A Logistic Equation to Determine the Validity of Tramadol From Related Gene Polymorphisms and Psychological Factors

Abstract

This study was performed to develop an algorithm using polymorphisms of CYP2D6, p-gp, OPRM1, COMT and psychological variables to predict tramadol response in Chinese patients recovering from upper limb fracture internal fixation surgery. A total of 250 Han Chinese patients recovering from fracture in the upper limb were enrolled. CYP2D6*10, p-gp G2677T, p-gp C3435T, OPRM1 A118G and COMT Val158Met were detected by the ligase detection reaction (LDR) method. The algorithm was developed with binary logistic regression in cohort 1 (200 patients) and assessed with Wilcoxon signed-rank test in cohort 2 (50 patients). According to cohort 1, the predictive equation was calculated with the following logistic regression parameters: Logit (1) = 2.304-4.841 × (anxiety I) - 23.709 × (anxiety II) + 2.823 × (p-gp 3435CT) + 5.737 × (p-gp 3435 TT) - 1.586 × (CYP2D6*10 CT) - 4.542 × (CYP2D6*10 TT). The cutoff point for the prediction was defined as a probability value ≥0.5. The equation's positive predictive value is 90%. When applied to a new sample, the equation's positive predictive value is 86%. The Nagelkerke R² of the model is 0.819, the results of the Hosmer and Leme test show a value of 0.981. The nonparametric correlations between predicted and observed response showed significant correlation (coefficient = 0.879; p < 0.001). The algorithm we have developed might predict tramadol response in Chinese upper limb fracture patients.