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Record W2982563021 · doi:10.2106/jbjs.18.00352

Computer-Assisted Surgery for Total Knee Replacement: Navigating Toward Improved Outcomes

2018· letter· en· W2982563021 on OpenAlexaboutno aff
Adam Hart, Rafael J. Sierra

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2018
Typeletter
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsnot available
Fundersnot available
KeywordsComputer-assisted surgeryKinematicsImplantKnee replacementRoboticsComputer scienceMedicineMedical physicsArtificial intelligenceSurgeryOrthopedic surgeryRobot

Abstract

fetched live from OpenAlex

Commentary While total hip replacement has been referred to as the “operation of the century,”1 with almost universally outstanding clinical outcomes, approximately 20% of patients after total knee replacement are somewhat dissatisfied2. The disparity in clinical outcomes between hip and knee replacement, seemingly intransigent over the last 2 decades3, has been the impetus for intense innovation on several fronts. Manufacturers have developed numerous implant designs with improved fidelity of preserving native knee anatomy and kinematics. Individualized, patient-specific implants and instruments that are based on preoperative axial imaging have come to market4. Surgical technique has evolved to minimize soft-tissue disruption, improve osseous as well as soft-tissue balancing, and consider both kinematic and mechanical alignment. Beyond implants and techniques, computer-assisted surgery has emerged as a major opportunity to improve the alignment of implanted components and, hopefully, clinical outcomes as a corollary. Computer-assisted surgery essentially uses specialized instruments and software to help guide the surgeon during component implantation. There are several types of navigation, including image-based navigation (in which preoperative axial images are coupled with intraoperative landmarks) or imageless navigation (in which multiple landmarks are fit to a virtual knee model using surface registration). Recent ameliorations of computer-assisted surgery include the introduction of portable, accelerometer-based navigation5 as well as robotics with haptic assistance to guide bone preparation6,7. No matter the type of computer-assisted surgery used, the prevailing evidence demonstrates superior implant alignment compared with conventionally instrumented knee replacements8. Computer-assisted surgery limits the number of outliers and therefore improves the reproducibility of accurate component placement. The elimination of intramedullary referencing also mitigates fat emboli and is particularly useful in the presence of extra-articular deformity or hardware that precludes the use of intramedullary instrumentation. Despite more reproducible component alignment with computer-assisted surgery, few studies have demonstrated improved clinical outcomes over conventional instrumentation. In a study using follow-up data from the Australian National Joint Replacement Registry, de Steiger et al. reported slightly fewer revisions in computer-assisted knee replacements at the 9-year follow-up in patients who were <65 years old9. Nonetheless, studies clearly demonstrating improved implant survivorship and patient-reported outcomes have not been forthcoming8. The added cost of computer-assisted surgery, increased setup complexity, increased operative time, and potential pin-site complications must also be considered. Muddling the subject even more is the controversy surrounding alignment altogether. Radiographic assessment of healthy, nonarthritic knees has demonstrated variations in native mechanical alignment, with a substantial portion of individuals who have several degrees of “constitutional varus.”10 Furthermore, in a series of 398 total knee replacements performed at the Mayo Clinic, supposedly malaligned knees (a mean mechanical axis [and standard deviation] of >0° ± 3°) did not have an increased risk of aseptic loosening or revision at 15 years11. We may therefore need to further elucidate and define the ideal target alignment of total knee replacement before potentially reaping the benefits of computer-assisted surgery. In the present article, the authors report the short-term clinical outcomes of a well-designed, multicenter, blinded randomized controlled trial comparing computer-assisted with conventionally instrumented total knee replacements. The study randomized 190 patients, and 167 of them completed the final evaluation and were included in the analysis. Comparison of the groups with respect to the 5 subscales of the Knee Injury and Osteoarthritis Outcome Score (KOOS) at 2 years demonstrated significantly larger improvements in the computer-assisted group for 2 subscales (symptoms, and sports and recreation). The authors also used the Outcome Measures in Rheumatology-Osteoarthritis Research Society International (OMERACT-OARSI) criteria to trichotomize Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores into high responders, moderate responders, and nonresponders. The computer-assisted group had significantly more high responders than the conventionally instrumented group. In comparison with previously reported randomized trials12, the present study demonstrated tangible improvements in several functional outcome measures in patients receiving computer-assisted knee replacements. These early clinical results are encouraging and possibly lend credence to the adoption of computer-assisted knee replacement. Nonetheless, it is important to note that significant improvements in the mean difference between the groups were demonstrated for only a subset of the KOOS and WOMAC scores, while the remaining measures and subsets were similar. Furthermore, the segregation of WOMAC scores into 3 binary responses may amplify the true clinical effect of the outcome. The authors engaged 2 pertinent questions in their subgroup analysis. First, they compared patients with postoperative mechanical malalignment (>0° ± 3°) and those with well-aligned knees (<3° of varus or valgus). Interestingly, there was no difference in functional outcomes between these 2 groups, which undermines the concept that neutral anatomic alignment (facilitated by computer-assisted surgery) leads to gains in functional outcomes. Second, the authors looked at patients who had constitutional varus before surgery. Within this subgroup of 82 patients, there were no significant differences in the postoperative KOOS scores between the 60 knees that were left in some varus alignment and the 22 knees that had corrected to neutral alignment. While both analyses may have been underpowered, they certainly contribute to the controversy of what alignment we should be targeting with knee replacement surgery. The present article describes encouraging early functional results with the use of computer-assisted total knee replacement. Nonetheless, it is difficult to attribute these gains solely to improved mechanical alignment. Further studies are needed to better understand the ideal alignment following knee replacement surgery. The use of computer-assisted surgery to achieve such a target might then more easily translate into beneficial clinical outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.024
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0030.001
Research integrity0.0120.009
Insufficient payload (model declined to judge)0.0240.007

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.

Opus teacher head0.043
GPT teacher head0.275
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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