Machine learning algorithms are applied to discover gut flora markers that predict the clinical efficacy of acupuncture, so as to screen the appropriate population for acupuncture and optimise the allocation of healthcare resources.
Crohn's disease is an intestinal inflammatory disease,causing abdominal pain, diarrhea and other symptoms.The intestinal flora disorder is closely related to the occurrence and development of Crohn's disease. Acupuncture can induce remission of Crohn's disease during mild to moderate active period, improve clinical symptoms such as abdominal pain. Acupuncture can affect the gut microbiota. The aim of this study was to apply gut microbiological data and clinical data from subjects at baseline to predict the clinical efficacy of acupuncture by machine learning algorithms, and to classify patients as effective/ineffective in order to screen for suitable subjects for acupuncture.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
SCREENING
Masking
NONE
Enrollment
55
The investigators selected acupoints including Zhongwan (CV12) and bilateral Shangjuxu (ST37), Sanyinjiao (SP6), Gongsun (SP4), Taichong (LR3), Taixi (KI3), Hegu (LI4), and Quchi (LI11)17 according to the World Health Organization standard. Single-use 0.30×40 mm or 0.30×25 mm acupuncture needles (Hwato, Suzhou, China) were vertically inserted into each acupoint to 20-30 mm depth to obtain a deqi sensation (a soreness, distention, numbness or heaviness sensation). Bilateral Zusanli (ST36) and Tianshu (ST25) were selected for moxibustion. Pure moxa sticks (diameter: 2.8 cm; Hanyi, Nanyang, China) were ignited and fixed on a moxibustion stand at a distance of 3-5 cm to the surface of acupoints. The temperature of skin surface at the acupoints was maintained at 43 ± 1°C and monitored with a miniature infrared thermometer (Fluke 62, Fluke Corporation, Everett, WA, USA). Acupuncture and moxibustion were concomitantly performed for 30 min.
Shanghai Institute of Acupuncture, Moxibustion and Meridian
Shanghai, China
Characterisation of gut microflora strains predictive of acupuncture efficacy
Application of machine learning algorithms to discover gut microbial markers (Characterisation of intestinal flora strains) predicting clinical efficacy of acupuncture to screen for appropriate acupuncture populations. Criteria for judging the efficacy of acupuncture: At 12 weeks, patients had complete remission of abdominal pain ( VAS score or frequency of abdominal pain per week = 0) or remission of abdominal pain ( the reduction of VAS score or the frequency of abdominal pain ≥ 3 compare with baseline) were considered effective. Otherwise, it was considered ineffective. Visual Analogue Scale(VAS score):The maximum score is 0, and the minimum score is 10.Higher scores represent a greater degree of abdominal pain and higher scores mean a worse outcome. Frequency of abdominal pain:The maximum score is 0, and the minimum score is 7. Higher scores represent a greater degree of abdominal pain and higher scores mean a worse outcome.
Time frame: Week 12
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