines based on the clinical needs of the patient.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».