Dataset Information

Evaluating the Effects of Heat-Clearing Traditional Chinese Medicine in Stable Bronchiectasis by a Series of N-of-1 Trials.

ABSTRACT:

Purpose

The purpose of this study is to study the effects of heat-clearing Traditional Chinese Medicine (TCM) in the stable stage of bronchiectasis via N-of-1 trials.

Methods

The N-of-1 trials in this study were randomized and double-blinded with crossover comparisons consisting of three pairs. Each pair was of two 4-week periods. Each patient took the individualized decoction in the experimental period and the individualized decoction was removed of heat-clearing drugs, mainly including heat-clearing and detoxifying drugs, in the control period for three weeks. After three weeks, the patients stopped taking the decoction for one week. The primary outcome was from patients' self-reporting symptoms scores on a 1-7-point Likert scale. Mixed-effects models were used to conduct statistical analysis on these N-of-1 trials.

Results

Of the 21 patients enrolled, 15 completed three pairs of N-of-1 trials (71.43%). (1) Seen from the individual level, no statistical difference between the experimental decoction and the control (P > 0.05) was observed. However, 5 patients found better decoctions according to the clinical criteria. (2) As revealed by the group data of all the N-of-1 trials, the control was better than the individualized decoction in terms of symptom scores on the Likert scale (1.94 ± 0.69 versus 2.08 ± 0.68, P = 0.04, mean difference, and 95% CI: 0.19 (0.01, 0.37)) and on CAT scores (13.66 ± 6.57 versus 13.95 ± 6.97, P = 0.04, mean difference, and 95% CI: 0.86 (0.042, 1.67)), but such differences were not clinically significant. The other outcomes, such as Likert scale score of respiratory symptoms and 24-hour sputum volume, showed no statistical difference.

Conclusion

The experimental design of this study can make the TCM individualized treatment fully play its role and can detect the individualized tendencies according to the severity of phlegm and heat in some subjects. With the intermittent use or reduced use of heat-clearing drugs, most of the subjects, at the group level, enrolled in the series of N-of-1 trials may improve the symptoms and quality of life while saving the cost of TCM and reducing the potential side effects of heat-clearing TCM. This trial is registered with clinicaltrials.goc (NCT03147443).

SUBMITTER: Zi S

PROVIDER: S-EPMC8789431 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundAction ("gongxiao" in Chinese) of traditional Chinese medicine (TCM) is the high recapitulation for therapeutic and health-preserving effects under the guidance of TCM theory. TCM-defined herbal properties ("yaoxing" in Chinese) had been used in this research. TCM herbal property (TCM-HP) is the high generalization and summary for actions, both of which come from long-term effective clinical practice in two thousands of years in China. However, the specific relationship between TCM-HP and action of TCM is complex and unclear from a scientific perspective. The research about this is conducive to expound the connotation of TCM-HP theory and is of important significance for the development of the TCM-HP theory.MethodsOne hundred and thirty-three herbs including 88 heat-clearing herbs (HCHs) and 45 blood-activating stasis-resolving herbs (BAHRHs) were collected from reputable TCM literatures, and their corresponding TCM-HPs/actions information were collected from Chinese pharmacopoeia (2015 edition). The Kennard-Stone (K-S) algorithm was used to split 133 herbs into 100 calibration samples and 33 validation samples. Then, machine learning methods including supported vector machine (SVM), k-nearest neighbor (kNN) and deep learning methods including deep belief network (DBN), convolutional neutral network (CNN) were adopted to develop action classification models based on TCM-HP theory, respectively. In order to ensure robustness, these four classification methods were evaluated by using the method of tenfold cross validation and 20 external validation samples for prediction.ResultsAs results, 72.7-100% of 33 validation samples including 17 HCHs and 16 BASRHs were correctly predicted by these four types of methods. Both of the DBN and CNN methods gave out the best results and their sensitivity, specificity, precision, accuracy were all 100.00%. Especially, the predicted results of external validation set showed that the performance of deep learning methods (DBN, CNN) were better than traditional machine learning methods (kNN, SVM) in terms of their sensitivity, specificity, precision, accuracy. Moreover, the distribution patterns of TCM-HPs of HCHs and BASRHs were also analyzed to detect the featured TCM-HPs of these two types of herbs. The result showed that the featured TCM-HPs of HCHs were cold, bitter, liver and stomach meridians entered, while those of BASRHs were warm, bitter and pungent, liver meridian entered.ConclusionsThe performance on validation set and external validation set of deep learning methods (DBN, CNN) were better than machine learning models (kNN, SVM) in sensitivity, specificity, precision, accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. The deep learning classification methods owned better generalization ability and accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. Besides, the methods of deep learning would help us to improve our understanding about the relationship between herbal property and action, as well as to enrich and develop the theory of TCM-HP scientifically.

Project description:BackgroundExtensive studies have been conducted to evaluate the pain relief effect of traditional Chinese medicine (TCM) interventions on patients with low back pain, including in China. However, there is a dearth in the literature documenting the diverse goals of TCM interventions, let alone the overall effect of such interventions. In this study, the goal attainment scaling (GAS) method was adopted to evaluate individualised TCM interventions on chronic low back pain.MethodsA pre-post intervention study was conducted on patients with chronic low back pain who received individualised TCM interventions in community health services. The study was undertaken in three community health centres in Hangzhou of China. A total of 165 eligible patients were invited, and 150 participated in the study, including 136 who completed both pre- and postintervention surveys. Each participant was asked to identify three to five intended goals from a pool of 26 outcome indicators and their corresponding expectations of these goals prior to the TCM interventions. Their conditions were rated against the selected indicators on a self-report five-point Likert scale before and after the TCM interventions, respectively. Gaps between the actual conditions and the expected goals were summed up for each participant and converted into a standardised GAS score, with a higher score indicating higher achievements, and 50 indicting patient expectations were met. Linear regression models were established to determine the factors associated with the pre-post GAS changes after adjustment for variations in other variables.ResultsOn average, an increase of 14.99 (SD = 9.81) in the GAS scores was achieved. This resulted in a mean GAS score of 48.33 (SD = 9.74) after the TCM interventions, falling slightly short (<2) of patient expectations. The multivariate linear regression models revealed that local residents, the retired, and those who perceived lower professional competency of their attending doctors had a smaller increase in the GAS scores after adjustment for variations in other variables.ConclusionThe individualised TCM interventions can help patients with low back pain to achieve their expected goals as measured by the GAS. Further studies are needed to better understand how patients set up their goals and the professional competency requirements to meet patient expectations.