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.

Unsupervised Machine Learning of the Combined Danish and Norwegian Knee Ligament Registers: Identification of 5 Distinct Patient Groups With Differing ACL Revision Rates.

BACKGROUND: Most clinical machine learning applications use a supervised learning approach using labeled variables. In contrast, unsupervised learning enables pattern detection without a prespecified outcome. PURPOSE/HYPOTHESIS: The purpose of this study was to apply unsupervised learning to the combined Danish and Norwegian knee ligament register (KLR) with the goal of detecting distinct subgroups. It was hypothesized that resulting groups would have differing rates of subsequent anterior cruciate ligament reconstruction (ACLR) revision. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: K-prototypes clustering was performed on the complete case KLR data. After performing the unsupervised learning analysis, the authors defined clinically relevant characteristics of each cluster using variable summaries, surgeons' domain knowledge, and Shapley Additive exPlanations analysis. RESULTS: Five clusters were identified. Cluster 1 (revision rate, 9.9%) patients were young (mean age, 22 years; SD, 6 years), received hamstring tendon (HT) autograft (91%), and had lower baseline Knee injury and Osteoarthritis Outcome Score (KOOS) Sport and Recreation (Sports) scores (mean, 25.0; SD, 15.6). Cluster 2 (revision rate, 6.9%) patients received HT autograft (89%) and had higher baseline KOOS Sports scores (mean, 67.2; SD, 16.5). Cluster 3 (revision rate, 4.7%) patients received bone-patellar tendon-bone (BPTB) or quadriceps tendon (QT) autograft (94%) and had higher baseline KOOS Sports scores (mean, 65.8; SD, 16.4). Cluster 4 (revision rate, 4.1%) patients received BPTB or QT autograft (88%) and had low baseline KOOS Sports scores (mean, 20.5; SD, 14.0). Cluster 5 (revision rate, 3.1%) patients were older (mean age, 42 years; SD, 7 years), received HT autograft (89%), and had low baseline KOOS Sports scores (mean, 23.4; SD, 17.6). CONCLUSION: Unsupervised learning identified 5 distinct KLR patient subgroups and each grouping was associated with a unique ACLR revision rate. Patients can be approximately classified into 1 of the 5 clusters based on only 3 variables: age, graft choice (HT, BPTB, or QT autograft), and preoperative KOOS Sports subscale score. If externally validated, the resulting groupings may enable quick risk stratification for future patients undergoing ACLR in the clinical setting. Patients in cluster 1 are considered high risk (9.9%), cluster 2 patients medium risk (6.9%), and patients in clusters 3 to 5 low risk (3.1%-4.7%) for revision ACLR.

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.

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.

Major Increase in Incidence of Pediatric ACL Reconstructions From 2005 to 2021: A Study From the Norwegian Knee Ligament Register.

BACKGROUND: The incidence of pediatric and adolescent anterior cruciate ligament reconstruction (ACLR) is increasing in several countries. It is uncertain whether this trend applies to countries that traditionally prefer an initial nonoperative treatment approach whenever possible, like Norway. Nationwide, long-term patient-reported outcomes and revision rates after ACLR in the pediatric population are also lacking. PURPOSE: To determine the incidence of pediatric ACLR in Norway since 2005, as well as to detect trends in surgical details and describe patient-reported outcomes up to 10 years after ACLR. STUDY DESIGN: Descriptive cohort study. METHODS: This study is based on prospectively collected data on girls ≤14 years and boys ≤16 years, registered in the Norwegian Knee Ligament Register at the time of their primary ACLR, between 2005 and 2021. The main outcome was the incidence of ACLR, adjusted to the corresponding population numbers for each year. The time trend was analyzed by comparing the mean of the first and last 3-year period (2005-2007 and 2019-2021). Patient-reported outcomes were assessed using the Knee injury and Osteoarthritis Outcome Score preoperatively and at 2, 5, and 10 years postoperatively. RESULTS: A total of 1476 patients (1484 cases) were included, with a mean follow-up of 8.1 years (range, 1-17). The incidence of pediatric ACLRs per 100,000 population increased from 18 to 26, which corresponds to an increase of 40% for boys and 55% for girls. Concurrent meniscal procedures increased significantly from 45% to 62%, and the proportion of meniscal repairs increased from 19% to 43% when comparing the first and last time period. The mean Knee injury and Osteoarthritis Outcome Score values for the Sport and Recreation and Quality of Life subscales were between 72 and 75 at the 2-, 5- and 10-year follow-up. The 5-year revision rate was 9.9%. CONCLUSION: There was a major increase in incidence of pediatric ACLR in Norway during the study period. There was a shift in the approach to concomitant meniscal procedures from resection to repair, with more than a doubling of the proportion of meniscal repairs. Patient-reported outcomes revealed long-lasting reduced knee function.

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.

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.

ACL Reconstruction Patients Have Increased Risk of Knee Arthroplasty at 15 Years of Follow-up: Data from the Norwegian Knee Ligament Register and the Norwegian Arthroplasty Register from 2004 to 2020.

Anterior cruciate ligament (ACL) injury is considered a risk factor for osteoarthritis. The primary aim of the present study was to investigate the cumulative risk of, and risk factors associated with, a subsequent knee arthroplasty after an ACL reconstruction at up to 15 years of follow-up. The secondary aim was to compare the relative risk of knee arthroplasty after ACL reconstruction compared with that in the general population. Methods: Data were analyzed by combining data from 2 national registries, the Norwegian Knee Ligament Register and the Norwegian Arthroplasty Register. The cumulative risk of knee arthroplasty after undergoing ACL reconstruction was calculated as 1 minus the corresponding Kaplan-Meier estimate, and possible risk factors for knee arthroplasty after ACL reconstruction were assessed in a Cox regression model with hazard ratios (HRs) as estimated effect measurements. The relative risk of knee arthroplasty for patients managed with ACL reconstruction as compared with that in the general population was calculated in stratified age groups. Results: From the study population of 27,122 knees, 115 knees underwent knee arthroplasty. We found a 1.1% (95% confidence interval [CI], 0.9 to 1.4) cumulative risk of knee arthroplasty 15 years after ACL reconstruction. Deep cartilage injury, ICRS (International Cartilage Repair Society) grade 3 to 4 (HR, 4.8; 95% CI, 3.1 to 7.6), revision of the ACL (HR, 3.9; 95% CI, 2.2 to 7.1), and a 2-year postoperative KOOS Sport/Recreation subscore of <44 (HR, 3.1; 95% CI, 1.5 to 6.2) were important risk factors for knee arthroplasty. We found a higher risk of knee arthroplasty at the age of 30 to 39 years after a previous ACL reconstruction as compared with the general population (relative risk, 3.3; 95% CI, 1.6 to 6.7). Conclusions: Fifteen years after an ACL reconstruction, the overall cumulative risk of knee arthroplasty was 1.1%. Cartilage injury at the time of ACL reconstruction, revision ACL reconstruction, and a KOOS Sport/Recreation subscore of <44 (at 2 years postoperatively) were major risk factors for subsequent knee arthroplasty. We found a 3.3-times higher risk of knee arthroplasty at the age of 30 to 39 years after a previous ACL reconstruction as compared with that in the general population. Level of Evidence: Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.

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.

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.

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.

Low annual hospital volume of anterior cruciate ligament reconstruction is not associated with higher revision rates.

PURPOSE: Surgery performed in low-volume centres has been associated with longer operating time, longer hospital stays, lower functional outcomes, and higher rates of revision surgery, complications and mortality. This has been reported consistently in the arthroplasty literature, but there is a paucity of data regarding the relationship between surgical volume and outcome following anterior cruciate ligament (ACL) reconstruction. The purpose was to compare ACL reconstruction failure rates between hospitals performing different annual surgical volumes. METHODS: All patients from the Norwegian Knee Ligament Register having primary autograft ACL reconstruction between 2004 and 2016 were included. Hospital volume was divided into quintiles based on the number of ACL reconstructions performed annually, defined arbitrarily as: 1-12 (V1), 13-24 (V2), 25-49 (V3), 50-99 (V4) and ≥ 100 (V5) annual procedures. Kaplan-Meier estimated survival curves and survival percentages were calculated with revision ACL reconstruction as the end point. Secondary outcome measures included (1) mean change in Knee Injury and Osteoarthritis Outcome Score (KOOS) Quality of Life (QoL) and Sport subsections from pre-operative to 5-year follow-up and (2) subjective failure defined as KOOS QoL < 44. RESULTS: Twenty thousand eight hundred and fifty patients met the inclusion criteria and 1195 (5.7%) underwent subsequent revision ACL reconstruction over the study period. Revision rates were lower in the lower volume hospitals compared with the higher volume hospitals (p < 0.001). There was no clinically significant difference in improvement between pre-operative and 5-year follow-up KOOS scores between hospital volume categories, but a higher proportion of patients having surgery at lower volume hospitals reported a subjective failure. Patients in the lower volume categories (V1-3) were more often male and older compared to the higher volume hospitals (V4-5). Concomitant meniscal injuries and participation in pivoting sports were most common in V5 compared with V1 (p < 0.001). Median operative time decreased as hospital volume increased, ranging from 90 min at V1 hospitals to 56 min at V5 hospitals (p < 0.001). CONCLUSION: Patients having ACL reconstruction at lower volume hospitals had a lower rate of subsequent revision surgery relative to higher volume hospitals. However, complications occurred more frequently, operative duration was longer, and the number of patients reporting a subjective failure of ACL reconstruction was highest at these lower volume hospitals. LEVEL OF EVIDENCE: Level III.

The missing pieces for better future predictions in subarctic ecosystems: A Torneträsk case study.

Arctic and subarctic ecosystems are experiencing substantial changes in hydrology, vegetation, permafrost conditions, and carbon cycling, in response to climatic change and other anthropogenic drivers, and these changes are likely to continue over this century. The total magnitude of these changes results from multiple interactions among these drivers. Field measurements can address the overall responses to different changing drivers, but are less capable of quantifying the interactions among them. Currently, a comprehensive assessment of the drivers of ecosystem changes, and the magnitude of their direct and indirect impacts on subarctic ecosystems, is missing. The Torneträsk area, in the Swedish subarctic, has an unrivalled history of environmental observation over 100 years, and is one of the most studied sites in the Arctic. In this study, we summarize and rank the drivers of ecosystem change in the Torneträsk area, and propose research priorities identified, by expert assessment, to improve predictions of ecosystem changes. The research priorities identified include understanding impacts on ecosystems brought on by altered frequency and intensity of winter warming events, evapotranspiration rates, rainfall, duration of snow cover and lake-ice, changed soil moisture, and droughts. This case study can help us understand the ongoing ecosystem changes occurring in the Torneträsk area, and contribute to improve predictions of future ecosystem changes at a larger scale. This understanding will provide the basis for the future mitigation and adaptation plans needed in a changing climate.

How boys and testicles wander to surgery: a nationwide cohort study of surgical delay in Sweden.

BACKGROUND: Early orchidopexy is recommended for cryptorchidism and the surgery is increasingly centralised. The objectives were to determine the incidence, risk factors and if distance to treating hospital impacted on timely treatment of cryptorchidism. METHODS: In this observational study, all boys born in Sweden from 2001 to 2014 were followed in national registers to determine the incidence of cryptorchidism by levels of birth-related risk factors and social determinants. Travel time to hospital was used as the primary exposure in multivariable survival analysis, with age at surgery as main outcome. RESULTS: Of 748 678 boys at risk for cryptorchidism, 7351 were treated and evaluated for timing of surgery (cumulative childhood incidence 1.4%, 95% CI 1.3% to 1.5%). The incidence was clearly associated with prematurity and overdue pregnancy (HR for <32 weeks 2.77 (95% CI 2.39 to 3.21); 32-36 weeks HR 1.36 (95% CI 1.24 to 1.49); >41 weeks HR 1.19 (95% CI 1.10 to 1.29)), low birth weight (<1000 g HR 3.94 (95% CI 3.15 to 4.92); 1000-1499 g HR 3.70 (95% CI 3.07 to 4.46); 1500-2500 g HR 1.69 (95% CI 1.52 to 1.88)) and intrauterine growth restriction (small for gestational age HR 2.38 (95% CI 2.14 to 2.65); large for gestational age HR 1.26 (95% CI 1.13 to 1.42)), but not with smoking or maternal age. Each 30 min increase in travel time was associated with a reduced probability of timely treatment (HR for being treated by age 3 adjusted for risk factors and socioeconomic determinants: 0.91 (95% CI 0.88 to 0.95)). Lower income and financial support were also associated with treatment delays (adjusted HR for lowest income quintile 0.82 (95% CI 0.72 to 0.93) and for families with financial support 0.85 (95% CI 0.73 to 0.97)). CONCLUSIONS: Travel distance to treating hospital was associated with delayed treatment. 'Not all those who wander are lost', but these findings suggest a trade-off between centralisation benefits and barriers of geography also in elective paediatric surgery.

Evaluation of a new X-band weather radar for operational use in south Sweden.

The performance of a new type of X-band weather radar (WR) for Sweden during a pilot run is studied. Compared to the conventional C-band WRs, the X-band WR covers a smaller area but with a higher spatiotemporal resolution, making it suitable for urban hydrological applications. Rainfall estimations from different elevation angles of the radar (levels) are compared at one-minute and single-event timescales with the observations of several rain gauges at different ranges using hyetographs. In general, the estimations aligned well with observations and the best match appeared for ranges as long as 5-10 km. Seemingly, radar estimations suffered from overshooting of lower lying showers by higher level scans in longer ranges (19-30 km) and from the reflectivity contamination due to moving objects in short ranges (<1 km). Also, the effective range of the radar dropped sharply for the moments when a cloudburst was located over the radar. Although various sources of error could affect the X-band WR rainfall estimates, higher resolution spatiotemporal rainfall monitoring for wider areas will benefit from an integration of data from a network of X-band WRs.

Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring.

Robotic agents should be able to learn from sub-symbolic sensor data and, at the same time, be able to reason about objects and communicate with humans on a symbolic level. This raises the question of how to overcome the gap between symbolic and sub-symbolic artificial intelligence. We propose a semantic world modeling approach based on bottom-up object anchoring using an object-centered representation of the world. Perceptual anchoring processes continuous perceptual sensor data and maintains a correspondence to a symbolic representation. We extend the definitions of anchoring to handle multi-modal probability distributions and we couple the resulting symbol anchoring system to a probabilistic logic reasoner for performing inference. Furthermore, we use statistical relational learning to enable the anchoring framework to learn symbolic knowledge in the form of a set of probabilistic logic rules of the world from noisy and sub-symbolic sensor input. The resulting framework, which combines perceptual anchoring and statistical relational learning, is able to maintain a semantic world model of all the objects that have been perceived over time, while still exploiting the expressiveness of logical rules to reason about the state of objects which are not directly observed through sensory input data. To validate our approach we demonstrate, on the one hand, the ability of our system to perform probabilistic reasoning over multi-modal probability distributions, and on the other hand, the learning of probabilistic logical rules from anchored objects produced by perceptual observations. The learned logical rules are, subsequently, used to assess our proposed probabilistic anchoring procedure. We demonstrate our system in a setting involving object interactions where object occlusions arise and where probabilistic inference is needed to correctly anchor objects.

Re-revision Anterior Cruciate Ligament Reconstruction: An Evaluation From the Norwegian Knee Ligament Registry.

PURPOSE: To identify the rate of re-revision anterior cruciate ligament reconstruction (ACLR) to estimate the influence of patient-related factors on the risk of re-revision ACLR. The secondary aim of the study was to report the intra-articular findings and patient-related factors at the time of revision ACLR and to compare these with the findings in a matched controlled group of primary ACLR. METHODS: Patients with primary ACLR without a subsequent need of revision and patients with a revision ACLR identified in the Norwegian Knee Ligament Registry from June 2004 through September 2016 were included. Using age at operation, sex, activity at injury, and year of ACLR as covariates, a propensity score matched control group of primary ACLR patients for the revision ACLR patients was identified. For the revision ACLR patients, re-revision ACLR rates at 1, 2, 5, and 8 years were estimated with Kaplan-Meier analysis; the hazard ratio for a re-revision ACLR was estimated using a multivariable Cox regression model. RESULTS: The cumulative estimated proportion of patients undergoing a re-revision ACLR at 1, 2, 5, and 8 years after the original revision ACLR was 0.4%, 3.0%, 6.5%, and 9.0% respectively. There was no significant difference between the control and revision ACLR groups regarding cartilage injury (P = .72) or associated ligament injury (P = .17). Revision ACLR patients did have fewer meniscal injuries (P < .001). There were no intraoperative findings or surgical techniques identified as a predictor for a higher risk of re-revision ACLR. CONCLUSIONS: Based on a review of a large ligament reconstruction registry,one can expect 9% of patients to undergo a re-revision ALCR at 8 years of follow up. Revision ACLR did not have an increase in cartilage injuries or associated ligament injuries and had significantly fewer meniscal injuries compared with a primary ACLR control group. LEVEL OF EVIDENCE: Level III, retrospective comparative study.

Graft Fixation and Timing of Surgery Are Predictors of Early Anterior Cruciate Ligament Revision: A Cohort Study from the Swedish and Norwegian Knee Ligament Registries Based on 18,425 Patients.

The identification of surgical risk factors for early anterior cruciate ligament (ACL) revision is important when appropriate treatment for patients undergoing primary ACL reconstruction is selected. The purposes of this study were to determine the short-term ACL revision rate of patients undergoing primary ACL reconstruction and to identify surgical risk factors for ACL revision within 2 years of primary ACL reconstruction. METHODS: This study was based on data collected prospectively from the Norwegian and Swedish National Knee Ligament Registries. Patients who underwent primary ACL reconstruction from 2004 through 2014 were included. We examined revisions through 2016. The relative risks (RRs) of revision ACL reconstruction dependent on graft fixation, the time interval between injury and surgical procedure, and meniscal and cartilage injury were estimated by using generalized linear models with a binomial distribution and log-link function. The outcome was set as revision ACL reconstruction during the first 2 years. RESULTS: A total of 58,692 patients were assessed for eligibility; of these, 18,425 patients were included. The overall 2-year revision rate was 2.1%. Patients treated with a metal interference screw had an increased risk of ACL revision when compared with patients who were treated with other femoral fixations (RR, 1.78 [95% confidence interval (CI), 1.38 to 2.29]; p < 0.001). The use of the RIGIDFIX Cross Pin System (DePuy Synthes) entailed a lower risk of ACL revision compared with other femoral fixations (RR, 0.58 [95% CI, 0.42 to 0.82]; p = 0.0017). Patients undergoing ACL reconstruction within 3 months of the injury had an increased risk of ACL revision (RR, 2.07 [95% CI, 1.64 to 2.61]; p < 0.001). CONCLUSIONS: Patients undergoing ACL reconstruction within 3 months of an injury, as well as patients treated with a metal interference screw in the femur, had a significantly higher risk of ACL revision, and patients treated with the RIGIDFIX Cross Pin in the femur had a significantly lower risk of ACL revision. LEVEL OF EVIDENCE: Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.