This retrospective study constructs the largest multicenter registry of strabismus care in Japan. The registry evaluates real-world surgical and conservative treatment outcomes, safety, and natural history for various types of strabismus, including common, rare, and complex cases.
This retrospective multicenter cohort study collects clinical data from participating academic and high-volume centers in Japan. To minimize chronological bias and maintain feasibility, the observation period depends on the target disease. For common types of strabismus, data extraction is limited to a recent timeframe (e.g., the past 3 years) to avoid the influence of behavioral changes during the coronavirus disease 2019 (COVID-19) pandemic. For rare and complex strabismus, the extraction period extends up to 10 years (from January 2016 to the date of Institutional Review Board approval) to secure an adequate sample size. Statistical analyses will incorporate mixed-effects models to adjust for institutional clustering and differences in patient backgrounds. Missing data will be addressed via multiple imputation before developing prediction models with machine learning algorithms such as random forests.
Study Type
OBSERVATIONAL
Enrollment
10,000
Surgical correction of ocular deviation.
Non-surgical management, including clinical observation for natural history, prism therapy, or orthoptic treatment.
Kyoto University Graduate School of Medicine
Kyoto, Kyoto, Japan
Success rate of ocular deviation correction and residual deviation angle
Assess the angle of ocular deviation and determine the success rate of clinical management (including observation for natural history, surgical, or conservative treatments) based on established clinical criteria.
Time frame: At standard observation intervals (2 to 4 weeks, 3 to 6 months, 1 year, and 2 years post-treatment or baseline) and through study completion, up to 10 years.
Recovery of binocular visual function
Evaluate binocularity using standardized clinical tests.
Time frame: Through study completion, up to 10 years
Predictive accuracy of the machine learning nomogram
Compare the predictive performance of the developed machine learning models against actual surgical outcomes.
Time frame: Through study completion, up to 10 years
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.