Osteoporosis is common in total hip arthroplasty (THA) patients. It plays a substantial factor in the surgery’s outcome, and previous studies have revealed that pharmacological treatment for osteoporosis influences implant survival rate. The purpose of this study was to examine the prevalence of and treatment rates for osteoporosis prior to THA, and to explore differences in osteoporosis-related biomarkers between patients treated and untreated for osteoporosis. This single-centre retrospective study included 398 hip joints of patients who underwent THA. Using medical records, we examined preoperative bone mineral density measures of the hip and lumbar spine using dual energy X-ray absorptiometry (DXA) scans and the medications used to treat osteoporosis at the time of admission. We also assessed the following osteoporosis-related biomarkers: tartrate-resistant acid phosphatase 5b (TRACP-5b); total procollagen type 1 amino-terminal propeptide (total P1NP); intact parathyroid hormone; and homocysteine.Aims
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The Patient-Reported Outcomes Measurement Information System (PROMIS) has demonstrated faster administration, lower burden of data capture and reduced floor and ceiling effects compared to traditional Patient Reported Outcomes Measurements (PROMs). We investigated the suitability of PROMIS Mobility score in assessing physical function in the sequelae of childhood hip disease. In all, 266 adolscents (aged ≥ 12 years) and adults were identified with a prior diagnosis of childhood hip disease (either Perthes’ disease (n = 232 (87.2%)) or Slipped Capital Femoral Epiphysis (n = 34 (12.8%)) with a mean age of 27.73 years (SD 12.24). Participants completed the PROMIS Mobility Computer Adaptive Test, the Non-Arthritic Hip Score (NAHS), EuroQol five-dimension five-level questionnaire, and the Numeric Pain Rating Scale. We investigated the correlation between the PROMIS Mobility and other tools to assess use in this population and any clustering of outcome scores.Aims
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This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images. The study analyzed 978 pairs of hip CT and dual-energy X-ray absorptiometry (DXA) measurements of the proximal femur (DXA-BMD) collected from three institutions. From the CT images, the femur and a calibration phantom were automatically segmented using previously trained deep-learning models. The Hounsfield units of each voxel were converted into density (mg/cm3). Then, a deep-learning model trained by manual landmark selection of 315 cases was developed to select the landmarks at the proximal femur to rotate the CT volume to the neutral position. Finally, the CT volume of the femur was projected onto the coronal plane, and the areal BMD of the proximal femur (CT-aBMD) was quantified. CT-aBMD correlated to DXA-BMD, and a receiver operating characteristic (ROC) analysis quantified the accuracy in diagnosing osteoporosis.Aims
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Total hip arthroplasty (THA) is a common procedure to address pain and enhance function in hip disorders such as osteoarthritis. Despite its success, postoperative patient recovery exhibits considerable heterogeneity. This study aimed to investigate whether patients follow distinct pain trajectories following THA and identify the patient characteristics linked to suboptimal trajectories. This retrospective cohort study analyzed THA patients at a large academic centre (NYU Langone Orthopedic Hospital, New York, USA) from January 2018 to January 2023, who completed the Patient-Reported Outcomes Measurement Information System (PROMIS) pain intensity questionnaires, collected preoperatively at one-, three-, six-, 12-, and 24-month follow-up times. Growth mixture modelling (GMM) was used to model the trajectories. Optimal model fit was determined by Bayesian information criterion (BIC), Vuong-Lo-Mendell-Rubin likelihood ratio test (VLMR-LRT), posterior probabilities, and entropy values. Association between trajectory groups and patient characteristics were measured by multinomial logistic regression using the three-step approach.Aims
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