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. Recently, machine learning was used to develop a tool that can quantify revision risk for a patient undergoing primary anterior cruciate ligament (ACL) reconstruction (https://swastvedt.shinyapps.io/calculator_rev/). The source of data included nearly 25,000 patients with primary ACL reconstruction recorded in the Norwegian Knee Ligament Register (NKLR). The result was a well-calibrated tool capable of predicting revision risk one, two, and five years after primary ACL reconstruction with moderate accuracy. 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).

The primary outcome measure of the NKLR model was probability of revision ACL reconstruction within 1, 2, and/or 5 years. For the index study, 24 total predictor variables in the NKLR were included and the models eliminated variables which did not significantly improve prediction ability - without sacrificing accuracy. The result was a well calibrated algorithm developed using the Cox Lasso model that only required five variables (out of the original 24) for outcome prediction. For this external validation study, all DKLR patients with complete data for the five variables required for NKLR prediction were included. The five variables were: graft choice, femur fixation device, Knee Injury and Osteoarthritis Outcome Score (KOOS) Quality of Life subscale 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.

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.

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.

High tibial osteotomy (HTO) is an established treatment for medial compartment osteoarthritis of the knee; the aim being to achieve a somewhat valgus coronal alignment, thereby unloading the affected medial compartment. This study investigated knee kinematics and kinetics before and after HTO and compared them with matched control data.

A three dimensional motion analysis system and two force platforms were used to collect kinematic and kinetic data from eight patients with medial compartment knee osteoarthritis during walking preoperatively and 12 months following HTO (opening wedge). Nine control participants of similar age and the same sex were tested using the same protocol. Sagittal and coronal knee angles and moments were measured on both the operated and non-operated knees and compared between the two time points and between HTO participants and controls. In addition, preoperative and postoperative radiographic coronal plane alignments were compared in the HTO participants.

The point at which the mechanical axis passed through the knee joint was corrected from a preoperative mean of 10% tibial width from the medial tibial margin to 56% postoperatively. Stride length and walking speed both improved to essentially normal levels (1.57 m and 1.5 m/s) ostoperatively. In the coronal plane the mean peak adduction angle during stance reduced from 14.3° to 5.2° (control: 6.8°). Mean maximum adduction moments were similarly reduced to levels less than in control participants, in keeping with the aim of the surgical procedure: peak adduction moment 1: pre 3.8, post 2.7, control 3.6 peak adduction moment 2: pre 2.5, post 1.7 and control 2.6.

In the sagittal plane, both mean maximum flexion and extension during stance increased postoperatively—extension to greater than in control participants and flexion to almost control levels. The maximum external knee flexor moment during stance also increased to near normal postoperatively.

High tibial osteotomy appears to achieve the intended biomechanical effects in the coronal plane (reduced loading of the medial compartment during stance). At the same time there were improvements in sagittal plane kinematics and kinetics which may reflect a reduction in pain. The net effect was to reduce quadriceps demand.