Assessing Total Knee Arthroplasty Implant Balance with a Passive Knee Drop Test
Notice bibliographique
Résumé
INTRODUCTION: Soft-tissue balancing is a critical component of total knee arthroplasty (TKA), though most current modalities to evaluate this intraoperatively are subjective and based upon empiric observation. A modified pendulum knee drop (PKD) technique has been developed to quantitatively evaluate knee joint soft-tissue stiffness. By measuring the amplitude and decay rate of oscillations when the leg is passively swung from extension to flexion, the modified PKD test offers a novel approach to evaluating knee stiffness in a reproducible manner. The purpose of this study was to explore the ability of the modified PKD test to quantify changes in stiffness induced by insert thickness in a cadaveric TKA model. MATERIALS AND METHODS: There were eleven (N=11) fresh frozen cadaver specimens that underwent a robotic-assisted total knee arthroplasty (RATKA) procedure. Nine of the 11 specimens underwent an RATKA with a cruciate-retaining (CR) femoral implant, and the remaining two specimens underwent an RATKA with a posterior-stabilized (PS) implant. The modified PKD test was performed on each RATKA specimen, where a planned insert was targeted to achieve an anatomically balanced knee and then increased by 2mm increments to simulate stiffer knee joints (in two cases, an additional 2mm insert was utilized for a total 4mm increment). An inertial measurement unit (IMU) sensor was placed on the tibia to record the range of motion (ROM). The thigh of the specimen was abducted over the side of the surgical table and positioned parallel to the floor to allow the shank to oscillate freely. The knee was then flexed to 45 degrees, calibrated in this reference position, and released, allowing the joint to oscillate until coming to rest. The procedure was repeated three times for each of the insert thicknesses. The IMU sensor was used to measure knee ROM, and the log-decrement ratio was calculated for each condition to estimate knee stiffness and was averaged over the three trials. The data was normally distributed, and paired sample t-tests were used to assess significance within specimens. Stiffness ratios were calculated as the log-decrement values of the thickest tibial inserts divided by the log-decrement value of the thinnest tibial inserts and were used to estimate the magnitude of stiffness increases. RESULTS: The modified PKD was able to detect the increased stiffness caused by increasing insert thickness in all specimens. This increase in stiffness was not impacted by implant design or implant size. The modified PKD test was able to reproducibly demonstrate an increase in stiffness when the same specimen was trialed with 2 to 4mm thicker polyethylene inserts. The modified PKD demonstrated reproducible results with respect to log decrement estimations, with an average standard deviation of 0.02 for all trials. DISCUSSION: This study investigated the ability of a modified PKD test to quantify the relative change in the stiffness of a TKA when changing the thickness of tibial inserts. Comparing the stiffness ratios between test constructs demonstrated that the modified PKD test was sensitive to variations in stiffness caused by thicker implants. A significant increase in knee stiffness was observed with as little as a 2mm incremental insert thickness, resulting in nearly twice the stiffness of the TKA, as documented by the increase in log decrement ratios. The lack of impact on implant size or design type suggests that these variables, when the TKA is appropriately sized and the procedure appropriately performed, do not impact stiffness. The ability of the modified PKD test to produce a sensitive and reproducible measure of relative construct stiffness is a promising new tool to help the surgeon in the operating room assess appropriate insert thickness for soft-tissue balancing.
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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) | 0,000 | 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 | 0,001 |
| 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 ».