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Predicting acupuncture efficacy for functional dyspepsia based on routine clinical features: a machine learning study in the framework of predictive, preventive, and personalized medicine

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Abstract

Background

Acupuncture is safe and effective for functional dyspepsia (FD), while its efficacy varies among individuals. Predicting the response of different FD patients to acupuncture treatment in advance and therefore administering the tailored treatment to the individual is consistent with the principle of predictive, preventive, and personalized medicine (PPPM/3PM). In the current study, the individual efficacy prediction models were developed based on the support vector machine (SVM) algorithm and routine clinical features, aiming to predict the efficacy of acupuncture in treating FD and identify the FD patients who were appropriate to acupuncture treatment.

Methods

A total of 745 FD patients were collected from two clinical trials. All the patients received a 4-week acupuncture treatment. Based on the demographic and baseline clinical features of 80% of patients in trial 1, the SVM models were established to predict the acupuncture response and improvements of symptoms and quality of life (QoL) at the end of treatment. Then, the left 20% of patients in trial 1 and 193 patients in trial 2 were respectively applied to evaluate the internal and external generalizations of these models.

Results

These models could predict the efficacy of acupuncture successfully. In the internal test set, models achieved an accuracy of 0.773 in predicting acupuncture response and an R2 of 0.446 and 0.413 in the prediction of QoL and symptoms improvements, respectively. Additionally, these models had well generalization in the independent validation set and could also predict, to a certain extent, the long-term efficacy of acupuncture at the 12-week follow-up. The gender, subtype of disease, and education level were finally identified as the critical predicting features.

Conclusion

Based on the SVM algorithm and routine clinical features, this study established the models to predict acupuncture efficacy for FD patients. The prediction models developed accordingly are promising to assist doctors in judging patients’ responses to acupuncture in advance, so that they could tailor and adjust acupuncture treatment plans for different patients in a prospective rather than the reactive manner, which could greatly improve the clinical efficacy of acupuncture treatment for FD and save medical expenditures.

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Code availability

The prediction models (Matlab code) generated in this study can be found on our Github homepage: https://github.com/YinTao0828/FD_ACU_Predicition.git. Reasonable requests for the original data can be sent to the corresponding author.

Abbreviations

AUC :

Area under the receiver operating characteristic curve

CAM :

Complementary and alternative medicine

EPS :

Epigastric pain syndrome

FD :

Functional dyspepsia

GLM :

Generalized linear model

ML :

Machine learning

MSE :

Mean squared error

NDSI :

Nepean Dyspepsia Symptom Index

NDLQI :

Nepean Dyspepsia Life Quality Index

PDS :

Postprandial distress syndrome

PPPM/3PM :

Predictive, preventive, and personalized medicine

QoL :

Quality of life

R 2 :

Coefficient of determination

SVC :

Support vector classification

SVM :

Support vector machine

SVR :

Support vector regression

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Acknowledgements

We thank Yangke Mao of Chengdu University of Traditional Chinese Medicine for language editing.

Funding

The study was financially supported by the National Natural Science Foundation of China (No. 81973960, 81622052, 81473602), the National Basic Research Program of China (973 Program) (No. 2012CB518500, 2012CB518501), and the Sichuan Science and Technology Program (No. 2019JDTD0011).

National Basic Research Program of China (973 Program), No. 2012CB518500, Fanrong Liang, 2012CB518501, Fanrong Liang, National Natural Science Foundation of China, No. 81973960, Fang Zeng, 81622052, Fang Zeng, 81473602, Fang Zeng, Sichuan Science and Technology Program, No. 2019JDTD0011, Fang Zeng

Author information

Author notes

  1. Tao Yin, Hui Zheng and Tingting Ma contributed equally.

Authors and Affiliations

  1. Acupuncture and Tuina School/The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, Sichuan, China

    Tao Yin, Hui Zheng, Xiaoping Tian, Jing Xu, Lei Lan, Ruirui Sun, Yong Tang, Fanrong Liang & Fang Zeng

  2. Acupuncture-Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, Sichuan, China

    Tao Yin, Lei Lan, Ruirui Sun & Fang Zeng

  3. Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, Sichuan, China

    Tingting Ma

  4. Graduate School, Chengdu University of Traditional Chinese Medicine, Chengdu, 610075, Sichuan, China

    Ying Li

  5. Acupuncture and Tuina School, Hunan University of Chinese Medicine, Changsha, 410208, Hunan, China

    Mailan Liu

  6. Key Laboratory of Sichuan Province for Acupuncture and Chronobiology, Chengdu, 610075, Sichuan, China

    Yong Tang

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Contributions

Tao Yin: Conceptualization, Methodology, Software, Writing-original draft; Hui Zheng: Data curation, Formal analysis, Investigation, Validation; Tingting Ma: Data curation, Formal analysis, Investigation; Xiaoping Tian: Investigation, Validation; Jing Xu: Investigation; Ying Li: Resources, Supervision; Lei Lan: Investigation, Methodology; Maillan Liu: Data curation, Formal analysis, Investigation; Ruirui Sun: Formal analysis; Yong Tang: Project administration, Resources; Fanrong Liang: Conceptualization, Funding acquisition, Resources; Fang Zeng: Conceptualization, Funding acquisition, Resources, Writing-review and editing.

Corresponding authors

Correspondence to Fanrong Liang or Fang Zeng.

Ethics declarations

Ethics approval

These included two trials were approved by the Ethics Committee of Hospital of Chengdu University of Traditional Chinese Medicine.

Consent to participate

All the patients provided written informed consents before entering the trial.

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All authors had full access to all the data in the study and accept responsibility to submit for publication.

Conflict of interest

The authors declare no competing interests.

Role of the funder

The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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Yin, T., Zheng, H., Ma, T. et al. Predicting acupuncture efficacy for functional dyspepsia based on routine clinical features: a machine learning study in the framework of predictive, preventive, and personalized medicine. EPMA Journal 13, 137–147 (2022). https://doi.org/10.1007/s13167-022-00271-8

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  • DOI: https://doi.org/10.1007/s13167-022-00271-8

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