Abstract
The purpose of this study was to evaluate the performance of machine learning algorithms to predict trabeculectomy surgical outcomes. Preoperative systemic, demographic and ocular data from consecutive trabeculectomy surgeries from a single academic institution between January 2014 and December 2018 were incorporated into models using random forest, support vector machine, artificial neural networks and multivariable logistic regression. Mean area under the receiver operating characteristic curve (AUC) and accuracy were used to evaluate the discrimination of each model to predict complete success of trabeculectomy surgery at 1 year. The top performing model was optimized using recursive feature selection and hyperparameter tuning. Calibration and net benefit of the final models were assessed. Among the 230 trabeculectomy surgeries performed on 184 patients, 104 (45.2%) were classified as complete success. Random forest was found to be the top performing model with an accuracy of 0.68 and AUC of 0.74 using 5-fold cross-validation to evaluate the final optimized model. These results provide evidence that machine learning models offer value in predicting trabeculectomy outcomes in patients with refractory glaucoma.
Highlights
The purpose of this study was to evaluate the performance of machine learning algorithms to predict trabeculectomy surgical outcomes
Incisional surgeries are often necessary for patients with refractory glaucoma who are at high risk for progressive vision loss
A total of 35 preoperative parameters were collected for model input consisting of 3 demographic parameters, 15 parameters from systemic health data and 17 ocular parameters
Summary
The purpose of this study was to evaluate the performance of machine learning algorithms to predict trabeculectomy surgical outcomes. Random forest was found to be the top performing model with an accuracy of 0.68 and AUC of 0.74 using 5-fold cross-validation to evaluate the final optimized model These results provide evidence that machine learning models offer value in predicting trabeculectomy outcomes in patients with refractory glaucoma. Yoo et al found machine learning algorithms statistically superior to classic clinical methods for predicting the complication of corneal ectasia following refractive s urgery10 Their random forest model had the highest prediction performance of the commonly used machine learning algorithms, with an area under the receiver operating characteristic curve (AUC) of 0.967 on an external validation set. Merali et al applied machine learning to predict quality of life metrics following surgery to treat degenerative cervical m yelopathy12 Their best performing model utilized a random forest algorithm incorporating neurological exam findings and systemic comorbidities to predict quality of life scores with an AUC of 0.71 at 1 year. The objective of this study was to evaluate machine learning models in their ability to predict real world trabeculectomy outcomes using readily available preoperative patient demographic, ocular and systemic health data
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