PloS one · 2022
Calculating the individual probability of successful ocriplasmin treatment in eyes with vitreomacular traction-Validation and refinement of a multivariable prediction model.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Paul Christoph, Müller Hans-Helge, Raber Thomas, Bertelmann Thomas, EXPORT study group
Аннотация
PURPOSE: To evaluate a multivariable model predicting the individual probability of successful intravitreal ocriplasmin (IVO) treatment in eyes with vitreomacular traction (VMT).
METHODS: Data from three prospective, multicenter IVO studies (OASIS, ORBIT, and INJECT) were pooled. Patients were included if they were treated for a symptomatic VMT without a full-thickness macular hole. A prediction model for VMT resolution using the factors 'age' and 'horizontal VMT diameter' was validated by receiver operating characteristic analysis and according to grouped prediction after calibration. Multivariable regression analysis was performed to check robustness and explore further improvements.
RESULTS: Data from 591 eyes was included. In the univariate analysis all key factors (age, gender, VMT diameter, lens status, ERM) significantly correlated to treatment success. The prediction model was robust and clinically applicable to estimate the success rate of IVO treatment (AUC of ROC: 0.70). A refinement of the model was achieved through a calibration process.
CONCLUSION: The developed multivariable model using 'horizontal VMT diameter' and 'age' is a valid tool for prediction of VMT resolution upon IVO treatment.
METHODS: Data from three prospective, multicenter IVO studies (OASIS, ORBIT, and INJECT) were pooled. Patients were included if they were treated for a symptomatic VMT without a full-thickness macular hole. A prediction model for VMT resolution using the factors 'age' and 'horizontal VMT diameter' was validated by receiver operating characteristic analysis and according to grouped prediction after calibration. Multivariable regression analysis was performed to check robustness and explore further improvements.
RESULTS: Data from 591 eyes was included. In the univariate analysis all key factors (age, gender, VMT diameter, lens status, ERM) significantly correlated to treatment success. The prediction model was robust and clinically applicable to estimate the success rate of IVO treatment (AUC of ROC: 0.70). A refinement of the model was achieved through a calibration process.
CONCLUSION: The developed multivariable model using 'horizontal VMT diameter' and 'age' is a valid tool for prediction of VMT resolution upon IVO treatment.