Axial spondyloarthritis guidelines - aiming for maximum impact.

PURPOSE OF REVIEW: This review discusses international clinical practice guidelines (CPGs) for axial spondyloarthritis (axSpA) focusing on methodology, guideline quality, and implementation. RECENT FINDINGS: The Assessment of SpondyloArthritis International Society/European Alliance of Associations for Rheumatology (ASAS/EULAR) and Pan-American League of Associations for Rheumatology (PANLAR) recently published axSpA CPGs and updates of the American College of Rheumatology/Spondylitis Association of America/Spondyloarthritis Research and Treatment Network (ACR/SAA/SPARTAN) and Asia-Pacific League of Associations for Rheumatology (APLAR) CPGs are expected. GRADE has emerged as the dominant framework for CPG development and has been used by three of the four international axSpA guidelines. Notable differences exist among these guidelines in the way that the recommendations are presented. Two of the four acknowledge the need for implementation strategies, but little detail about this is provided. The few studies that have evaluated the implementation of axSpA CPGs have identified poor adherence to recommendations on physical therapy/exercise and disease activity monitoring. Implementation science has identified many barriers and facilitators affecting guideline uptake, including those related to healthcare professionals and to the guidelines themselves. Creation of a tailored implementation plan simultaneously with the CPG is recommended. SUMMARY: While methodological rigor in the creation of evidence-based recommendations is the focus of CPG development, recommendations must be presented in a user-friendly format that makes them easy to apply. 'Living guidelines' could facilitate keeping content up to date. Implementation is critical for the success of a CPG and should be emphasized in future axSpA guideline updates. Further research is needed to better understand the factors impacting the successful implementation of axSpA CPGs.

Deep Learning-Based Estimation of Implantable Collamer Lens Vault Using Optical Coherence Tomography.

PURPOSE: To develop and validate a deep learning neural network for automated measurement of implantable collamer lens (ICL) vault using anterior segment optical coherence tomography (AS-OCT). DESIGN: Cross-sectional retrospective study. METHODS: A total of 2647 AS-OCT scans were used from 139 eyes of 82 subjects who underwent ICL surgery in 3 different centers. Using transfer learning, a deep learning network was trained and validated for estimating the ICL vault on OCT. A trained operator separately reviewed all OCT scans and measured the central vault using a built-in caliper tool. The model was then separately tested on 191 scans. A Bland-Altman plot was constructed and the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), Pearson correlation coefficient (r), and determination coefficient (R2) were calculated to evaluate the strength and validity of the model. RESULTS: On the test set, the model achieved a MAPE of 3.42%, an MAE of 15.82 µm, a RMSE of 18.85 µm, a Pearson correlation coefficient r of +0.98 (P < .00001), and a coefficient of determination R2 of +0.96. There was no significant difference between the vaults of the test set labeled by the technician vs those estimated by the model: 478 ± 95 µm vs 475 ± 97 µm, respectively, P = .064). CONCLUSIONS: Using transfer learning, our deep learning neural network was able to accurately compute the ICL vault from AS-OCT scans, overcoming the limitations of an imbalanced data set and limited training data. Such an algorithm can assist the postoperative assessment in ICL surgery.