OBJECTIVE: To explore the correlations between diagnostic information and therapeutic efficacy in rheumatoid arthritis (RA) with decision tree model analysis. METHODS:Three hundred and ninety seven patients came from 9 clinical centers were randomly divided into the Western medicine (WM) group (n=194) treated with non-steroidal anti-inflammatory drugs and slow-acting antirheumatic drug and the Chinese medicine (CM) group (n=203) with basic therapy and syndrome-differentiation dependant TCM treatment. TCM and WM diagnostic information were collected. The ACR 20 was used for efficacy evaluation and the information of patients before treatment was analyzed by SAS 8.2 statistical package. Through single-factor exploratory analysis, odds ratio of efficacy and variable was calculated taken P < 0.2 as the including criteria for data mining analysis with decision tree model. All data were classified into the training set (75%) and verifying set (25%) with efficacy as the variable for layering to make further verification of the data-mining analysis. RESULTS:Twenty variables were included in the CM group and 26 in the WM group in the data-mining model. In the former, 9 variables were positively correlated to the efficacy, including degree of arthralgia, tenderness and morning stiffness, number of swollen joint, and joint with tenderness, levels of IgM, rheumatoid factor (RF), C-reactive protein (CRP), and total assessment from doctor; and disease duration and degree of nocturnal polyuria were negatively correlated to that. While in the latter, 8 were positively correlated to the efficacy, including erythrocyte sedimentation rate (ESR), sour and weak waist and knees, white fur in tongue, joint ache and stiffness, swollen joint, and total assessment from doctor and patient, and red tongue with yellow fur and leucocyte count negatively correlated to it. Data mining with decision tree analysis revealed that different combinations of morning stiffness, slight red tongue, joint tenderness and nocturnal polyuria in the CM group, and those of white fur in tongue, CRP level, leucocyte count and morning stiffness in the WM group showed different efficacy, which were also verified in the randomly chosen verifying set. CONCLUSION: To analyze the correlations between diagnostic information and therapeutic efficacy with decision tree analysis is conformed to the theory of TCM in applying treatment according to syndrome differentiation individually, thus it would contribute to elevate the accuracy of therapy.
RCT Entities:
OBJECTIVE: To explore the correlations between diagnostic information and therapeutic efficacy in rheumatoid arthritis (RA) with decision tree model analysis. METHODS: Three hundred and ninety seven patients came from 9 clinical centers were randomly divided into the Western medicine (WM) group (n=194) treated with non-steroidal anti-inflammatory drugs and slow-acting antirheumatic drug and the Chinese medicine (CM) group (n=203) with basic therapy and syndrome-differentiation dependant TCM treatment. TCM and WM diagnostic information were collected. The ACR 20 was used for efficacy evaluation and the information of patients before treatment was analyzed by SAS 8.2 statistical package. Through single-factor exploratory analysis, odds ratio of efficacy and variable was calculated taken P < 0.2 as the including criteria for data mining analysis with decision tree model. All data were classified into the training set (75%) and verifying set (25%) with efficacy as the variable for layering to make further verification of the data-mining analysis. RESULTS: Twenty variables were included in the CM group and 26 in the WM group in the data-mining model. In the former, 9 variables were positively correlated to the efficacy, including degree of arthralgia, tenderness and morning stiffness, number of swollen joint, and joint with tenderness, levels of IgM, rheumatoid factor (RF), C-reactive protein (CRP), and total assessment from doctor; and disease duration and degree of nocturnal polyuria were negatively correlated to that. While in the latter, 8 were positively correlated to the efficacy, including erythrocyte sedimentation rate (ESR), sour and weak waist and knees, white fur in tongue, joint ache and stiffness, swollen joint, and total assessment from doctor and patient, and red tongue with yellow fur and leucocyte count negatively correlated to it. Data mining with decision tree analysis revealed that different combinations of morning stiffness, slight red tongue, joint tenderness and nocturnal polyuria in the CM group, and those of white fur in tongue, CRP level, leucocyte count and morning stiffness in the WM group showed different efficacy, which were also verified in the randomly chosen verifying set. CONCLUSION: To analyze the correlations between diagnostic information and therapeutic efficacy with decision tree analysis is conformed to the theory of TCM in applying treatment according to syndrome differentiation individually, thus it would contribute to elevate the accuracy of therapy.