We present the indications and outcomes of a series of custom 3D printed titanium acetabular implants used over a 9 year period at our institution (Sydney, Australia), in the setting of revision total hip arthroplasty.

Individualised image-based case planning with additive manufacturing of pelvic components was combined with screw fixation and off-the-shelf femoral components to treat patients presenting with failed hip arthroplasty involving acetabular bone loss. Retrospective chart review was performed on the practices of three contributing surgeons, with an initial search by item number of the Medicare Benefits Scheme linked to a case list maintained by the manufacturer. An analysis of indications, patient demographics and clinical outcome was performed.

The cohort comprised 65.2% female with a median age of 70 years (interquartile range 61–77) and a median follow up of 32.9 months (IQR 13.1 - 49.7). The indications for surgery were infection (12.5%); aseptic loosening (78.1%) and fracture (9.4%), with 65.7% of cases undergoing previous revision hip arthroplasty. A tumour prosthesis was implanted into the proximal femur in 21.9% of cases. Complications were observed in 31.3% of cases, with four cases requiring revision procedures and no deaths reported in this series. Kaplan-Meier analysis of all-cause revision revealed an overall procedure survival of 88.7% at two years (95%confidence interval 69 - 96.2) and 83.8% (95%CI 62 - 93.7) at five years, with pelvic implant-specific survival of 98% (95%CI 86.6 - 99.7) at two and five year follow up.

We conclude that an individualised planning approach for custom 3D printed titanium acetabular implants can provide high overall and implant-specific survival at up to five years follow up in complex cases of failed hip arthroplasty and acetabular bone loss.

PVNS or TGCT (Pigmented Villonodular Synovitis, or Tenosynovial Giant Cell tumour) is a benign tumour affecting the synovial lining of joints and tendon sheaths, historically treated with surgical excision or debridement. We have shown previously this management is fraught with high recurrence rates, especially in its diffuse form. We present the encouraging early results of medical management for this condition with use of a CSF1 inhibitor, in comparison to a cohort of 137 cases previously treated at our institution.

Degenerative lumbar spondylolisthesis (DLS) is a common condition with many available treatment options. The Degenerative Spondylolisthesis Instability Classification (DSIC) scheme, based on a systematic review of best available evidence, was proposed by Simmonds et al. in 2015. This classification scheme proposes that the stability of the patient's pathology be determined by a surgeon based on quantitative and qualitative clinical and radiographic parameters. The purpose of the study is to utilise machine learning to classify DLS patients according to the DSIC scheme, offering a novel approach in which an objectively consistent system is employed.

The patient data was collected by CSORN between 2015 and 2018 and included 224 DLS surgery cases. The data was cleaned by two methods, firstly, by deleting all patient entries with missing data, and secondly, by imputing the missing data using a maximum likelihood function. Five machine learning algorithms were used: logistic regression, boosted trees, random forests, support vector machines, and decision trees. The models were built using Python-based libraries and trained and tested using sklearn and pandas librairies. The algorithms were trained and tested using the two data sets (deletion and imputation cleaning methods). The matplotlib library was used to graph the ROC curves, including the area under the curve.

The machine learning models were all able to predict the DSIC grade. Of all the models, the support vector machine model performed best, achieving an area under the curve score of 0.82. This model achieved an accuracy of 63% and an F1 score of 0.58. Between the two data cleaning methods, the imputation method was better, achieving higher areas under the curve than the deletion method. The accuracy, recall, precision, and F1 scores were similar for both data cleaning methods.

The machine learning models were able to effectively predict physician decision making and score patients based on the DSIC scheme. The support vector machine model was able to achieve an area under the curve of 0.82 in comparison to physician classification. Since the data set was relatively small, the results could be improved with training on a larger data set. The use of machine learning models in DLS classification could prove to be an efficient approach to reduce human bias and error. Further efforts are necessary to test the inter- and intra-observer reliability of the DSIC scheme, as well as to determine if the surgeons using the scheme are following DLS treatment recommendations.