Functional anterior cruciate ligament braces may have a role in select patient groups although there is presently limited evidence supporting or refuting their routine use: A scoping review of clinical practice guidelines and an updated bracing classification.

PURPOSE: The purpose of this study was to perform a scoping review of clinical practice guidelines (CPGs) concerning the use of functional anterior cruciate ligament (ACL) braces and to clarify the nomenclature for bracing relevant to ACL injury treatment in order to support prescribing clinicians. METHODS: A PubMed search for CPGs for the use of braces following ACL injury or reconstruction was performed. CPGs on the treatment of ACL injuries with sufficient attention to postoperative braces were included in this scoping review. The references used for supporting the specific CPG recommendations were reviewed. Specific indications for brace use including brace type, period of use following surgery and activities requiring brace use were collected. RESULTS: Six CPGs were identified and included this this review. Three randomised trials provided the evidence for recommendations on functional brace use following ACL reconstruction in the six CPGs. Functional ACL braces were the primary focus of the three randomised trials, although extension braces (postoperative knee immobilisers) were also discussed. A novel dynamic ACL brace category has been described, although included CPGs did not provide guidance on this brace type. CONCLUSIONS: Guidance on the use of functional ACL braces following ACL reconstruction is provided in six CPGs supported by three randomised trials. However, the brace protocols and patient compliance in the randomised trials render these CPGs inadequate for providing guidance on the use of functional ACL braces in the general and high-risk patient populations when returning to sport after ACL reconstruction. Functional ACL braces are commonly utilised during the course of ACL injury treatment although there is presently limited evidence supporting or refuting the routine use of these braces. Future studies are, therefore, necessary in order to provide guidance on the use of functional and dynamic ACL braces in high-risk patient populations. LEVEL OF EVIDENCE: Level II.

Rates of Subjective Failure After Both Isolated and Combined Posterior Cruciate Ligament Reconstruction: A Study From the Norwegian Knee Ligament Registry 2004-2021.

BACKGROUND: Outcomes after posterior cruciate ligament (PCL) reconstruction (PCLR) have been reported to be inferior to those of anterior cruciate ligament reconstruction. Furthermore, combined ligament injuries have been reported to have inferior outcomes compared with isolated PCLR. PURPOSE/HYPOTHESIS: The purpose of this study was to report on PCLR outcomes and failure rates and compare these outcomes between isolated PCLR and multiligament knee surgery involving the PCL. The hypothesis was that combined PCL injury reconstruction would have higher rates of subjective failure and revision relative to isolated PCLR. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: Patients with primary PCLR with or without concomitant ligament injuries registered in the Norwegian Knee Ligament Registry between 2004 and 2021 were included. Knee injury and Osteoarthritis Outcome Score (KOOS) totals were collected preoperatively and at 2 years and 5 years postoperatively. The primary outcome measure was failure, defined as either a revision surgery or a KOOS Quality of Life (QoL) subscale score <44. RESULTS: The sample included 631 primary PCLR procedures, with 185 (29%) isolated PCLR procedures and 446 (71%) combined reconstructions, with a median follow-up time of 7.3 and 7.9 years, respectively. The majority of patients had poor preoperative knee function as defined by a KOOS QoL score <44 (90.1% isolated PCLR, 85.7% combined PCL injuries; P = .24). Subjective outcomes improved significantly at 2- and 5-year follow-up compared with preoperative assessments in both groups (P < .001); however, at 2 years, 49.5% and 46.5% had subjective failure (KOOS QoL <44) for isolated PCLR and combined PCLR, respectively (P = .61). At 5 years, the subjective failure rates of isolated and combined PCLR were 46.7% and 34.2%, respectively (P = .04). No significant difference was found in revision rates between the groups at 5 years (1.9% and 4.6%, respectively; P = .07). CONCLUSION: Patients who underwent PCLR had improved KOOS QoL scores relative to their preoperative state. However, the subjective failure rate was high for both isolated and multiligament PCLR. Within the first 2 years after surgery, patients who undergo isolated PCLR can be expected to have similar failure rates to patients who undergo combined ligament reconstructions.

Increased risk for early revision with quadriceps graft compared with patellar tendon graft in primary ACL reconstructions.

PURPOSE: Bone patella-tendon bone (BPTB) and hamstring tendon (HT) autografts are the most used grafts in primary anterior cruciate ligament (ACL) reconstructions (ACLR) in Norway. Quadriceps tendon (QT) autograft has gained more popularity during the past years. The purpose of this study is to compare revision rates and patient-reported outcomes of primary QT with BPTB and HT autograft ACL reconstructions in Norway at 2-year follow-up. It was hypothesized that there would be no difference in 2-year revision rates between all three autografts. METHODS: Data included primary ACLR without concomitant ligament surgeries, registered in the Norwegian Knee Ligament Register from 2004 through 2021. Revision rates at 2 years were calculated using Kaplan-Meier analysis. Hazard ratios (HR) for revision were estimated using multivariable Cox regression analysis with revision within 2 years as endpoint. Mean change in patient-reported outcome was recorded preoperatively and at 2 years through the Knee Injury and Osteoarthritis Outcome Score (KOOS) subcategories 'Sport' and 'Quality of Life' was measured for patients that were not revised and analysed with multiple linear regression. RESULTS: A total of 24,790 primary ACLRs were identified, 10,924 with BPTB, 13,263 with HT and 603 with a QT graft. Patients in the QT group were younger (23.5 years), more of them were women (58.2%) and over 50% had surgery <3 months after injury. The QT group had the highest prevalence of meniscal injuries (61.9%). Revision estimates at 2-years were 3.6%, 2.5% and 1.2% for QT, HT and BPTB, respectively (p < 0.001). In a Cox regression analysis with QT as reference, BPTB had a lower risk of revision (HR 0.4, 95% Cl 0.2-0.7, p < 0.001). No significant difference was observed in the revision risk between QT and HT (HR 1.1, 95% Cl 0.7-1.8, n.s.). The two most common reported reasons for revision were: traumatic graft rupture and nontraumatic graft failure. There were no differences between the groups in change of KOOS in subcategories 'Sport' and 'Quality of Life' at 2-years follow-up. CONCLUSION: The 2-year risk of revision after ACLR with QT was higher than BPTB and similar to HT. No difference was found between the groups in patient-reported outcomes. This study provides valuable insights for both surgeons and patients when making decisions about the choice of autografts in primary ACL reconstructions. LEVEL OF EVIDENCE: Level II.

The Delaware-Oslo ACL Cohort treatment algorithm yields superior outcomes to usual care 9-12 years after anterior cruciate ligament reconstruction.

PURPOSE: Patient-reported outcomes were compared between participants who followed the treatment algorithm of the Delaware-Oslo ACL Cohort, consisting of progressive preoperative and postoperative rehabilitation, patient education, clinical testing and shared decision-making about treatment choice, and those who followed usual care 9-12 years after anterior cruciate ligament reconstruction (ACLR). METHODS: Participants with primary ACLR were included from the Norwegian arm of the Delaware-Oslo ACL Cohort and the Norwegian Knee Ligament Registry (usual care). The Knee Injury and Osteoarthritis Outcome Score (KOOS) subscale scores and the International Knee Documentation Committee-Subjective Knee Form (IKDC-SKF) scores were compared. KOOS scores for the usual care group were converted to IKDC-SKF scores with recently published validated crosswalk. The percentages of participants with scores above predefined thresholds for patient acceptable symptom state (PASS) were also calculated. RESULTS: Eighty of 100 (80%) participants from the Delaware-Oslo ACL Cohort and 1588 of 3248 (49%) from the usual care group participated in the follow-up. Participants from the Delaware-Oslo ACL Cohort had higher KOOS subscale (p < 0.001) and IKDC-SKF scores (p < 0.001), and a higher percentage reached PASS (84%-96% vs. 62%-76%, p ≤ 0.002) for KOOS Pain, symptoms, activities of daily living and sports compared to the usual care group. No significant differences were found for KOOS quality of life scores (not significant [n.s.]) or PASS percentages (80% vs. 74%, n.s.). CONCLUSION: Participants with ACLR who followed the Delaware-Oslo ACL Cohort treatment algorithm had reduced knee symptoms, superior function and higher percentages of satisfactory outcomes than participants who followed usual care. LEVEL OF EVIDENCE: Level II.

Previous cartilage surgery is associated with inferior patient-reported outcomes after knee arthroplasty.

PURPOSE: The hypothesis of the present study assumed that a history of focal cartilage lesions would not affect Knee Injury and Osteoarthritis Outcome scores (KOOSs) following knee arthroplasty compared to a matched national cohort of knee arthroplasty patients. METHODS: Fifty-eight knee arthroplasty patients with previous surgery for focal cartilage lesions (cartilage cohort) were compared to a matched cohort of 116 knee arthroplasty patients from the Norwegian Arthroplasty Register (control group). Age, sex, primary or revision arthroplasty, type of arthroplasty (total, unicondylar or patellofemoral), year of arthroplasty surgery and arthroplasty brand were used as matching criteria. Demographic data and KOOS were obtained through questionnaires. Regression models were employed to adjust for confounding factors. RESULTS: Mean follow-up post knee arthroplasty surgery was 7.6 years (range 1.2-20.3) in the cartilage cohort and 8.1 (range 1.0-20.9) in the control group. The responding patients were at the time of surgery 54.3 versus 59.0 years in the cartilage and control group, respectively. At follow-up the control group demonstrated higher adjusted Knee Injury and Osteoarthritis Outcome subscores than the previous focal cartilage patients with a mean adjusted difference (95% confidence interval in parentheses): Symptoms 8.4 (0.3, 16.4), Pain 11.8 (2.2, 21.4), Activities of daily living (ADL) 9.3 (-1.2, 18.6), Sport and recreation 8.9 (-1.6, 19.4) and Quality of Life (QoL) 10.6 (0.2, 21.1). The control group also demonstrated higher odds of reaching the patient-acceptable symptom state threshold for the Knee Injury and Osteoarthritis Outcome subscores with odds ratio: Symptoms 2.7 (1.2, 6.4), Pain 3.0 (1.3, 7.0), ADL 2.1 (0.9, 4.6) and QoL 2.4 (1.0, 5.5). CONCLUSION: Previous cartilage surgery was associated with inferior patient-reported outcomes after knee arthroplasty. These patients also exhibited significantly lower odds of reaching the patient-acceptable symptom state threshold for the Knee Injury and Osteoarthritis Outcome subscores. LEVEL OF EVIDENCE: Level III.

External validation of the Norwegian anterior cruciate ligament reconstruction revision prediction model using patients from the STABILITY 1 Trial.

PURPOSE: A machine learning-based anterior cruciate ligament (ACL) revision prediction model has been developed using Norwegian Knee Ligament Register (NKLR) data, but lacks external validation outside Scandinavia. This study aimed to assess the external validity of the NKLR model (https://swastvedt.shinyapps.io/calculator_rev/) using the STABILITY 1 randomized clinical trial (RCT) data set. The hypothesis was that model performance would be similar. METHODS: The NKLR Cox Lasso model was selected for external validation owing to its superior performance in the original study. STABILITY 1 patients with all five predictors required by the Cox Lasso model were included. The STABILITY 1 RCT was a prospective study which randomized patients to receive either a hamstring tendon autograft (HT) alone or HT plus a lateral extra-articular tenodesis (LET). Since all patients in the STABILITY 1 trial received HT ± LET, three configurations were tested: 1: all patients coded as HT, 2: HT + LET group coded as bone-patellar tendon-bone (BPTB) autograft, 3: HT + LET group coded as unknown/other graft choice. Model performance was assessed via concordance and calibration. RESULTS: In total, 591/618 (95.6%) STABILITY 1 patients were eligible for inclusion, with 39 undergoing revisions within 2 years (6.6%). Model performance was best when patients receiving HT + LET were coded as BPTB. Concordance was similar to the original NKLR prediction model for 1- and 2-year revision prediction (STABILITY: 0.71; NKLR: 0.68-0.69). Concordance 95% confidence interval (CI) ranged from 0.63 to 0.79. The model was well calibrated for 1-year prediction while the 2-year prediction demonstrated evidence of miscalibration. CONCLUSION: When patients in STABILITY 1 who received HT + LET were coded as BPTB in the NKLR prediction model, concordance was similar to the index study. However, due to a wide 95% CI, the true performance of the prediction model with this Canadian and European cohort is unclear and a larger data set is required to definitively determine the external validity. Further, better calibration for 1-year predictions aligns with general prediction modelling challenges over longer periods. While not a large enough sample size to elicit the true accuracy and external validity of the prediction model when applied to North American patients, this analysis provides more support for the notion that HT plus LET performs similarly to BPTB reconstruction. In addition, despite the wide confidence interval, this study suggests optimism regarding the accuracy of the model when applied outside of Scandinavia. LEVEL OF EVIDENCE: Level 3, cohort study.

A Machine Learning Model Demonstrates Excellent Performance in Predicting Subscapularis Tears Based on Pre-Operative Imaging Parameters Alone.

PURPOSE: To develop a machine learning model capable of identifying subscapularis tears before surgery based on imaging and physical examination findings. METHODS: Between 2010 and 2020, 202 consecutive shoulders underwent arthroscopic rotator cuff repair by a single surgeon. Patient demographics, physical examination findings (including range of motion, weakness with internal rotation, lift/push-off test, belly press test, and bear hug test), and imaging (including direct and indirect signs of tearing, biceps status, fatty atrophy, cystic changes, and other similar findings) were included for model creation. RESULTS: Sixty percent of the shoulders had partial or full thickness tears of the subscapularis verified during surgery (83% of these were upper third). Using only preoperative imaging-related parameters, the XGBoost model demonstrated excellent performance at predicting subscapularis tears (c-statistic, 0.84; accuracy, 0.85; F1 score, 0.87). The top 5 features included direct signs related to the presence of tearing as evidenced on magnetic resonance imaging (MRI) (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. CONCLUSIONS: In this study, machine learning was successful in predicting subscapularis tears by MRI alone in 85% of patients, and this accuracy did not decrease by isolating the model to the top features. The top five features included direct signs related to the presence of tearing as evidenced on MRI (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Last, in advanced modeling, the addition of physical examination or patient characteristics did not make a significant difference in the predictive ability of this model. LEVEL OF EVIDENCE: Level III, diagnostic case-control study.

Injury Patterns in Posterolateral Corner Knee Injury.

Background: Posterolateral corner (PLC) knee injuries associated with different injury mechanisms are not well known. Purpose/Hypothesis: This study sought to assess the patterns of associated injuries in the setting of PLC injury. The hypothesis was that there are recognizable injury patterns in PLC injuries that may correlate with injury mechanism. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Patients who sustained a multiligament knee injury were retrospectively reviewed. Patients who sustained an acute grade 3 PLC injury and underwent surgery were enrolled in this study. A description of the PLC injury (location of the injury of the fibular collateral ligament [FCL], popliteus tendon, and/or popliteofibular ligament) and reported concomitant injuries (biceps femoris tendon or meniscal tears, cartilage pathology and/or peroneal nerve palsy, or bone bruises) were collected and classified based on intraoperative and magnetic resonance imaging (MRI) findings. Results: Of 135 patients reviewed, 83 did not have PLC involvement and 13 were excluded due to insufficient MRI scans available. Thus, 39 patients were included in this study. For both the anterior cruciate ligament (ACL)-PLC and ACL-posterior cruciate ligament-PLC injury patterns, the most frequent injury pattern entailed a bone bruise of the anteromedial (AM) femur and tibia, an FCL tear from the fibular head, the popliteus tendon avulsed off the femur, a biceps femoris tendon torn off the fibular head, and a common peroneal nerve palsy. Conversely, when no bone bruise occurred on the AM femur and tibia, the FCL was injured on the femoral side and the popliteus tendon, biceps femoris, and peroneal nerve were not injured. Conclusion: AM bone bruise was associated with a peroneal nerve injury in almost half of the patients, and peroneal nerve injury was not seen if there was no AM bone bruise.

Ceiling Effect of the Combined Norwegian and Danish Knee Ligament Registers Limits Anterior Cruciate Ligament Reconstruction Outcome Prediction.

BACKGROUND: Clinical tools based on machine learning analysis now exist for outcome prediction after primary anterior cruciate ligament reconstruction (ACLR). Relying partly on data volume, the general principle is that more data may lead to improved model accuracy. PURPOSE/HYPOTHESIS: The purpose was to apply machine learning to a combined data set from the Norwegian and Danish knee ligament registers (NKLR and DKRR, respectively), with the aim of producing an algorithm that can predict revision surgery with improved accuracy relative to a previously published model developed using only the NKLR. The hypothesis was that the additional patient data would result in an algorithm that is more accurate. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: Machine learning analysis was performed on combined data from the NKLR and DKRR. The primary outcome was the probability of revision ACLR within 1, 2, and 5 years. Data were split randomly into training sets (75%) and test sets (25%). There were 4 machine learning models examined: Cox lasso, random survival forest, gradient boosting, and super learner. Concordance and calibration were calculated for all 4 models. RESULTS: The data set included 62,955 patients in which 5% underwent a revision surgical procedure with a mean follow-up of 7.6 ± 4.5 years. The 3 nonparametric models (random survival forest, gradient boosting, and super learner) performed best, demonstrating moderate concordance (0.67 [95% CI, 0.64-0.70]), and were well calibrated at 1 and 2 years. Model performance was similar to that of the previously published model (NKLR-only model: concordance, 0.67-0.69; well calibrated). CONCLUSION: Machine learning analysis of the combined NKLR and DKRR enabled prediction of the revision ACLR risk with moderate accuracy. However, the resulting algorithms were less user-friendly and did not demonstrate superior accuracy in comparison with the previously developed model based on patients from the NKLR alone, despite the analysis of nearly 63,000 patients. This ceiling effect suggests that simply adding more patients to current national knee ligament registers is unlikely to improve predictive capability and may prompt future changes to increase variable inclusion.

The Long-Term Risk of Knee Arthroplasty in Patients with Arthroscopically Verified Focal Cartilage Lesions: A Linkage Study with the Norwegian Arthroplasty Register, 1999 to 2020.

BACKGROUND: Focal cartilage lesions are common in the knee. The risk of later ipsilateral knee arthroplasty remains unknown. The purposes of the present study were to evaluate the long-term cumulative risk of knee arthroplasty after arthroscopic identification of focal cartilage lesions in the knee, to investigate the risk factors for subsequent knee arthroplasty, and to estimate the subsequent cumulative risk of knee arthroplasty compared with that in the general population. METHODS: Patients who had undergone surgical treatment of focal cartilage lesions at 6 major Norwegian hospitals between 1999 and 2012 were identified. The inclusion criteria were an arthroscopically classified focal cartilage lesion in the knee, an age of ≥18 years at the time of surgery, and available preoperative patient-reported outcomes (PROMs). The exclusion criteria were osteoarthritis or "kissing lesions" at the time of surgery. Demographic data, later knee surgery, and PROMs were collected with use of a questionnaire. A Cox regression model was used to adjust for and investigate the impact of risk factors, and Kaplan-Meier analysis was performed to estimate cumulative risk. The risk of knee arthroplasty in the present cohort was compared with that in the age-matched general Norwegian population. RESULTS: Of the 516 patients who were eligible, 322 patients (328 knees) consented to participate. The mean age at the time of the index procedure was 36.8 years, and the mean duration of follow-up was 19.8 years. The 20-year cumulative risk of knee arthroplasty in the cartilage cohort was 19.1% (95% CI, 14.6% to 23.6%). Variables that had an impact on the risk of knee arthroplasty included an ICRS grade of 3 to 4 (hazard ratio [HR], 3.1; 95% CI, 1.1 to 8.7), an age of ≥40 years at time of cartilage surgery (HR, 3.7; 95% CI, 1.8 to 7.7), a BMI of 25 to 29 kg/m 2 (HR, 3.9; 95% CI, 1.7 to 9.0), a BMI of ≥30 kg/m 2 (HR, 5.9; 95% CI, 2.4 to 14.3) at the time of follow-up, autologous chondrocyte implantation (ACI) at the time of the index procedure (HR, 3.4; 95% CI, 1.0 to 11.4), >1 focal cartilage lesion (HR, 2.1; 95% CI, 1.1 to 3.7), and a high preoperative visual analog scale (VAS) score for pain at the time of the index procedure (HR, 1.1; 95% CI, 1.0 to 1.1). The risk ratio of later knee arthroplasty in the cartilage cohort as compared with the age-matched general Norwegian population was 415.7 (95% CI, 168.8 to 1,023.5) in the 30 to 39-year age group. CONCLUSIONS: In the present study, we found that the 20-year cumulative risk of knee arthroplasty after a focal cartilage lesion in the knee was 19%. Deep lesions, higher age at the time of cartilage surgery, high BMI at the time of follow-up, ACI, and >1 cartilage lesion were associated with a higher risk of knee arthroplasty. LEVEL OF EVIDENCE: Prognostic Level IV . See Instructions for Authors for a complete description of levels of evidence.

Characterization of POLE c.1373A > T p.(Tyr458Phe), causing high cancer risk.

The cancer syndrome polymerase proofreading-associated polyposis results from germline mutations in the POLE and POLD1 genes. Mutations in the exonuclease domain of these genes are associated with hyper- and ultra-mutated tumors with a predominance of base substitutions resulting from faulty proofreading during DNA replication. When a new variant is identified by gene testing of POLE and POLD1, it is important to verify whether the variant is associated with PPAP or not, to guide genetic counseling of mutation carriers. In 2015, we reported the likely pathogenic (class 4) germline POLE c.1373A > T p.(Tyr458Phe) variant and we have now characterized this variant to verify that it is a class 5 pathogenic variant. For this purpose, we investigated (1) mutator phenotype in tumors from two carriers, (2) mutation frequency in cell-based mutagenesis assays, and (3) structural consequences based on protein modeling. Whole-exome sequencing of two tumors identified an ultra-mutator phenotype with a predominance of base substitutions, the majority of which are C > T. A SupF mutagenesis assay revealed increased mutation frequency in cells overexpressing the variant of interest as well as in isogenic cells encoding the variant. Moreover, exonuclease repair yeast-based assay supported defect in proofreading activity. Lastly, we present a homology model of human POLE to demonstrate structural consequences leading to pathogenic impact of the p.(Tyr458Phe) mutation. The three lines of evidence, taken together with updated co-segregation and previously published data, allow the germline variant POLE c.1373A > T p.(Tyr458Phe) to be reclassified as a class 5 variant. That means the variant is associated with PPAP.

Return to Pivoting Sports after Cartilage Repair Surgery of the Knee: A Scoping Review.

OBJECTIVE: To perform a scoping literature review evaluating and reporting on outcomes and return to pivoting sports after cartilage procedures. For this review, the following cartilage procedures were evaluated: microfracture, osteochondral autograft transplantation (OAT), osteochondral allograft transplantation (OCA), and autologous chondrocyte implantation (ACI). DESIGN: The scoping review incorporated articles identified using PubMed (MEDLINE), CINAHL, and Cochrane Central Register of Controlled Trials. Screening of reference lists of included studies and forward citation tracking were performed to identify additional studies. Reported on return to pivoting sports after cartilage surgery written in English language. RESULTS: Sixteen studies fulfilled the inclusion criteria. The return to sports (RTS) rates after microfracture ranged from 44% to 83%, and to preinjury level from 25% to 75%. The RTS rates after OAT ranged from 87% to 100%, and to preinjury level from 67% to 93%. The RTS rates after OCA ranged from 77% to 80%, and to preinjury level 64%. The RTS rates after ACI ranged from 33% to 96%, and to preinjury level from 26% to 67%. CONCLUSIONS: There was a high heterogeneity and range in rates of RTS in athletes participating in pivoting sports. Most studies reported high rates of RTS; however, return to preinjury level was lower. These data may be important to clinicians in shared decision making on the type of procedure to be performed and counseling pivoting sports athletes on prognosis and expected RTS rates.

Predicting subjective failure of ACL reconstruction: a machine learning analysis of the Norwegian Knee Ligament Register and patient reported outcomes.

OBJECTIVES: Accurate prediction of outcome following anterior cruciate ligament (ACL) reconstruction is challenging, and machine learning has the potential to improve our predictive capability. The purpose of this study was to determine if machine learning analysis of the Norwegian Knee Ligament Register (NKLR) can (1) identify the most important risk factors associated with subjective failure of ACL reconstruction and (2) develop a clinically meaningful calculator for predicting the probability of subjective failure following ACL reconstruction. METHODS: Machine learning analysis was performed on the NKLR. All patients with 2-year follow-up data were included. The primary outcome was the probability of subjective failure 2 years following primary surgery, defined as a Knee Injury and Osteoarthritis Outcome Score (KOOS) Quality of Life (QoL) subscale score of <44. Data were split randomly into training (75%) and test (25%) sets. Four models intended for this type of data were tested: Lasso logistic regression, random forest, generalized additive model (GAM), and gradient boosted regression (GBM). These four models represent a range of approaches to statistical details like variable selection and model complexity. Model performance was assessed by calculating calibration and area under the curve (AUC). RESULTS: Of the 20,818 patients who met the inclusion criteria, 11,630 (56%) completed the 2-year follow-up KOOS QoL questionnaire. Of those with complete KOOS data, 22% reported subjective failure. The lasso logistic regression, GBM, and GAM all demonstrated AUC in the moderate range (0.67-0.68), with the GAM performing best (0.68; 95% CI 0.64-0.71). Lasso logistic regression, GBM, and the GAM were well-calibrated, while the random forest showed evidence of mis-calibration. The GAM was selected to create an in-clinic calculator to predict subjective failure risk at a patient-specific level (https://swastvedt.shinyapps.io/calculator_koosqol/). CONCLUSION: Machine learning analysis of the NKLR can predict subjective failure risk following ACL reconstruction with fair accuracy. This algorithm supports the creation of an easy-to-use in-clinic calculator for point-of-care risk stratification. Clinicians can use this calculator to estimate subjective failure risk at a patient-specific level when discussing outcome expectations preoperatively. LEVEL OF EVIDENCE: Level-III Retrospective review of a prospective national register.

Good validity in the Norwegian Knee Ligament Register: assessment of data quality for key variables in primary and revision cruciate ligament reconstructions from 2004 to 2013.

BACKGROUND: The Norwegian Knee Ligament Register was founded in 2004 to provide representative and reliable data on cruciate ligament surgery. The aim of this study was to evaluate the validity of key variables in the Norwegian Knee Ligament Register to reveal and prevent systematic errors or incompleteness, which can lead to biased reports and study conclusions. METHOD: We included a stratified cluster sample of 83 patients that had undergone both primary and revision anterior cruciate ligament surgery. A total of 166 medical records were reviewed and compared with their corresponding data in the database of the Norwegian Knee Ligament Register. We assessed the validity of a selection of key variables using medical records as a reference standard to compute the positive predictive values of the register data for the variables. RESULTS: The positive predictive values for the variables of primary and revision surgery ranged from 92 to 100% and from 39 to 100% with a mean positive predictive value of 99% and 88% respectively. Data on intraoperative findings and surgical details had high positive predictive values, ranging from 91 to 100% for both primary and revision surgery. The positive predictive value for the variable "date of injury" was 92% for primary surgeries but only 39% for revision surgeries. The positive predictive value for "activity at the time of injury" was 99% for primary surgeries and 52% for revisions. CONCLUSION: Overall, the data quality of the key variables examined in the Norwegian Knee Ligament Register was high, making the register a valid source for research.

Comparative Outcomes Occur After Superficial Medial Collateral Ligament Augmented Repair vs Reconstruction: A Prospective Multicenter Randomized Controlled Equivalence Trial.

BACKGROUND: Although previous studies have reported good short-term results for superficial medial collateral ligament (sMCL) reconstruction, whether an augmented MCL repair is clinically equivalent remains unclear. PURPOSE/HYPOTHESIS: The purpose of this study was to compare clinical outcomes between randomized groups that underwent sMCL augmentation repair and sMCL autograft reconstruction. The hypothesis was that there would be no significant differences in objective or subjective outcomes between groups. STUDY DESIGN: Randomized controlled trial; Level of evidence, 1. METHODS: Patients were prospectively enrolled between 2013 and 2019 from 3 centers. Grade III sMCL injuries were confirmed via stress radiography. Patients were randomized to anatomic sMCL reconstruction versus augmented repair with surgical treatment, determined after examination under anesthesia confirmed sMCL incompetence. Postoperative visits occurred at 6 weeks and 6 months for repeat evaluation, with repeat stress radiography at final follow-up. Patient-reported outcome measures were obtained pre- and postoperatively at 6 months, 1 year, and final follow-up. The primary outcome measure was side-to-side difference on valgus stress radiographs at a minimum follow-up of 1 year. The two 1-sided t test procedure was used to test clinical equivalence for side-to-side difference in valgus gapping, and the Mann-Whitney U test was used to compare postoperative patient-reported outcome measures between groups. RESULTS: A total of 54 patients were prospectively enrolled into this study. Of these, 50 patients had 6-month stress radiograph data, while 40 had 1-year postoperative valgus stress radiograph data. The mean (SD) patient age was 38.0 years (14.2), and body mass index was 25.0 (3.6). Preoperative valgus stress radiographs demonstrated 3.74 mm (1.1 mm) of increased side-to-side gapping overall, while it was 4.10 mm (1.46 mm) in the MCL augmentation group and 3.42 mm (0.55 mm) in the MCL reconstruction group. Postoperative valgus stress radiographs at an average of 6 months were obtained in 50 patients after surgery, which showed 0.21 mm (0.81 mm) for the MCL augmentation group and 0.19 mm (0.67 mm) for the MCL reconstruction group (P = .940). At final follow-up (minimum 1 year), median (interquartile range) Lysholm scores were significantly higher in the reconstruction group (90 [83-99]) as compared with the repair group (80 [67-92]) (P = .031). Final International Knee Documentation Committee (IKDC) scores were also significantly higher for the reconstruction group (85 [68-89]) versus the repair group (72 [60-78] (P = .039). Postoperative Tegner scores were not significantly different between the repair group (5 [3.5-6]) and the reconstruction group (5.5 [4-7]) (P = .123). Patient satisfaction was also not significantly different between repair (7.5 [5.75-9.25]) and reconstruction groups (9.0 [7-10]) (P = .184). CONCLUSION: This study found no difference in objective outcomes between an sMCL augmentation repair and a complete sMCL reconstruction at 1 year postoperatively, indicating equivalence between these procedures. Patient-reported clinical outcomes favored the reconstruction over a repair. In addition, this study demonstrated that anatomic-based treatment of MCL tears with an early knee motion program had a very low risk of graft attenuation and a low risk of arthrofibrosis.

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

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.

Full thickness quadriceps tendon grafts with bone had similar material properties to bone-patellar tendon-bone and a four-strand semitendinosus grafts: a biomechanical study.

PURPOSE: Despite increasing interest in utilizing quadriceps tendon (QT) grafts in anterior cruciate ligament reconstruction (ACLR), data on the optimal quadriceps graft thickness are limited. The purpose of this study was to characterize the mechanical properties for the quadriceps tendon, comparing full-thickness (FT) QT grafts with and without bone to a partial-thickness (PT) QT graft, and comparing the three QT grafts to four-stranded semitendinosus (4-SST) and bone-patellar tendon-bone (BTB) grafts and one experimental graft, the two-stranded rectus femoris (RF). METHODS: Forty-eight (n = 48) young cadaveric grafts (mean age 32 ± 6 years) were utilized for testing with N = 8 specimens in each of the following groups; (1) FT QT with bone, (2) FT QT without bone, (3) PT QT without bone, (4) BTB, (5) RF, and (6) 4-SST. Each specimen was harvested and rigidly fixed in custom clamps to a dynamic tensile testing machine for biomechanical evaluation. Graft ultimate load and stiffness were recorded. Independent groups one-factor ANOVAs and Tukey's pairwise comparisons were performed for statistical analyses. RESULTS: FT QT with bone and 4-SST grafts demonstrated similar ultimate loads to BTB grafts (both n.s), whereas PT QT demonstrate statistically significantly lower ultimate loads to BTB grafts (n.s) and 4-SST grafts (n.s). Furthermore, no statistically significant differences were observed between the ultimate loads of FT QT vs. PT QT grafts without bone (n.s) or between FT QT with vs. without bone (n.s). FT QT grafts with bone did not demonstrate statistically significantly greater ultimate loads than PT QT grafts without bone (n.s). The RF graft demonstrated statistically significantly lower ultimate loads to BTB grafts (p < 0.005) and 4-SST grafts (p < 0.014). CONCLUSIONS: Full thickness QT grafts with bone had similar material properties to BTB and a 4-SST grafts, while Partial thickness QT graft without bone had significantly lower material properties than BTB and 4-SST, in a biomechanical setting.

Predicting Anterior Cruciate Ligament Reconstruction Revision: A Machine Learning Analysis Utilizing the Norwegian Knee Ligament Register.

BACKGROUND: Several factors are associated with an increased risk of anterior cruciate ligament (ACL) reconstruction revision. However, the ability to accurately translate these factors into a quantifiable risk of revision at a patient-specific level has remained elusive. We sought to determine if machine learning analysis of the Norwegian Knee Ligament Register (NKLR) can identify the most important risk factors associated with subsequent revision of primary ACL reconstruction and develop a clinically meaningful calculator for predicting revision of primary ACL reconstruction. METHODS: Machine learning analysis was performed on the NKLR data set. The primary outcome was the probability of revision ACL reconstruction within 1, 2, and/or 5 years. Data were split randomly into training sets (75%) and test sets (25%). Four machine learning models were tested: Cox Lasso, survival random forest, generalized additive model, and gradient boosted regression. Concordance and calibration were calculated for all 4 models. RESULTS: The data set included 24,935 patients, and 4.9% underwent a revision surgical procedure during a mean follow-up (and standard deviation) of 8.1 ± 4.1 years. All 4 models were well-calibrated, with moderate concordance (0.67 to 0.69). The Cox Lasso model required only 5 variables for outcome prediction. The other models either used more variables without an appreciable improvement in accuracy or had slightly lower accuracy overall. An in-clinic calculator was developed that can estimate the risk of ACL revision (Revision Risk Calculator). This calculator can quantify risk at a patient-specific level, with a plausible range from near 0% for low-risk patients to 20% for high-risk patients at 5 years. CONCLUSIONS: Machine learning analysis of a national knee ligament registry can predict the risk of ACL reconstruction revision with moderate accuracy. This algorithm supports the creation of an in-clinic calculator for point-of-care risk stratification based on the input of only 5 variables. Similar analysis using a larger or more comprehensive data set may improve the accuracy of risk prediction, and future studies incorporating patients who have experienced failure of ACL reconstruction but have not undergone subsequent revision may better predict the true risk of ACL reconstruction failure. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.