The last two decades have seen remarkable technological advances in total hip arthroplasty (THA) implant design. Porous ingrowth surfaces and highly crosslinked polyethylene (HXLPE) have been expected to dramatically improve implant survivorship. The purpose of the present study was to evaluate survival of contemporary cementless acetabular components following primary THA. 16,421 primary THAs performed for osteoarthritis between 2000 and 2019 were identified from our institutional total joint registry. Patients received one of 12 contemporary cementless acetabular designs with HXLPE liners. Components were grouped based on ingrowth surface into 4 categories: porous titanium (n=10,952, mean follow-up 5 years), porous tantalum (n=1223, mean follow-up 5 years), metal mesh (n=2680, mean follow-up 6.5 years), and hydroxyapatite (HA) coated (n=1566, mean follow-up 2.4 years). Kaplan-Meier analyses were performed to assess the survivorship free of acetabular revision. A historical series of 182 Harris-Galante-1 (HG-1) acetabular components was used as reference. The 15-year survivorship free of acetabular revision was >97% for all 4 contemporary cohorts. Compared to historical control, porous titanium (HR 0.06, 95% CI 0.02–0.17, p<0.001), porous tantalum (HR 0.09, 95%CI 0.03–0.29, p<0.001), metal mesh (HR 0.11, 95%CI 0.04–0.31, p<0.001), and HA-coated (HR 0.14, 95%CI 0.04–0.48, p=0.002) ingrowth surfaces had significantly lower risk of any acetabular revision. There were 16 cases (0.1%) of acetabular aseptic loosening that occurred in 8 (0.07%) porous titanium, 5 (0.2%) metal mesh, and 3 (0.2%) HA-coated acetabular components. 7 of the 8 porous titanium aseptic loosening cases occurred in one known problematic design. There were no cases of aseptic loosening in the porous tantalum group. Modern acetabular ingrowth surfaces and HXLPE liners have improved on historical results at the mid-term. Contemporary designs have extraordinarily high revision-free survivorship, and aseptic loosening is now a rare complication. At mid-term follow-up, survivorship of contemporary uncemented acetabular components is excellent and aseptic loosening occurs in a very small minority of patients.
Instability remains a common complication following total hip arthroplasty (THA) and continues to account for the highest percentage of revisions in numerous registries. Many risk factors have been described, yet a patient-specific risk assessment tool remains elusive. The purpose of this study was to apply a machine learning algorithm to develop a patient-specific risk score capable of dynamic adjustment based on operative decisions. 22,086 THA performed between 1998–2018 were evaluated. 632 THA sustained a postoperative dislocation (2.9%). Patients were robustly characterized based on non-modifiable factors: demographics, THA indication, spinal disease, spine surgery, neurologic disease, connective tissue disease; and modifiable operative decisions: surgical approach, femoral head size, acetabular liner (standard/elevated/constrained/dual-mobility). Models were built with a binary outcome (event/no event) at 1-year and 5-year postoperatively. Inverse Probability Censoring Weighting accounted for censoring bias. An ensemble algorithm was created that included Generalized Linear Model, Generalized Additive Model, Lasso Penalized Regression, Kernel-Based Support Vector Machines, Random Forest and Optimized Gradient Boosting Machine. Convex combination of weights minimized the negative binomial log-likelihood loss function. Ten-fold cross-validation accounted for the rarity of dislocation events.Introduction
Methods
Manual chart review is labor-intensive and requires specialized knowledge possessed by highly-trained medical professionals. The cost and infrastructure challenges required to implement this is prohibitive for most hospitals. Natural language processing (NLP) tools are distinctive in their ability to extract critical information from raw text in the electronic health records (EHR). As a simple proof-of-concept, for the potential application of this technology, we examined its ability to discriminate between a binary classification (periprosthetic fracture [PPFFx] vs. no PPFFx) followed by a more complex classification of the same problem (Vancouver). PPFFx were identified among all THAs performed at a single academic institution between 1977 and 2015. A training cohort (n = 90 PPFFx) selected randomly by an electronic program was utilized to develop a prototype NLP algorithm and an additional randomly-selected 86 PPFFx were used to further validate the algorithm. Keywords to identify, and subsequently classify, Vancouver type PPFFx about THA were defined. The algorithm was applied to consult and operative notes to evaluate language used by surgeons as a means to predict the correct pathology in the absence of a listed, precise diagnosis (e.g. Vancouver B2). Validation statistics were calculated using manual chart review as the gold standard.Introduction
Methods
