Single level discectomy (SLD) is one of the most commonly performed spinal surgery procedures. Two key drivers of their cost-of-care are duration of surgery (DOS) and postoperative length of stay (LOS). Therefore, the ability to preoperatively predict SLD DOS and LOS has substantial implications for both hospital and healthcare system finances, scheduling and resource allocation. As such, the goal of this study was to predict DOS and LOS for SLD using machine learning models (MLMs) constructed on preoperative factors using a large North American database. The American College of Surgeons (ACS) National Surgical and Quality Improvement (NSQIP) database was queried for SLD procedures from 2014-2019. The dataset was split in a 60/20/20 ratio of training/validation/testing based on year. Various MLMs (traditional regression models, tree-based models, and multilayer perceptron neural networks) were used and evaluated according to 1) mean squared error (MSE), 2) buffer accuracy (the number of times the predicted target was within a predesignated buffer), and 3) classification accuracy (the number of times the correct class was predicted by the models). To ensure real world applicability, the results of the models were compared to a mean regressor model. A total of 11,525 patients were included in this study. During validation, the neural network model (NNM) had the best MSEs for DOS (0.99) and LOS (0.67). During testing, the NNM had the best MSEs for DOS (0.89) and LOS (0.65). The NNM yielded the best 30-minute buffer accuracy for DOS (70.9%) and ≤120 min, >120 min classification accuracy (86.8%). The NNM had the best 1-day buffer accuracy for LOS (84.5%) and ≤2 days, >2 days classification accuracy (94.6%). All models were more accurate than the mean regressors for both DOS and LOS predictions. We successfully demonstrated that MLMs can be used to accurately predict the DOS and LOS of SLD based on preoperative factors. This big-data application has significant practical implications with respect to surgical scheduling and inpatient bedflow, as well as major implications for both private and publicly funded healthcare systems. Incorporating this artificial intelligence technique in real-time hospital operations would be enhanced by including institution-specific operational factors such as surgical team and operating room workflow

Objective. To evaluate the clinical and functional outcomes obtained by combination of high-flexion Freedom® Total Knee System (TKS) and mini-subvastus approach in total knee replacement patients. Method. This is a retrospective, observational, real world study conducted at Mumbai in India from 2011 to 2016. All patients who were above the age of 18 and operated for total knee replacement (TKR) with mini-subvastus approach using Freedom (Maxx Medical) by the senior author were included. The Implant survivorship was the survey endpoint; primary endpoint was range of motion (ROM); and secondary endpoints were AKSS (American Knee Society Score) and WOMAC (Western Ontario and McMaster Universities Osteoarthritis) scores collected pre- and post-operatively. Results. 184 patients with 242 knees (126 unilateral and 58 bilateral) were operated with high-flexion TKS. Average age of patients was 70 ± 6.2 years. The mean ROM increased from 99.4°±10.44° (50°-120°) preoperatively to 116.78°±8.18° (88°–140°) postoperatively (p<0.001). Clinical and functional AKSS scores improved from 60.83±5.12 to 91.16±2.19 (p<0.001) and 65.35±3.52 to 99.13±4.61 (p<0.001) respectively. There average WOMAC pain scores improved from 12.12±1.72 to 0.066±0.37 (<0.0001). Moreover, post-operative WOMAC stiffness and function scores depicted significant improvement from 4.43±0.97 to 0.03±0.26 (p<0.0001) and 0.03±0.26 to 0.18±1.21 (p<0.0001) respectively at a mean follow-up of 3.71 ± 0.98 years. Implant survivorship was 100%. Conclusion. High-flexion Freedom® TKS demonstrated a satisfactory clinical and functional improvements including high flexion when operated by the mini-subvastus approach at a mean FU of 4 years

Computer assisted surgery is becoming more frequently used in the medical world. Navigation of surgical instruments and implants plays an important role in this surgery. OrthoPilot™ Hip Suite (BBraun Aesculap) is one such system used for hip navigation in orthopaedic surgery. However the accuracy of this system remains to be determined independently of the manufacturer. The manufacturer supplies a technical specification for the accuracy of the system (± 2 mm and ± 2°) and previous research has been undertaken to compare its clinical accuracy against conventional hip replacements by x-ray. This clinical validation is important but contains many sources of error or deviation from an ideal outcome in terms of the surgeons' use of the system, inaccurate palpation of landmarks, variation in actual cup position from that given by the navigation system and measurement of the final cup position. It is therefore not possible to validate the claims of the manufacturer from this data. There is no literature evaluating the technical accuracy of the software i.e. the accuracy of the system given known inputs. This study had two main aims 1) validating the accuracy of the OrthoPilot data while navigating the surgical instruments and 2) validating the accuracy of navigation algorithm inside the OrthoPilot system which determines cup implant placement. The OrthoPilot validation was performed and compared against the gold standard of a VICON movement analysis system. The system used was OrthoPilot™ with a Spectra camera from Northern Digital Inc. (Ontario, Canada). Software investigated was the Hip Suite THA cup only navigation software Version 3.1. The validation was performed and compared against the VICON Nexus version 1.4.116 with Bodybuilder software version 3.55. An aluminium pelvis phantom was used for measurement allowing accurate and repeatable inputs. The OrthoPilot system has three types of instruments sets; passive, active and hybrid. This study was carried out with the passive instruments set. Data were captured simultaneously from both the OrthoPilot and VICON systems for the supine position of the phantom. Distances between the anatomical land marks on the phantom were compared to test the data capturing accuracy of the OrthoPilot system. Anatomical land marks of right anterior superior iliac supine (RASIS), left anterior superior iliac supine (LASIS) and Pubic Symphasis (PS) were palpated to define the Anterior Pelvic Plane (APP). Distances between the anatomical landmarks of RASIS to LASIS, RASIS to PS and LASIS to PS were considered for comparison. Width and height of the pelvis was varied to examine different APPs. The width and height used were 170 mm and 53 mm, 230 mm and 88 mm, and 290 mm and 123 mm respectively. One hundred APP data sets were captured at each instance. The accuracy of the hip navigation algorithm was tested by applying similar algorithm to calculate the native anteversion and inclination angles of the acetabulum using the VICON system. Data were captured simultaneously from both OrthoPilot and VICON systems. Radiographic anteversion and inclination angles were obtained with phantom model, which had 14° of anteversion angle and 45° of inclination angle. APP of 230 mm in width and 88 mm in height was used to obtain anterior pelvic plane data. Position vectors for each anatomical land mark from the OrthoPilot system were extracted from relevant transformation matrices, while position vectors from the VICON system were extracted from static trial modelling. The distance data from both systems were compared with calibrated distance data from the phantom model. Mean values of the distances between anatomical landmarks were found to be similar for both OrthoPilot and VICON systems. In addition, these distances were comparable with the pelvic phantom model data, within 1 mm for all measured distances for the VICON and 2 mm for the OrthoPilot. Furthermore, the standard deviations were less than 1% of the measured value. Comparison was also made for the anteversion and inclination angles of the acetabulum of the pelvic model with OrthoPilot and VICON data. Both systems produced similar results for the mean angle values, within 0.5° of the known angles for the VICON and 1° for the OrthoPilot and with standard deviations of the measured values of less than 1%. All the data were captured simultaneously from both OrthoPilot and VICON systems under the same laboratory conditions. According to the above results it is clear that the distance readings obtained from the OrthoPilot are comparable to the results obtained from the gold standard VICON system and the calibrated distance readings of the phantom. In addition, acetabular angle results obtained from OrthoPilot are almost equivalent to results obtained from VICON and the calibrated phantom angles. Finally it is can be concluded that, both the data palpation with OrthoPilot system and acetabular angle calculation algorithm of the OrthoPilot system are accurate enough for the real world clinical tasks they are expected to perform

Aims

The aim of this study was to develop and evaluate machine-learning-based computerized adaptive tests (CATs) for the Oxford Hip Score (OHS), Oxford Knee Score (OKS), Oxford Shoulder Score (OSS), and the Oxford Elbow Score (OES) and its subscales.

Aims

There is little published on the outcomes after restarting elective orthopaedic procedures following cessation of surgery due to the COVID-19 pandemic. During the pandemic, the reported perioperative mortality in patients who acquired SARS-CoV-2 infection while undergoing elective orthopaedic surgery was 18% to 20%. The aim of this study is to report the surgical outcomes, complications, and risk of developing COVID-19 in 2,316 consecutive patients who underwent elective orthopaedic surgery in the latter part of 2020 and comparing it to the same, pre-pandemic, period in 2019.