A novel method predicting clinical response using only background clinical data in RA patients before treatment with infliximab

Abstract

Objectives: The aim of the present study was to generate a novel method for predicting the clinical response to infliximab (IFX), using a machine-learning algorithm with only clinical data obtained before the treatment in rheumatoid arthritis (RA) patients.

Methods: We obtained 32 variables out of the clinical data on the patients from two independent hospitals. Next, we selected both clinical parameters and machine-learning algorithms and decided the candidates of prediction method. These candidates were verified by clinical variables on different patients from two other hospitals. Finally, we decided the prediction method to achieve the highest score.

Results: The combination of multilayer perceptron algorithm (neural network) and nine clinical parameters shows the best accuracy performance. This method could predict the good or moderate response to IFX with 92% accuracy. The sensitivity of this method was 96.7%, while the specificity was 75%.

Conclusions: We have developed a novel method for predicting the clinical response using only background clinical data in RA patients before treatment with IFX. Our method for predicting the response to IFX in RA patients may have advantages over the other previous methods in several points including easy usability, cost-effectiveness and accuracy.

Keywords:

• Clinical data
• Infliximab
• Machine-learning
• Rheumatoid arthritis
• The prediction of clinical response

Acknowledgments

The authors acknowledge Dr K. Sato for his critical reading of this article.

Conflict of interest

S.M. has received honoraria from Eisai Pharmaceutical, Takeda Pharmaceutical, Asahi Kasei, Santen Pharmaceutical, Mitsubishi-Tanabe Pharma, Pfizer, Astellas Pharmaceutical, Daiichi Sankyo, Abbvie, Actelion, Bristol Myers, Eli Lilly, Ono Pharmaceutical and GlaxoSmithKline, and unlimited research funds from Takeda Pharmaceutical, Mitsubishi-Tanabe Pharma, Asahi Kasei, Shionogi, Astellas Pharmaceutical, Teijin, Ono Pharmaceutical, Pfizer, Eisai, Abbvie, Chugai Pharmaceutical and MSD, and royalty from Chugai Pharmaceutical. T.M. has received research grants from Abbvie, Bristol Myers Squibb, Chugai Pharmaceutical, Eisai Pharmaceutical, Mitsubishi-Tanabe Pharma, Takeda Pharmaceutical, Astellas Pharmaceutical and Pfizer and received lecture fees from Chugai Pharmaceutical, Janssen Pharmaceutical, Mitsubishi-Tanabe Pharma, and Takeda Pharmaceutical. All other authors have declared no conflicts of interest. F.M. has become an employee of Mitsubishi-Tanabe Pharma after this study finished.

Supplementary material available online