Predicting progressive glaucomatous optic neuropathy using baseline standard automated perimetry data.

Baseline results from standard automated perimetry (SAP) have been used to quantify the risk of conversion to glaucoma in individuals with ocular hypertension (OH) and the risk of progression in patients with early glaucoma (EG).1,2 In previous investigations of this predictive capacity, visual field data in terms of global indices were used, such as mean deviation (MD), pattern or corrected pattern SD (PSD/CPSD) or the glaucoma hemifield test (GHT).3 Other studies have used scoring systems that also condense visual field data into a single value, such as the AGIS (Advanced Glaucoma Intervention Study) and CIGTS (Collaborative Initial Glaucoma Treatment Study).4–6 This report describes the application of a statistical machine learning technique, called classification and regression tree (CART) analysis,7 to a longitudinal dataset collected from patients with high-risk OH or EG. The principal goal was to use data from a baseline SAP examination, together with common clinical and demographic variables, to predict which patients would go on to exhibit progressive glaucomatous optic neuropathy (pGON). In addition, we addressed the hypothesis that certain locations and patterns of threshold findings within the visual field convey greater predictive information for pGON than do other locations. We used all visual field test locations individually and avoided using indices that condense the data contained in a visual field to a single number. Breiman et al.7 first described CART as a flexible, nonparametric, data-mining tool that, unlike traditional statistical models, makes few assumptions about the distribution of the underlying data. For example, it deals well with independent variables of mixed type that are correlated, high-dimensional, and inhomogeneous. CART analysis produces decision rules arranged in a tree structure that are relatively simple to interpret for nonstatisticians. CART also performs well with some missing data by exploiting correlation between independent variables, so long as the data are missing at random. CART relies on recursive binary partitioning of a dataset based on several independent variables, or inputs, as they are called in machine learning. During the process, the dataset is successively split into smaller and smaller subsets. The purpose is to produce a set of decision rules applied to the inputs that can divide the data with respect to the classification variable (i.e., separate those who display pGON from those who do not). If the inputs contain data that were collected at an earlier time point than the classification variable, then a truly predictive model is produced. In an early example of this technique in the literature, vital signs and laboratory results at hospital admittance were used to predict the mortality risk of patients with acutely decompensated heart failure.8 Forms of CART analysis have already been applied to ocular findings, including data from patients with glaucoma. For example, CART has been applied to results from confocal scanning laser ophthalmoscopy to assist in classifying patients as normal or glaucomatous.9–13 Conceptually, the process can be thought of as growing a tree where the trunk (entire cohort) is repeatedly split into two branches. Each new case (eye) follows a path along the decision tree, with the direction taken at each branch determined by questions applied to the inputs. An optimal set of inputs, with appropriate cutoffs applied to them, is selected to minimize the number of misclassified cases. The operator has the option of weighting the importance of different misclassification events, for example placing more importance on misclassifying progressing cases versus misclassifying nonprogressing cases, which has the effect of pushing the decision tree toward higher sensitivity or higher specificity. The purpose of the present study is to test the hypothesis that specific locations and patterns of threshold findings within the visual field have predictive value for progressive glaucomatous optic neuropathy pGON. This approach may allow useful information to be extracted that is normally ignored.