Predictive Model for Earlier Diagnosis of Suspected Age-Related Macular Degeneration Patients

Abstract

The primary goal of tailored medicine is to presymptomatically identify individuals at high risk for disease using information of each individual's genetic profile and collection of environmental risk factors. Recently, algorithms were given the strong recognition of several replicated risk factors for age-related macular degeneration (AMD), this distant goal is beginning to seem less mysterious. The purpose of the study was to develop a statistical model for AMD. This study includes total 106 subjects. To identify the risk of earlier diagnosis of suspected AMD patients, 22 independent variables were included in the study. Forward stepwise (likelihood ratio) binary logistic regression has been used to find significant variables associated with the risk of AMD. Prediction equation, based on significant risk factors, and model authenticity have been developed. Hosmer-Lemeshow goodness of fit statistic (χ(2)=0.143, df=8, p=1.0), which is nonsignificant, indicates the appropriateness of the logistic regression model to predict AMD. After going through stepwise logistic regression, only 6 variables out of the 22 independent variables, namely, serum complement factor H (CFH), serum chemokine (C-C motif) ligand 2 (CCL2), serum superoxide dismutase 1 (SOD1), polymorphism in CCL2 (rs4586), stress, and comorbidity were found to be significant (p<0.05). The binary logistic regression model is an appropriate tool to predict AMD in the presence of serum CFH, serum CCL2, serum SOD1, polymorphism in CCL2 (rs4586), stress, and comorbidity with high specificity and sensitivity. The area under the receiver operating characteristic curve (0.909, p=0.001) with less standard error of 0.034 and close 95% confidence intervals (0.842-0.976) further validates the model.