Aims. To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports. Methods. Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation. Results. For THA, there were 5,558 patient radiology reports included, of which 4,137 were used for model training and testing, and 1,421 for external validation. Following training, model performance demonstrated average (mean across three folds) accuracy, F1 score, and area under the receiver operating curve (AUROC) values of 0.850 (95% confidence interval (CI) 0.833 to 0.867), 0.813 (95% CI 0.785 to 0.841), and 0.847 (95% CI 0.822 to 0.872), respectively. For TKA, 7,457 patient radiology reports were included, with 3,478 used for model training and testing, and 3,152 for external validation. Performance metrics included accuracy, F1 score, and AUROC values of 0.757 (95% CI 0.702 to 0.811), 0.543 (95% CI 0.479 to 0.607), and 0.717 (95% CI 0.657 to 0.778) respectively. There was a notable deterioration in performance on external validation in both cohorts. Conclusion. The use of routinely available preoperative radiology reports provides promising potential to help screen suitable candidates for THA, but not for TKA. The external validation results demonstrate the importance of further model testing and training when confronted with new clinical cohorts. Cite this article: Bone Joint J 2024;106-B(7):688–695

Aims

While internet search engines have been the primary information source for patients’ questions, artificial intelligence large language models like ChatGPT are trending towards becoming the new primary source. The purpose of this study was to determine if ChatGPT can answer patient questions about total hip (THA) and knee arthroplasty (TKA) with consistent accuracy, comprehensiveness, and easy readability.

Aims. The aim of this study was to determine the prevalence of depressive and anxiety disorders prior to total hip (THA) and total knee arthroplasty (TKA) and to assess their impact on the rates of any infection, revision, or reoperation. Methods. Between January 2000 and March 2019, 21,469 primary and revision arthroplasties (10,011 THAs; 11,458 TKAs), which were undertaken in 15,504 patients at a single academic medical centre, were identified from a 27-county linked electronic medical record (EMR) system. Depressive and anxiety disorders were identified by diagnoses in the EMR or by using a natural language processing program with subsequent validation from review of the medical records. Patients with mental health diagnoses other than anxiety or depression were excluded. Results. Depressive and/or anxiety disorders were common before THA and TKA, with a prevalence of 30% in those who underwent primary THA, 33% in those who underwent revision THA, 32% in those who underwent primary TKA, and 35% in those who underwent revision TKA. The presence of depressive or anxiety disorders was associated with a significantly increased risk of any infection (primary THA, hazard ratio (HR) 1.5; revision THA, HR 1.9; primary TKA, HR 1.6; revision TKA, HR 1.8), revision (THA, HR 1.7; TKA, HR 1.6), re-revision (THA, HR 2.0; TKA, HR 1.6), and reoperation (primary THA, HR 1.6; revision THA, HR 2.2; primary TKA, HR 1.4; revision TKA, HR 1.9; p < 0.03 for all). Patients with preoperative depressive and/or anxiety disorders were significantly less likely to report “much better” joint function after primary THA (78% vs 87%) and primary TKA (86% vs 90%) compared with those without these disorders at two years postoperatively (p < 0.001 for all). Conclusion. The presence of depressive or anxiety disorders prior to primary or revision THA and TKA is common, and associated with a significantly higher risk of infection, revision, reoperation, and dissatisfaction. This topic deserves further study, and surgeons may consider mental health optimization to be of similar importance to preoperative variables such as diabetic control, prior to arthroplasty. Cite this article: Bone Joint J 2023;105-B(5):526–533