A predictive model for the development of chronic obstructive pulmonary disease.

The screening of a person at risk for chronic obstructive pulmonary disease (COPD) and timely treatment may provide opportunities to delay the progressive destruction of lung function. Therefore, a model to predict the disease is required. We hypothesized that demographic and clinical information in combination with genetic markers would aid in the prediction of COPD development, prior to its onset. The aim of the present study was to create a predictive model for COPD development. Demographic, clinical presentation and genetic polymorphisms were recorded in COPD patients and control subjects. Nighty-six single-nucleotide polymorphisms of 46 genes were selected for genotyping in the case-control study. A predictive model was produced using logistic regression with a stepwise model-building approach and was validated. A total of 331 patients and 351 control subjects were included. The logistic regression identified the following predictors: Gender, respiratory infection in early life, low birth weight, smoking history and genotype polymorphisms (rs2070600, rs10947233, rs1800629, rs2241712 and rs1205). The model was established using the following formula: COPD = 1/[1 + exp (-2.4933-1.2197 gender + 1.1842 respiratory infection in early life + 2.4350 low birth weight + 1.8524 smoking - 1.1978 rs2070600 + 2.0270 rs10947233 + 1.1913 rs10947233 + 0.6468 rs1800629 + 0.5272 rs2241712 + 0.4024 rs1205)] (when the value is >0.5). The Hosmer-Lemeshow test showed no significant deviations between the observed and predicted events. Validation of the model in 50 patients showed a modest sensitivity and specificity. Therefore, a predictive model based on demographic, clinical and genetic information may identify COPD prior to its onset.