Generalized Joint Laxity Is Associated With Increased Failure Rates of Primary Anterior Cruciate Ligament Reconstructions: A Systematic Review

Krebs, Nathan M; Barber-Westin, Sue D.; Noyes, Frank R.

doi:10.1016/j.arthro.2021.02.021

Cited by 20 publications

(20 citation statements)

References 39 publications

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“…As noted in the original paper, this may be related to data quality since several risk factors for failure of ACL reconstruction are not captured in the NKLR [ 19 ]. Examples of these factors include radiographic variables such as tibial slope and coronal alignment [ 2 – 4 , 10 , 15 , 20 , 31 ], physical examination and rehabilitation details [ 11 , 14 , 18 , 22 ], and surgical technique factors such as tunnel position [ 16 ] and graft size [ 1 , 7 , 17 ]. The addition of these variables into the national knee ligament registers may improve future machine learning prediction endeavours.…”

Section: Discussionmentioning

confidence: 99%

“…At the time of primary surgery, how does a surgeon estimate the risk of their patient needing a revision anterior cruciate ligament (ACL) reconstruction in the future? Numerous studies have defined failure rate epidemiology and identified risk factors such as age [ 13 , 18 , 24 , 27 , 32 , 33 ], graft choice [ 13 , 18 , 21 ] and size [ 1 ], activity level [ 13 , 33 ], body composition [ 27 ], ligamentous laxity [ 14 , 18 ], and tibial slope [ 10 , 31 ]. Despite this mass of knowledge, the ability to synthesize it and accurately quantify revision risk at a patient-specific level remains elusive and is often influenced by surgeon experience.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning algorithm to predict anterior cruciate ligament revision demonstrates external validity

Martin

Wastvedt

Pareek

et al. 2022

Knee Surg Sports Traumatol Arthrosc

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Purpose External validation of machine learning predictive models is achieved through evaluation of model performance on different groups of patients than were used for algorithm development. This important step is uncommonly performed, inhibiting clinical translation of newly developed models. Machine learning analysis of the Norwegian Knee Ligament Register (NKLR) recently led to the development of a tool capable of estimating the risk of anterior cruciate ligament (ACL) revision (https://swastvedt.shinyapps.io/calculator_rev/). The purpose of this study was to determine the external validity of the NKLR model by assessing algorithm performance when applied to patients from the Danish Knee Ligament Registry (DKLR). Methods The primary outcome measure of the NKLR model was probability of revision ACL reconstruction within 1, 2, and/or 5 years. For external validation, all DKLR patients with complete data for the five variables required for NKLR prediction were included. The five variables included graft choice, femur fixation device, KOOS QOL score at surgery, years from injury to surgery, and age at surgery. Predicted revision probabilities were calculated for all DKLR patients. The model performance was assessed using the same metrics as the NKLR study: concordance and calibration. Results In total, 10,922 DKLR patients were included for analysis. Average follow-up time or time-to-revision was 8.4 (± 4.3) years and overall revision rate was 6.9%. Surgical technique trends (i.e., graft choice and fixation devices) and injury characteristics (i.e., concomitant meniscus and cartilage pathology) were dissimilar between registries. The model produced similar concordance when applied to the DKLR population compared to the original NKLR test data (DKLR: 0.68; NKLR: 0.68–0.69). Calibration was poorer for the DKLR population at one and five years post primary surgery but similar to the NKLR at two years. Conclusion The NKLR machine learning algorithm demonstrated similar performance when applied to patients from the DKLR, suggesting that it is valid for application outside of the initial patient population. This represents the first machine learning model for predicting revision ACL reconstruction that has been externally validated. Clinicians can use this in-clinic calculator to estimate revision risk at a patient specific level when discussing outcome expectations pre-operatively. While encouraging, it should be noted that the performance of the model on patients undergoing ACL reconstruction outside of Scandinavia remains unknown. Level of evidence III.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning algorithm to predict anterior cruciate ligament revision demonstrates external validity

Martin

Wastvedt

Pareek

et al. 2022

Knee Surg Sports Traumatol Arthrosc

View full text Add to dashboard Cite

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“…GJL adversely affects knee stability, graft failure rates, and functional outcomes after ACL reconstruction. 24 , 25 , 28 However, there has been a paucity of publications that describe the relationship between the presence of GJL and the initial constraint level of the knee. In the current study, the presence of GJL was significantly more common in the high-constraint group; however, it was not an independent predictor for the high constraint level of the knee.…”

Section: Discussionmentioning

confidence: 99%

Factors Influencing the Initial Constraint Level of the Knee Joint and Its Effect on Clinical Outcomes After ACL Reconstruction With Hamstring Graft

Chung

Choi

Jung

et al. 2023

Orthopaedic Journal of Sports Medicine

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Background: A force-based tension protocol that uses a certain amount of tension at graft fixation could still give rise to variations in initial constraint levels of the knee joint in terms of side-to-side difference (SSD) in anterior translation. Purpose: To investigate the factors influencing the initial constraint level in anterior cruciate ligament (ACL)–reconstructed knees and compare outcomes according to the level of constraint in terms of anterior translation SSD. Study Design: Cohort study; Level of evidence, 3. Methods: Included were 113 patients who underwent ipsilateral ACL reconstruction using an autologous hamstring graft and had minimum 2-year follow-up outcomes. All grafts were tensioned and fixed at 80 N using a tensioner at the time of graft fixation. The patients were classified into the following 2 groups according to the initial anterior translation SSD, measured using the KT-2000 arthrometer: a physiologic constraint group with restored anterior laxity ≤2 mm (group P; n = 66) and a high-constraint group with restored anterior laxity >2 mm (group H; n = 47). Clinical outcomes were compared between the groups, and preoperative and intraoperative variables were evaluated to identify factors affecting the initial constraint level. Results: Between group P and group H, generalized joint laxity ( P = .005), posterior tibial slope ( P = .022), and anterior translation measured in the contralateral knee ( P < .001) were found to differ significantly. Measured anterior translation in the contralateral knee was the only significant predictor of high initial graft tension ( P = .001). No significant differences were found between the groups regarding clinical outcomes and subsequent surgery. Conclusion: Greater anterior translation measured in the contralateral knee was an independent predictor of a more constrained knee after ACL reconstruction. The short-term clinical outcomes after ACL reconstruction were comparable, regardless of the initial constraint level in terms of anterior translation SSD.

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“…42 There was additionally a greater risk of ACL graft failure and worse subjective outcomes in patients with increased Beighton score and generalized hypermobility. 20,21,53 To study the cohort of patients with matched related factors would more accurately determine the outcomes after R-ACLR in comparison with P-ACLR.…”

mentioning

confidence: 99%

Comparison of the Clinical Outcomes of Revision and Primary ACL Reconstruction: A Matched-Pair Analysis With 3-5 Years of Follow-up

Lin

Wang

et al. 2023

Am J Sports Med

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Background: There are limited studies designed by matching related factors to compare clinical outcomes and return to sport (RTS) between patients undergoing revision anterior cruciate ligament reconstruction (R-ACLR) and primary ACLR (P-ACLR). Purpose: (1) To compare the outcomes between R-ACLR and P-ACLR in a matched-pair analysis with 3- to 5-year follow-up and (2) to evaluate patient-reported factors for not returning to preinjury-level sport. Study Design: Cohort study; Level of evidence, 4. Methods: Patients who underwent R-ACLR between September 2016 and November 2018 were propensity matched by age, sex, body mass index, passive anterior tibial subluxation, and generalized hypermobility in a 1:1 ratio to patients who underwent P-ACLR during the same period. By combining in person follow-up at 2 years postoperatively and telemedicine interview at the final follow-up (January 2022), knee stability and clinical scores were compared, including International Knee Documentation Committee (IKDC), Lysholm, and Tegner. Status of RTS was requested, specifically whether the patient returned to preinjury level of sport. Patient-reported reasons for not returning were analyzed. Results: There were 63 matched pairs in the present study. Knee stability was similar in terms of KT-2000 arthrometer, Lachman test, and pivot-shift test results between the groups at 2 years of follow-up. At the final follow-up, no significant difference was found between groups for postoperative clinical scores (IKDC, Tegner, and Lysholm) ( P > .05). There was a significant difference in total RTS: 53 (84.1%) in the P-ACLR cohort and 41 (65.1%) in the R-ACLR cohort ( P = .014). No significant difference was shown in terms of RTS at the same level: 35 (55.6%) in P-ACLR and 31 (49.2%) in R-ACLR ( P = .476). Significantly more patients showed fear of reinjury: 26 of 32 (81.3%) in the R-ACLR group as compared with 15 of 28 (53.5%) in the P-ACLR group ( P < .021). Conclusion: R-ACLR resulted in similar clinical scores (IKDC, Tegner, and Lysholm) but significantly lower RTS versus P-ACLR at 3 to 5 years of follow-up. Fear of reinjury was the most common factor that caused sport changes in patients with R-ACLR.

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Generalized Joint Laxity Is Associated With Increased Failure Rates of Primary Anterior Cruciate Ligament Reconstructions: A Systematic Review

Cited by 20 publications

References 39 publications

Machine learning algorithm to predict anterior cruciate ligament revision demonstrates external validity

Machine learning algorithm to predict anterior cruciate ligament revision demonstrates external validity

Factors Influencing the Initial Constraint Level of the Knee Joint and Its Effect on Clinical Outcomes After ACL Reconstruction With Hamstring Graft

Comparison of the Clinical Outcomes of Revision and Primary ACL Reconstruction: A Matched-Pair Analysis With 3-5 Years of Follow-up

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