[Predictive role of diagnostic information in treatment efficacy of rheumatoid arthritis based on neural network model analysis].
OBJECTIVE
To analyze the indications of the therapies for rheumatoid arthritis (RA) with neural network model analysis.


METHODS
Three hundred and ninety-seven patients were included in the clinical trial from 9 clinical centers. They were randomly divided into Western medicine (WM) treated group, 194 cases; and traditional Chinese herbal medicine (CM) treated group, 203 cases. A complete physical examination and 18 common clinical manifestations were prepared before the randomization and after the treatment. The WM therapy included voltaren extended action tablet, methotrexate and sulfasalazine. The CM therapy included Glucosidorum Tripterygii Totorum Tablet and syndrome differentiation treatment. The American College of Rheumatology 20 (ACR20) was taken as efficacy evaluation. All data were analyzed on SAS 8.2 statistical package. The relationships between each variable and efficacy were analyzed, and the variables with P<0.2 were included for the data mining analysis with neural network model. All data were classified into training set (75%) and verification set (25%) for further verification on the data-mining model.


RESULTS
Eighteen variables in CM and 24 variables in WM were included in the data-mining model. In CM, morning stiffness, swollen joint number, peripheral immunoglobulin M (IgM) level, tenderness joint number, tenderness, rheumatoid factor (RF), C-reactive protein (CRP) and joint pain were positively related to the efficacy, and disease duration and more urination at night negatively related to the efficacy. In WM, erythrocyte sedimentation rate (ESR), weak waist, white fur in tongue, joint pain, joint stiffness and swollen joint were positively related to the efficacy, and yellow fur in tongue, red tongue, white blood negatively related to the efficacy. In the analysis with the neural network model in the patients of verification set, the predictive response rates of 20% patients would be 100% and 90% in the treatment with CM and WM, respectively.


CONCLUSIONS
Neural network model analysis, based on the full clinical trial data with collection of both traditional Chinese medicine and modern medicine diagnostic information, shows a good predictive role for the information in the efficacy in rheumatoid arthritis.
Zha
Qing-lin
QL
National Center of Pharmaceutical Engineering Research, Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi Province 330006, China.
He
Yi-ting
YT
Yan
Xiao-ping
XP
Su
Li
L
Song
Yue-jin
YJ
Zeng
Sheng-ping
SP
Liu
Wei
W
Feng
Xing-hua
XH
Qian
Xian
X
Zhu
Wan-hua
WH
Lin
Se-qi
SQ
Lü
Cheng
C
Lü
Ai-ping
AP
D000328:Adult; D018501:Antirheumatic Agents / Q000627:therapeutic use*; D001172:Arthritis, Rheumatoid / Q000175:diagnosis / Q000188:drug therapy*; D004008:Diclofenac / Q000627:therapeutic use; D004365:Drugs, Chinese Herbal / Q000627:therapeutic use; D005260:Female; D006801:Humans; D008297:Male; D008516:Medicine, Chinese Traditional; D008727:Methotrexate / Q000627:therapeutic use; D008875:Middle Aged; D016571:Neural Networks, Computer; D008517:Phytotherapy; D012460:Sulfasalazine / Q000627:therapeutic use; D016896:Treatment Outcome
D018501:Antirheumatic Agents; D004365:Drugs, Chinese Herbal; D004008:Diclofenac; D012460:Sulfasalazine; D008727:Methotrexate