Background Over five million joint replacements are performed across the world each year. Cobalt chrome (CoCr) components are used in most of these procedures. Some patients develop delayed-type hypersensitivity (DTH) responses to CoCr implants, resulting in tissue damage and revision surgery. DTH is unpredictable and genetic links have yet to be definitively established. Methods At a single site, we carried out an initial investigation to identify HLA alleles associated with development of DTH following metal-on-metal hip arthroplasty. We then recruited patients from other centres to train and validate an algorithm incorporating patient age, gender, HLA genotype, and blood metal concentrations to predict the development of DTH. Accuracy of the modelling was assessed using performance metrics including time-dependent receiver operator curves. Results Using next-generation sequencing, here we determine the HLA genotypes of 606 patients. 176 of these patients had experienced failure of their prostheses; the remaining 430 remain asymptomatic at a mean follow up of twelve years. We demonstrate that the development of DTH is associated with patient age, gender, the magnitude of metal exposure, and the presence of certain HLA class II alleles. We show that the predictive algorithm developed from this investigation performs to an accuracy suitable for clinical use, with weighted mean survival probability errors of 1.8% and 3.1% for pre-operative and post-operative models respectively. Conclusions The development of DTH following joint replacement appears to be determined by the interaction between implant wear and a patient’s genotype. The algorithm described in this paper may improve implant selection and help direct patient surveillance following surgery. Further consideration should be given towards understanding patient-specific responses to different biomaterials.

Aims Traditionally, acetabular component insertion during total hip arthroplasty (THA) is visually assisted in the posterior approach and fluoroscopically assisted in the anterior approach. The present study examined the accuracy of a new surgeon during anterior (NSA) and posterior (NSP) THA using robotic arm-assisted technology compared to two experienced surgeons using traditional methods. Methods Prospectively collected data was reviewed for 120 patients at two institutions. Data were collected on the first 30 anterior approach and the first 30 posterior approach surgeries performed by a newly graduated arthroplasty surgeon (all using robotic arm-assisted technology) and was compared to standard THA by an experienced anterior (SSA) and posterior surgeon (SSP). Acetabular component inclination, version, and leg length were calculated postoperatively and differences calculated based on postoperative film measurement. Results Demographic data were similar between groups with the exception of BMI being lower in the NSA group (27.98 vs 25.2; p = 0.005). Operating time and total time in operating room (TTOR) was lower in the SSA (p < 0.001) and TTOR was higher in the NSP group (p = 0.014). Planned versus postoperative leg length discrepancy were similar among both anterior and posterior surgeries (p > 0.104). Planned versus postoperative abduction and anteversion were similar among the NSA and SSA (p > 0.425), whereas planned versus postoperative abduction and anteversion were lower in the NSP (p < 0.001). Outliers > 10 mm from planned leg length were present in one case of the SSP and NSP, with none in the anterior groups. There were no outliers > 10° in anterior or posterior for abduction in all surgeons. The SSP had six outliers > 10° in anteversion while the NSP had none (p = 0.004); the SSA had no outliers for anteversion while the NSA had one (p = 0.500). Conclusion Robotic arm-assisted technology allowed a newly trained surgeon to produce similarly accurate results and outcomes as experienced surgeons in anterior and posterior hip arthroplasty. Cite this article: Bone Jt Open 2021;2(6):365–370.

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Joint replacement surgery provides pain relief and restoration of mobility for millions of patients around the world each year.[1] However, the release of wear debris from implant surfaces can limit the lifespan of a prosthesis through the promotion of inflammatory responses.[2] Implants must therefore be constructed from materials with sufficient durability and biocompatibility. One such material is cobalt chrome alloy, which is used in the majority of joint replacements.[3] Unfortunately, it is recognised that some patients develop lymphocyte mediated delayed type hypersensitivity (DTH) responses to this material,[4] a response which may result in extensive bone and soft tissue destruction.[5] A genetic predisposition to DTH has been proposed[6], though specific genes have yet to be identified, or the effects quantified. Here we show that variation in HLA class II genotype influences an individual’s susceptibility to DTH. HLA-DQ haplotypes encoding peptide binding grooves with greater affinity for the N terminal peptide sequence of albumin (containing two recognised metal binding sites) confer a greater risk of DTH. We describe the development and validation of a machine learning algorithm to investigate the possibility that a patient’s genotype and basic clinical parameters may be used to predict DTH. Incorporating this novel finding, gradient boosted survival analysis machine learning models were trained and validated using results from 606 patients from three international units. These models were assessed using Uno’s c-index, time-dependent AUROCs, and integrated calibration index performance statistics. At present, there are no tests in widespread clinical use which use a patient’s genetic profile to guide implant selection or inform post-operative management. The algorithm described herein may address this issue.