Total Knee Arthroplasty in the Valgus Knee: Can New Operative Technologies Affect Surgical Technique and Outcomes?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".