Introduction: Chemotherapy-induced nausea and vomiting (CINV) significantly impacts the quality of life of cancer patients undergoing treatment, often leading to treatment interruptions and compromised adherence to therapy. Our objective was to identify patterns for selecting the optimal acupoints and explore the treatment principles behind forming effective acupoint combinations for CINV. Methods: Clinical trials were retrieved from eight databases. Descriptive statistics analysis was performed, followed by association rule mining, network analysis, hierarchical cluster analysis, and correlation analysis, all implemented with R software. Results: In summary, this study investigated the potential acupoints and combinations for CINV treatment in 104 published controlled clinical trials and randomized controlled trials. 104 prescriptions involving 48 acupoints were extracted. ST36, PC6, CV12, SP4, LI4, and ST25 appeared to be the most frequently used acupoints for CINV. Stomach Meridian, Conception Vessel (Renmai), and Pericardium Meridian were the most common selected meridians. The lower limbs, chest, and abdomen appeared as the predominant sites for acupoint selection. Co-occurrence network analysis indicated that ST36, PC6, and CV12 were central key node acupoints. The clustering analysis displayed the treatment principle of “harmonizing the stomach, stopping vomiting, and descending counterflow.” Association rule mining revealed that the combination of CV4, CV12, ST36, CV6, and PC6 emerged as the optimal acupoint combination for effectively treating CINV. Conclusion: Overall, our research provides evidence-based optimal acupuncture prescription for acupuncturists to treat CINV and presents a complementary therapy for chemotherapy physicians as well as patients to address CINV symptoms.

Einleitung: Chemotherapie-induzierte Übelkeit und Erbrechen (chemotherapy-induced nausea and vomiting, CINV) beeinträchtigen die Lebensqualität der behandelten Krebspatienten erheblich und führen häufig zu Therapieunterbrechungen und schlechter Therapieadhärenz. Unser Ziel war es, Muster für die Auswahl der optimalen Akupunkturpunkte zu identifizieren, und die therapeutischen Prinzipien für die Bildung wirksamer Akupunkturpunktkombinationen bei CINV zu untersuchen.Methoden: Es wurden klinische Studien aus acht Datenbanken abgerufen. Es erfolgte eine deskriptive statistische Analyse, gefolgt von Association-Rule-Mining, Netzwerkanalyse, hierarchischer Clusteranalyse und Korrelationsanalyse, die alle mit der R-Software durchgeführt wurden.Ergebnisse: Insgesamt wurden in dieser Studie 104 veröffentlichte kontrollierte klinische Studien und randomisierte kontrollierte Studien in Hinblick auf potenzielle Akupunkturpunkte und Akupunkturpunktkombinationen zur Behandlung von CINV untersucht. Es wurden 104 Verordnungen mit 48 Akupunkturpunkten extrahiert. Die Akupunkturpunkte ST36, PC6, CV12, SP4, LI4, und ST25 schienen die am häufigsten verwendeten Akupunkturpunkte bei CINV zu sein. Der Magen-Meridian, das Konzeptionsgefäß (Renmai) und der Perikard-Meridian waren die am häufigsten ausgewählten Meridiane. Untere Extremitäten, Brustkorb und Bauch schienen die Hauptregionen für die Auswahl der Akupunkturpunkte zu sein. Die Koinzidenz-Netzwerkanalyse deutete darauf hin, dass ST36, PC6, und CV12 zentrale Schlüsselknoten-Akupunkturpunkte waren. In der Cluster-Analyse zeigte sich das Behandlungsprinzip “Harmonisierung des Magens, Stoppen des Erbrechens, und absteigender Gegenstrom”. Das Association-Rule-Mining ergab, dass die Kombination von CV4, CV12, ST36, CV6, und PC6 die optimale Akupunkturpunktkombination für die wirksame Behandlung von CINV darstellt.Schlussfolgerung: Zusammengefasst lässt sich sagen, dass unsere Forschungsarbeit Akupunkteuren eine evidenzbasierte optimale Akupunkturverordnung zur Behandlung von CINV an die Hand gibt und Ärzten, die Chemotherapien durchführen, sowie Patienten eine komplementärmedizinische Therapie zur Behandlung von CINV-Symptomen vorstellt.

SchlüsselwörterChemotherapie-induzierte Übelkeit und Erbrechen, Akupunktur, Data Mining, Akupunkturpunkte, Traditionelle chinesische Medizin

GLOBOCAN 2020 indicates that more than 19.29 million new cancer cases and 9.95 million new cancer deaths occurred worldwide in 2020 [1], causing threat to human life and health. In recent years, novel antitumor therapies such as targeted therapy and immunotherapy have continually surfaced [2, 3], but chemotherapy remains essential in the therapeutic toolbox against malignant tumors [4], contributing to the prolonged survival of patients. However, due to its target on normal cells besides malignant tumors cells, it can inevitably cause a series of side effects in the antitumor process [5].

Chemotherapy-induced nausea and vomiting (CINV) is one of the major chemotherapy-related side effects reported by various studies [6‒8], affecting 20–70% of patients despite routine antiemetic prescription [9]. CINV can pose a negative effect on patients’ food intake, thus increasing the risk of malnutrition [10]. Besides, persistent malnutrition can reduce patients’ immunity, which interferes with the treatment process [11]. Important progress was made with the promotion of early prevention, management concepts, and the application of new drugs such as the serotonin receptor antagonist class of antiemetics and the neurokinin-1 inhibitors [12], but some of these symptoms still cannot be effectively alleviated, which seriously affects follow-up chemotherapy and patient compliance [13]. Furthermore, frequent use of antinausea drugs can lead to constipation, diarrhea, and other toxic side effects [14]. Hence, there should be a growing focus on complementary therapies for CINV.

Acupuncture therapy is a traditional Chinese treatment method, which combines acupoints selection, manipulation and treatment courses, etc. Studies indicated that acupuncture with its advantages of low cost, no toxic side effect, and easy popularization can effectively relieve the symptoms of CINV [15‒17]. However, the selection of acupuncture points largely depends on the acupuncturists’ clinical experience and formulation ideas, which neglects the process of identifying the optimal selection and combination methods for acupoints. Data mining technique, which involves the systematic exploration of vast datasets using algorithms to uncover concealed information [18], provides insights and methodologies for regularized and guided selection of acupuncture points. Hence, we conduct this study utilizing prevalent data mining techniques such as association rule mining (ARM), network analysis, and hierarchical cluster analysis. We aim to provide evidence-based optimal acupuncture prescriptions for acupuncturists in managing CINV. Additionally, we endeavor to offer a complementary therapy for chemotherapy physicians and patients, aimed at mitigating CINV and enhancing the overall quality of life.

The Literature Search

A comprehensive literature extraction was conducted to explore the efficacy of acupuncture in alleviating CINV from the inception of each database to September 1, 2023. We searched eight electronic databases including PubMed, Web of Science Core Collection, Excerpt Medical Database (Embase), Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data, Chinese Biomedical Literature Database (SinoMed), and Chongqing VIP Database (CQVIP). The search strategy employed a combination of the following terms: (1) “acupuncture” or “electroacupuncture” or “needles” or “acupuncture points” or “acupuncture therapy”; and (2) “chemotherapy” or “drug therapy” or “chemically-induced disorders”; and (3) “nausea” or “vomiting” or “emetics” or “gastrointestinal symptoms” or “signs and symptoms, digestive.” The language was restricted to Chinese and English, and equivalent Chinese characters were utilized for retrieval in Chinese databases.

Inclusion and Exclusion Criteria

Types of Studies

We restricted the literature search to randomized control trials (RCTs) and clinical control trials (CCTs) for CINV, while excluding experts’ experience reports, animal studies, reviews, case reports, meta-analyses, commentaries, and protocols to ensure the inclusion of rigorous, methodologically sound studies.

Participants

All participants in the trials were cancer patients who had undergone chemotherapy treatment. Patients were not limited in duration or severity and without restrictions on age, sex, or race.

Intervention and Control Group

The intervention groups applied acupuncture alone or acupuncture in combination with antiemetic medications such as dexamethasone and granisetron. Trials that did not involve needle insertion were excluded (such as moxibustion and laser stimulation). Additionally, the data extraction process eliminated unconventional acupuncture techniques, including head acupuncture, ear acupuncture, and extraordinary points acupuncture therapy. Control groups employed antiemetic medications, Chinese medicine, sham acupuncture, or placebo. Nevertheless, the analysis did not incorporate trials that compared the effectiveness of various acupuncture manipulation techniques or different prescriptions of acupoints.

Outcomes

Outcomes of trials were generally assessed by Rhodes Index of Nausea, Vomiting and Retching (INVR) scale, MASCC Antiemesis Tool (MAT) scale, rate of complete remission of delayed vomiting, and frequency and timing of nausea and vomiting. Only studies with positive results, i.e., proving the therapeutic effect of acupuncture, were included in the collection.

Data Screening and Extraction

Data screening involved two reviewers, with a third reviewer for resolving discrepancies. We removed duplicates using Endnote 21, and only included the latest publication of each study. Subsequently, eligible trials were identified by meticulously scrutinizing the titles, abstracts, and full texts of the retrieved papers in accordance with the predetermined inclusion and exclusion criteria. The prescriptions were extracted from the final eligible trials utilizing a self-established data extraction sheet. Moreover, we acquired any absent data through direct communication with the respective or pertinent authors. The nomenclature and location of acupoints adhered to the WHO standard acupuncture point locations in the western Pacific region [19]. The comprehensive process of screening and cleaning the data is summarized in Figure 1.

Fig. 1.

Flowchart for selection of articles.

Fig. 1.

Flowchart for selection of articles.

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Data Mining

Initially, we carried out a comprehensive descriptive statistics analysis of the frequency of acupoints and meridians employed in the extracted acupuncture prescriptions. Then, we delved into the distribution of these acupoints across various body regions and elucidated the specific points utilized. To further identify frequently used acupoint combinations, we employed ARM, network analysis, and hierarchical cluster analysis. Additionally, we performed correlation analysis to establish the connections between different acupoints. All the analyses were conducted using software R (version 4.2.3).

Association Rule Mining

ARM is a data mining technique that aims to uncover insightful relationships or patterns among items in large datasets. We chose the Apriori algorithm for ARM due to its advantages in scalability and robustness. The results are typically represented as if-then rules, where the antecedent specifies a set of items and the consequent specifies the presence of another item. The strength of an association rule depends on three measures: support, the percentage of prescriptions in the dataset that contain both the antecedent acupoint set and consequent acupoint set; confidence, the ratio of the support of the rule to the support of the antecedent acupoint set alone, which indicates the likelihood of observing the consequent given the antecedent; and lift, which measures the relative strength of the association rule compared to its independent occurrence. We chose a minimal confidence threshold as 0.6 and a minimal support threshold as 0.07 to filter out weak associations, ensuring that only relatively strong and reliable rules are retained. If the size of the selected rules is comparatively large, we group the rules by the lift values of the left-hand side to efficiently visualize the features of the rules. The idea is that antecedents (left-hand side) that are statistically dependent on the same consequent (right-hand side) are similar and thus can be grouped together.

Network Analysis

In network analysis, we first constructed a co-occurrence matrix, where each element characterizes the frequency of a specific acupoint appearing in conjunction with another acupoint across the collected prescriptions. This matrix served as the foundation for building a network, where each node represents an acupoint and edges connect acupoints that frequently co-occur. The resulting network using the Kamada-Kawai layout algorithm provides a visual representation of the interconnectedness of acupoints. As a force-directed graph drawing method, the Kamada-Kawai algorithm offers superior readability and intuitive visualization, particularly for identifying core acupoints, compared to other algorithms. Network analysis differs from ARM in several key aspects. In contrast to network analysis, which primarily focuses on pairs of two acupoints, ARM does not impose any constraints on the size of the acupoint set when analyzing co-occurrences. Besides, ARM primarily quantifies the strength of relationships using measures such as support and confidence and life, while network analysis utilizes various graph metrics, including centrality measures and clustering coefficients, to assess the importance and organization of nodes within the network.

Hierarchical Cluster Analysis

Hierarchical clustering supports exploring acupoints at different levels of granularity, from individual acupoints to larger clusters encompassing multiple points. This flexibility enables a nuanced understanding of the underlying structure of acupoint relationships, which is particularly valuable in complex systems such as acupuncture therapy. Unlike k-means clustering, hierarchical clustering does not require specifying the number of clusters beforehand. Given the binary nature of our acupoints dataset, we utilized Jaccard distance to quantify the similarity between acupoints. In this case, the distance between two one-hot encoded acupoints is the number of prescriptions that include both points divided by the total number of prescriptions that include either point. This distance information was then used to group acupoints into clusters based on Ward’s minimum variance method, which minimizes the total within-cluster variance and can prevent chain-like structures. The resulting clusters were further visualized in a dendrogram.

Correlation Analysis

We employed the phi coefficient, a measure of association between binary variables, to quantify the correlation between frequently used acupoints. The calculated correlation matrix was then visualized as a heatmap, effectively highlighting the presence of strong and weak correlations and offering a comprehensive overview of the correlation structure among acupoints. Correlation analysis complements the analytical insights gained from previous methods. Note that the sample estimates of the phi coefficient coincide with the Pearson correlation coefficient estimates in the binary case.

Study Selection

Initially, we retrieved 7,335 records from the eight databases. The mean sample size of the included studies was 78, with a median sample size of 64. Following the screening procedure outlined in sections 2 and 3, our investigation narrowed down to 104 studies (comprising 16 in English and 88 in Chinese). These studies collectively furnished 104 prescriptions of acupoints for data mining. A comprehensive depiction of the selection process is presented in a flowchart in Figure 1.

Description of Acupoints

Summarizing the acquired 104 prescriptions, 48 distinct acupoints appeared in the treatment with a total count of 386 occurrences, all of which belong to the fourteen meridians. The frequencies of all acupoints are visualized in a word cloud shown in Figure 2a, where the size of the words increases with frequency. Figure 2b presents both the absolute frequency and the relative percentage of the top 10 most frequent acupoints. The top three acupoints – Zusanli (ST36), Neiguan (PC6), and Zhongwan (CV12) – comprise a significant 56.2% (217 times) of the total occurrences.

Fig. 2.

Visualization of acupoint distributions for CINV treatment. a Wordcloud of acupoints scaled by frequency. b Frequency distribution of the top 10 most frequently used acupoints for CINV treatment.

Fig. 2.

Visualization of acupoint distributions for CINV treatment. a Wordcloud of acupoints scaled by frequency. b Frequency distribution of the top 10 most frequently used acupoints for CINV treatment.

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Description of Meridians

The 48 acupoints distributed across 12 standard meridians and 2 extraordinary meridians, namely Conception Vessel (Renmai) and Governor Vessel (Dumai). Figure 3 and Table 1 display the absolute frequency and corresponding percentage of all meridians, where the top three most frequent meridians – Stomach Meridian, Conception Vessel (Renmai), and Pericardium Meridian – constitute approximately 75% (288 times) together. For the acupoints in the 12 standard meridians, 19 (55.9%) are Yang meridian acupoints and 15 (44.1%) are Yin meridian acupoints.

Fig. 3.

Frequency distribution of meridians for CINV treatment.

Fig. 3.

Frequency distribution of meridians for CINV treatment.

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Table 1.

Description of meridians for CINV treatment

IdMeridianFrequencyPercentage (%)No. of acupointsList of acupoints (frequency)
Stomach Meridian 110 28.5 ST36(83), ST25(15), ST40(6), ST21(2), ST37(2), ST24(1), ST44(1) 
Conception Vessel 95 24.6 10 CV12(53), CV6(12), CV4(9), CV10(8), CV13(4), CV11(3), CV9(3), CV17(1), CV21(1), CV8(1) 
Pericardium Meridian 83 21.5 PC6(81), PC5(1), PC8(1) 
Spleen Meridian 38 9.84 SP4(19), SP6(12), SP15(3), SP3(2), SP10(1), SP9(1) 
Large Intestine Meridian 17 4.40 LI4(15), LI11(2) 
Bladder Meridian 15 3.89 BL20(5), BL21(4), BL17(3), BL18(1), BL23(1), BL62(1) 
Liver Meridian 10 2.59 LR3(7), LR13(3) 
Gallbladder Meridian 1.04 GB34(3), GB41(1) 
Governor Vessel 1.04 DU14(1), DU20(1), DU24(1), M-HN-3(1) 
10 Heart Meridian 1.04 HT7(4) 
11 Kidney Meridian 0.518 KI21(1), KI6(1) 
12 Lung Meridian 0.518 LU7(2) 
13 Small Intestine Meridian 0.259 SI3(1) 
14 Triple Energizer Meridian 0.259 SJ5(1) 
IdMeridianFrequencyPercentage (%)No. of acupointsList of acupoints (frequency)
Stomach Meridian 110 28.5 ST36(83), ST25(15), ST40(6), ST21(2), ST37(2), ST24(1), ST44(1) 
Conception Vessel 95 24.6 10 CV12(53), CV6(12), CV4(9), CV10(8), CV13(4), CV11(3), CV9(3), CV17(1), CV21(1), CV8(1) 
Pericardium Meridian 83 21.5 PC6(81), PC5(1), PC8(1) 
Spleen Meridian 38 9.84 SP4(19), SP6(12), SP15(3), SP3(2), SP10(1), SP9(1) 
Large Intestine Meridian 17 4.40 LI4(15), LI11(2) 
Bladder Meridian 15 3.89 BL20(5), BL21(4), BL17(3), BL18(1), BL23(1), BL62(1) 
Liver Meridian 10 2.59 LR3(7), LR13(3) 
Gallbladder Meridian 1.04 GB34(3), GB41(1) 
Governor Vessel 1.04 DU14(1), DU20(1), DU24(1), M-HN-3(1) 
10 Heart Meridian 1.04 HT7(4) 
11 Kidney Meridian 0.518 KI21(1), KI6(1) 
12 Lung Meridian 0.518 LU7(2) 
13 Small Intestine Meridian 0.259 SI3(1) 
14 Triple Energizer Meridian 0.259 SJ5(1) 

Analysis of Specific Points

Of the 48 acupoints, 64.6% (31) are specific points, which were further divided into 9 groups: Luo-connecting points, eight-confluent points, he-sea points, lower he-sea points, front-mu points, eight-influential points, yuan-primary points, five-shu points, and back-shu points. Table 2 summarizes the absolute frequency, relative percentage, and the acupoints composition of each group. The three largest groups, namely Luo-connecting points (109 occurrences), eight-confluent points (107 occurrences), and he-sea points (89 occurrences), collectively account for 52.3% of the total occurrences of all acupoints within the prescriptions.

Table 2.

Description of specific points for CINV treatment

IdSpecific pointFrequencyPercentage (%)No. of acupointsPercentage of acupoints (%)List of acupoints (frequency)
Luo-connecting points 109 28.2 10.4 PC6(81), SP4(19), ST40(6), LU7(2), SJ5(1) 
Eight-confluent points 107 27.7 16.7 PC6(81), SP4(19), LU7(2), BL62(1), GB41(1), KI6(1), SI3(1), SJ5(1) 
He-sea points 89 23.1 8.33 ST36(83), GB34(3), LI11(2), SP9(1) 
Lower he-sea points 86 22.3 4.17 ST36(83), GB34(3) 
Front-mu points 81 21.0 10.4 CV12(53), ST25(15), CV4(9), LR13(3), CV17(1) 
Eight-influential points 65 16.8 12.5 CV12(53), BL17(3), GB34(3), LR13(3), ST37(2), CV17(1) 
Yuan-primary points 28 7.25 8.33 LI4(15), LR3(7), HT7(4), SP3(2) 
Five-shu points 18 4.66 16.7 LR3(7), HT7(4), SP3(2), GB41(1), PC5(1), PC8(1), SI3(1), ST44(1) 
Back-shu points 11 2.85 8.33 BL20(5), BL21(4), BL18(1), BL23(1) 
IdSpecific pointFrequencyPercentage (%)No. of acupointsPercentage of acupoints (%)List of acupoints (frequency)
Luo-connecting points 109 28.2 10.4 PC6(81), SP4(19), ST40(6), LU7(2), SJ5(1) 
Eight-confluent points 107 27.7 16.7 PC6(81), SP4(19), LU7(2), BL62(1), GB41(1), KI6(1), SI3(1), SJ5(1) 
He-sea points 89 23.1 8.33 ST36(83), GB34(3), LI11(2), SP9(1) 
Lower he-sea points 86 22.3 4.17 ST36(83), GB34(3) 
Front-mu points 81 21.0 10.4 CV12(53), ST25(15), CV4(9), LR13(3), CV17(1) 
Eight-influential points 65 16.8 12.5 CV12(53), BL17(3), GB34(3), LR13(3), ST37(2), CV17(1) 
Yuan-primary points 28 7.25 8.33 LI4(15), LR3(7), HT7(4), SP3(2) 
Five-shu points 18 4.66 16.7 LR3(7), HT7(4), SP3(2), GB41(1), PC5(1), PC8(1), SI3(1), ST44(1) 
Back-shu points 11 2.85 8.33 BL20(5), BL21(4), BL18(1), BL23(1) 

Note: Specific points are the points that have special therapeutic effect among the 14 meridian points and are summarized with specific titles. According to its different distribution characteristics, meaning, and therapeutic effect, it is divided into “yuan-primary points,” “Luo-connecting points,” “he-sea points,” “lower-he-sea points,” “back-shu points,” “ front-mu points,” “eight confluent points,” “eight-influential points,” etc.

Acupoints on Different Body Parts

Figures 4 and 5 and Table 3 visualize the distribution of all acupoints across various body parts. When considering the frequency of the points, almost all acupoints (95%) are distributed in the lower limbs, chest and abdomen, or upper limbs. The frequency of acupoints used is highest in the lower limbs (36.27%), with 14 acupoints used a total of 140 times, while the chest and abdomen have the most variation of acupoints used (33.33%), with 16 acupoints used a total of 120 times.

Fig. 4.

Distribution of acupoints across body parts: a pie chart analysis.

Fig. 4.

Distribution of acupoints across body parts: a pie chart analysis.

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Fig. 5.

Visualization of acupoint locations on body models.

Fig. 5.

Visualization of acupoint locations on body models.

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Table 3.

Location distribution of acupoints across body parts

IdSite of the pointFrequencyPercentage (%)No. of acupointsPercentage of acupoints (%)List of acupoints (frequency)
Lower limbs 140 36.3 14 29.2 ST36(83), SP4(19), SP6(12), LR3(7), ST40(6), GB34(3), SP3(2), ST37(2), BL62(1), GB41(1), KI6(1), SP10(1), SP9(1), ST44(1) 
Chest and abdomen 120 31.1 16 33.3 CV12(53), ST25(15), CV6(12), CV4(9), CV10(8), CV13(4), CV11(3), CV9(3), LR13(3), SP15(3), ST21(2), CV17(1), CV21(1), CV8(1), KI21(1), ST24(1) 
Upper limbs 108 28.0 18.8 PC6(81), LI4(15), HT7(4), LI11(2), LU7(2), PC5(1), PC8(1), SI3(1), SJ5(1) 
Back and lumbar 14 3.63 10.4 BL20(5), BL21(4), BL17(3), BL18(1), BL23(1) 
Head, face, and neck 1.04 8.33 DU14(1), DU20(1), DU24(1), M-HN-3(1) 
IdSite of the pointFrequencyPercentage (%)No. of acupointsPercentage of acupoints (%)List of acupoints (frequency)
Lower limbs 140 36.3 14 29.2 ST36(83), SP4(19), SP6(12), LR3(7), ST40(6), GB34(3), SP3(2), ST37(2), BL62(1), GB41(1), KI6(1), SP10(1), SP9(1), ST44(1) 
Chest and abdomen 120 31.1 16 33.3 CV12(53), ST25(15), CV6(12), CV4(9), CV10(8), CV13(4), CV11(3), CV9(3), LR13(3), SP15(3), ST21(2), CV17(1), CV21(1), CV8(1), KI21(1), ST24(1) 
Upper limbs 108 28.0 18.8 PC6(81), LI4(15), HT7(4), LI11(2), LU7(2), PC5(1), PC8(1), SI3(1), SJ5(1) 
Back and lumbar 14 3.63 10.4 BL20(5), BL21(4), BL17(3), BL18(1), BL23(1) 
Head, face, and neck 1.04 8.33 DU14(1), DU20(1), DU24(1), M-HN-3(1) 

Association Rules of Acupoints

ARM analysis resulted in a total of 48 rules, and we present in Table 4 a summary of the top 25 rules, organized in a descending order based on their confidence values. Choosing the style as a “grouped matrix,” the results are shown as a balloon plot in Figure 6. The color of the balloons represents the aggregated confidence in the group with a certain consequence, and the size of the balloon characterizes the aggregated support. Here the aggregation is via the mean transformation. Furthermore, the columns and rows in the plot are reordered such that the lift value decreases from top-down and from left to right, placing the most interesting group in the top left corner. The result plot implies that Guanyuan (CV4), Zhongwan (CV12), Zusanli (ST36), Qihai (CV6), and Neiguan (PC6) often appeared together in the prescriptions. In addition, the group consisting of Gongsun (CV12), Zusanli (ST36), and Neiguan (PC6) is popular in acupuncture practice for CINV. Furthermore, Hegu (LI4) and Tianshu (ST25) are often considered as valuable complementary points.

Table 4.

Association rules of acupoints for CINV treatment

IdLHSRHSSupportConfidenceCoverageLiftCount
{CV4} => {CV6} 0.0865 1.00 0.0865 8.67 
{CV4} => {CV12} 0.0865 1.00 0.0865 1.96 
{CV10} => {CV12} 0.0769 1.00 0.0769 1.96 
{LI4} => {PC6} 0.144 1.00 0.144 1.28 15 
{CV6} => {CV12} 0.115 1.00 0.115 1.96 12 
{SP4} => {PC6} 0.183 1.00 0.183 1.28 19 
{SP4} => {ST36} 0.183 1.00 0.183 1.25 19 
{CV4, CV6} => {CV12} 0.0865 1.00 0.0865 1.96 
{CV12, CV4} => {CV6} 0.0865 1.00 0.0865 8.67 
10 {LI4, ST36} => {PC6} 0.125 1.00 0.125 1.28 13 
11 {ST25, ST36} => {PC6} 0.106 1.00 0.106 1.28 11 
12 {CV12, SP4} => {PC6} 0.135 1.00 0.135 1.28 14 
13 {CV12, SP4} => {ST36} 0.135 1.00 0.135 1.25 14 
14 {PC6, SP4} => {ST36} 0.183 1.00 0.183 1.25 19 
15 {SP4, ST36} => {PC6} 0.183 1.00 0.183 1.28 19 
16 {CV12, PC6, ST25} => {ST36} 0.0865 1.00 0.0865 1.25 
17 {CV12, ST25, ST36} => {PC6} 0.0865 1.00 0.0865 1.28 
18 {CV12, PC6, SP4} => {ST36} 0.135 1.00 0.135 1.25 14 
19 {CV12, SP4, ST36} => {PC6} 0.136 1.00 0.135 1.28 14 
20 {CV12, PC6} => {ST36} 0.365 0.927 0.394 1.16 38 
21 {CV12, ST36} => {PC6} 0.365 0.927 0.394 1.19 38 
22 {SP6} => {ST36} 0.106 0.917 0.115 1.15 11 
23 {PC6, ST25} => {ST36} 0.106 0.917 0.115 1.15 11 
24 {PC6, SP6} => {ST36} 0.0865 0.900 0.0962 1.13 
25 {LI4} => {ST36} 0.125 0.867 0.144 1.09 13 
IdLHSRHSSupportConfidenceCoverageLiftCount
{CV4} => {CV6} 0.0865 1.00 0.0865 8.67 
{CV4} => {CV12} 0.0865 1.00 0.0865 1.96 
{CV10} => {CV12} 0.0769 1.00 0.0769 1.96 
{LI4} => {PC6} 0.144 1.00 0.144 1.28 15 
{CV6} => {CV12} 0.115 1.00 0.115 1.96 12 
{SP4} => {PC6} 0.183 1.00 0.183 1.28 19 
{SP4} => {ST36} 0.183 1.00 0.183 1.25 19 
{CV4, CV6} => {CV12} 0.0865 1.00 0.0865 1.96 
{CV12, CV4} => {CV6} 0.0865 1.00 0.0865 8.67 
10 {LI4, ST36} => {PC6} 0.125 1.00 0.125 1.28 13 
11 {ST25, ST36} => {PC6} 0.106 1.00 0.106 1.28 11 
12 {CV12, SP4} => {PC6} 0.135 1.00 0.135 1.28 14 
13 {CV12, SP4} => {ST36} 0.135 1.00 0.135 1.25 14 
14 {PC6, SP4} => {ST36} 0.183 1.00 0.183 1.25 19 
15 {SP4, ST36} => {PC6} 0.183 1.00 0.183 1.28 19 
16 {CV12, PC6, ST25} => {ST36} 0.0865 1.00 0.0865 1.25 
17 {CV12, ST25, ST36} => {PC6} 0.0865 1.00 0.0865 1.28 
18 {CV12, PC6, SP4} => {ST36} 0.135 1.00 0.135 1.25 14 
19 {CV12, SP4, ST36} => {PC6} 0.136 1.00 0.135 1.28 14 
20 {CV12, PC6} => {ST36} 0.365 0.927 0.394 1.16 38 
21 {CV12, ST36} => {PC6} 0.365 0.927 0.394 1.19 38 
22 {SP6} => {ST36} 0.106 0.917 0.115 1.15 11 
23 {PC6, ST25} => {ST36} 0.106 0.917 0.115 1.15 11 
24 {PC6, SP6} => {ST36} 0.0865 0.900 0.0962 1.13 
25 {LI4} => {ST36} 0.125 0.867 0.144 1.09 13 

LHS, left-hand side; RHS, right-hand side.

Fig. 6.

Matrix visualization of association rules grouped by lift. The size of the balloons indicates the degree of support, and the grayscale indicates the level of confidence.

Fig. 6.

Matrix visualization of association rules grouped by lift. The size of the balloons indicates the degree of support, and the grayscale indicates the level of confidence.

Close modal

Network of Acupoints

Figure 7 visualizes the co-occurrence network of all acupoints as an undirected graph, where the notes are acupoints and the edges characterize the co-occurrence information. The transparency and weight of edges vary according to the occurrence times. Furthermore, the color of the nodes indicates the meridian that the acupoint belongs to, and the size of the nodes grows as the frequency increases.

Fig. 7.

Co-occurrence network of acupoints for CINV treatment.

Fig. 7.

Co-occurrence network of acupoints for CINV treatment.

Close modal

The graph consists of 261 edges and 48 nodes with a network density of 0.231. The diameter of the graph is 3. The average degree is 10.875 and the median degree is 9, which means that every acupoint typically coexists with the other 10 acupoints (Fig. 8). Zusanli (ST36) has the highest degree as 42, followed by Neiguan (PC6) (39) and Zhongwan (CV12) (38). These three nodes are also the acupoints that have the largest closeness centrality and betweenness centrality, which is evident in their central role in the network. The three pairs between Zusanli (ST36), Neiguan (PC6), and Zhongwan (CV12), which form a triangle in the graph, have the highest co-occurrences among all acupoints (over 40 times).

Fig. 8.

Nodes degree distribution of the co-occurrence network.

Fig. 8.

Nodes degree distribution of the co-occurrence network.

Close modal

Hierarchical Cluster Analysis of Acupoints

We conducted hierarchical cluster analysis on the top 13 high-frequency acupoints (with minimal frequency 5), and the resulting clusters are presented in Figure 9. There are three clusters if cutting the dendrogram at a height of 1.25. The first cluster consists of Tianshu (ST25), Xiawan (CV10), Qihai (CV6), and Guanyuan (CV4); the second cluster includes Zhongwan (CV12), Zusanli (ST36), and Neiguan (PC6); and the last cluster includes Gongsun (CV12), Pishu (BL20), Taichong (LR3), Fenglong (ST40), Hegu (LI4), and Sanyinjiao (SP6).

Fig. 9.

Cluster dendrogram of the 13 most frequently used acupoints.

Fig. 9.

Cluster dendrogram of the 13 most frequently used acupoints.

Close modal

Correlation Analysis of Acupoints

Figure 10 displays the correlation between the top 16 most frequent acupoints as a heatmap based on phi coefficient, where red blocks indicate strong positive correlation and light blue blocks indicate weak correlation. The highest correlation is between Qihai (CV6) and Guanyuan (CV4) (0.85), followed by the correlation between Qihai (CV6) and Xiawan (CV10) (0.69). The heatmap corroborates the cluster results by visually demonstrating that the correlation within clusters is significantly stronger than the correlation between clusters.

Fig. 10.

Heatmap illustrating Phi correlation among the 16 acupoints that were most commonly employed.

Fig. 10.

Heatmap illustrating Phi correlation among the 16 acupoints that were most commonly employed.

Close modal

Overall, a total of 108 studies met our requirements, and the final data analysis consisted of 104 (96%) studies, which all reported positive results. According to these 104 RCTs/CCTs, a total of 8138 CINV patients participated. The most commonly used treatment method for CINV among these studies was acupuncture combined with antiemetic drugs (45.2%), and only 17 (16.3%) studies used acupuncture alone. This indicates the efficacy of acupuncture in treating CINV, with acupuncture combined therapy being extensively employed in clinical practice.

Among the clinical trials reviewed, Zusanli (ST36) emerged as the most commonly utilized acupoint, appearing with a frequency of 83. Furthermore, it occupied a pivotal position within the co-occurrence network, with the highest degree centrality (42), normalized betweenness centrality (0.207), and closeness centrality (0.013). Zusanli (ST36), located on the anterior aspect of the leg, is both the he-sea point and lower he-sea points of stomach meridian of Foot Yangming, which has the effect of strengthening the spleen and stomach and relieving nausea and vomiting. Study [20] has shown that puncturing Zusanli (ST36) can regulate gastrointestinal motility disorders by regulating nerve signal transduction [21], improving visceral hypersensitivity [22] and regulating Cajal interstitial cell network [23]. The second most frequently used acupoint is Neiguan (PC6), with a total frequency of 81. According to the theory of Chinese medicine, Neiguan (PC6) is the Luo-connecting point of pericardium meridian, and also belongs to the eight-confluent points, which has the functions of calming the heart and spirit, relieving nausea and vomiting, and easing pain. Meng et al. [24] found that stimulating Neiguan (PC6) can regulate the gastric pressure, activate the discharge of solitary nucleus neurons, promote the contraction of gastric smooth muscle, and protect gastric mucosa. Zhongwan (CV12), the front-mu points of stomach meridian and also the eight-influential point of Fu, is the third most frequently used acupoint for CINV, with the function of strengthening the spleen, promoting digestion, and descending stomach qi. Cui et al. [25] demonstrated that the beneficial effects of electropuncture (EA) at Zhongwan (CV12) for CINV in a rat mode may be mediated through inhibition of 5-HT secretion in the duodenum and activity of the nucleus of the solitary tract. Meanwhile, these three acupoints have the highest co-occurrences among all acupoints in network analysis, as shown in Figure 7.

Furthermore, Luo-connecting points were predominant specific points in the treatment of CINV, accounting for 28.24% of the total frequency. Luo-connecting points can treat the disease syndrome of both external and internal meridians. Besides, eight-confluent points are the second most commonly used specific points, with the frequency of 107 (27.72%). Among which, Gongsun (SP4) and Neiguan (PC6) are most frequently used in combination. Xie et al. [26] found that the combination acupoint of Gongsun (SP4) and Neiguan (PC6) can correct HPA axis function by increasing the content of hypothalamic monoamine neurotransmitters’ norepinephrine, dopamine, and serotonin, thus improving the gastrointestinal motility of functional dyspepsia rats. Their team’s previous study [27] also showed that EA at Gongsun (SP4) and Neiguan (PC6) can regulate organ index, promote gastric emptying, improve immune function, reduce inflammatory response, and improve gastrointestinal motility disorders. As can be seen from Figures 4 and 5, acupuncture points mainly distributed in the lower limbs, chest, and abdomen, while the frequency of acupuncture points is lower in the back, lumbar, head, face, and neck. The acupoints of lower limbs mainly belong to the spleen meridian and the stomach meridian, while the acupoints on the chest and abdomen are mainly Conception Vessel (Renmai)’s points. This reflects the selection principle of “combining nearby acupoints with remote acupoints following the meridians.”

In clinical settings, acupuncture practitioners commonly blend several acupoints, considering the symptoms and underlying causes of diseases, aiming to amplify their synergistic effects and enhance therapeutic outcomes. According to our study, we used ARM techniques to identify the three most commonly used acupoint combinations in CINV treatment (Fig. 6). Among which, the most common combination of selected acupoints in the prescriptions is Zhongwan (CV12), Guanyuan (CV4), Qihai (CV6), Zusanli (ST36), and Neiguan (PC6). Zhongwan (CV12), Guanyuan (CV4), and Qihai (CV6) all belong to the Conception Vessel (Renmai). The Conception Vessel (Renmai) can treat viscera diseases related to the chest and abdomen. Li et al. [28] used abdominal acupuncture to treat CINV by puncturing Zhongwan (CV12), Xiawan (CV10), Qihai (CV6), and Guanyuan (CV4), and their study indicated that acupuncture could prevent and treat CINV by regulating the central nervous system, gastrointestinal function, and visceral nerve reflex. Meanwhile, Zusanli (ST36) and Neiguan (PC6) are the commonly used acupoint combinations in treating nausea and vomiting. Xiao et al. [29] found that EA at Zusanli (ST36) and Neiguan (PC6) can restore gastrointestinal motility by regulating the level of gastrointestinal hormone in gastric antrum tissues (reducing the content of 5-HT and increasing the protein expression of motilin and ghrelin), thus improving the symptoms of nausea and vomiting. Another commonly used acupoint combinations are Gongsun (SP4), Neiguan (PC6), and Zusanli (ST36) based on rule mining. Peng et al. [30] found that EA at Gongsun (SP4), Neiguan (PC6), and Zusanli (ST36) can improve the content of epidermal growth factor of the gastric mucosa, promote the synthesis and release of nitric oxide, and protect the gastric mucosa. In addition, simultaneous stimulation of the three points demonstrated a superior effect compared to the paired stimulation of two points. Furthermore, Hegu (LI4) and Tianshu (ST25) are the yuan-primary points and front-mu points of large intestine meridian of hand-yangming, and Tianshu (ST25) also belongs to the stomach meridian. The combination of the two can treat gastrointestinal diseases.

By using a hierarchical clustering algorithm, 13 acupoints, each with a frequency exceeding 5, were clustered into three major clusters (Fig. 9): cluster 1, Tianshu (ST25), Xiawan (CV10), Qihai (CV6), and Guanyuan (CV4); cluster 2, Zhongwan (CV12), Zusanli (ST36), and Neiguan (PC6), where both clusters 1 and 2 can harmonize the stomach to stop vomiting; and cluster 3, Gongsun (SP4), Pishu (BL20), Taichong (LR3), Fenglong (ST40), Hegu (LI4), and Sanyinjiao (SP6), which can strengthen the spleen and stomach, and descend counterflow. Overall, these reflect the main treatment principle of “harmonizing the stomach, stopping vomiting and descending counterflow.” Chinese medicine believes that the pathogenesis of vomiting is stomach imbalance, stomach qi ascending counterflow; the lesion of the viscera is mainly the stomach, but also involves the liver and spleen. The above three clusters indicate the point selection principle of following the meridian and matching points near and far.

Our study holds clinical implications as below. First, by employing data mining techniques to derive rules for point selection and prescription formation, we offer evidence-based guidelines for clinical acupuncturists in treating CINV. These guidelines contribute to standardizing and optimizing acupuncture treatments, enhancing their efficacy and consistency in clinical practice. Second, our research outcomes introduce a complementary therapeutic approach for chemotherapy physicians in managing CINV. By incorporating acupuncture as an adjunctive therapy, clinicians can diversify treatment modalities, potentially improving patient outcomes and satisfaction. This integrative approach underscores the importance of a holistic patient-centered care model in oncology. Furthermore, our findings empower CINV patients by providing them with actionable information regarding acupoints that can alleviate symptoms at home. This self-care aspect not only empowers patients but also promotes active participation in managing their health and well-being between clinical visits.

Several limitations in the study should be considered. First, the absence of a standardized measurement for CINV in the included RCTs/CCTs poses a challenge. Most studies rely on nausea and vomiting grades or the subjective feelings of the patients for evaluating therapeutic effects, limiting detailed analysis on the outcome measurement during data mining. Second, some of the identified acupoint combinations and association rules in this study lack validation through rigorous clinical trials and mechanism research, necessitating further refinement in future investigations to enhance the applicability and reliability of acupuncture in the context of CINV. Third, our study exclusively encompasses acupoints from the 14 meridians, excluding empirical acupoints such as Dong’s odd point, wrist, and ankle points, and different stimulation methods of acupuncture (warm acupuncture, electric acupuncture, etc.) were not differentiated by efficacy, which underscores the need for future research to encompass a broader spectrum of acupoints, ensuring a more comprehensive understanding of effective acupuncture treatment for CINV. Fourth, in addition to the main points, some studies have matched different acupoints for different types of CINV. We did not statistically analyze these acupoints, but further investigation on adding or subtracting acupoints based on individual patient symptoms holds promise for enhancing personalized treatment strategies in clinical practice.

In summary, our research investigated the potential acupoints selection patterns for CINV treatment based on a data mining approach applied to published RCTs/CCTs. Zusanli (ST36), Neiguan (PC6), Zhongwan (CV12), Gongsun (SP4), Hegu (LI4), and Tianshu (ST25) appeared to be the most frequently used acupoints for CINV. Stomach Meridian, Conception Vessel (Renmai), and Pericardium Meridian were the predominant meridians. The most common locations for selecting acupoints were the lower limbs, chest, and abdomen. Co-occurrence network analysis indicated that Zusanli (ST36), Neiguan (PC6), and Zhongwan (CV12) were central key node acupoints. The clustering analysis displayed the treatment principle of “harmonizing the stomach, stopping vomiting and descending counterflow.” ARM demonstrated that Guanyuan (CV4), Zhongwan (CV12), Zusanli (ST36), Qihai (CV6), and Neiguan (PC6) were the preferred potential acupoint combinations for the treatment of CINV. Overall, our research provides acupoint selection guidelines for acupuncturists to consider for the treatment of CINV, as well as an alternative therapy for chemotherapists and patients to alleviate symptoms of CINV.

An ethics statement was not required for this study type as it is based exclusively on data extracted from PubMed, Web of Science Core Collection, Excerpt Medical Database (Embase), Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data, Chinese Biomedical Literature Database (SinoMed), and Chongqing VIP Database (CQVIP).

The authors have no conflicts of interest to declare.

This work was supported by the Research Projects of Biomedical Center of Hubei Cancer Hospital (Grant No. 2022SWZX21) and the Projects of Hubei Provincial Population Welfare Foundation (Grant No. 2022-5-6).

Z.K., H.C., Y.Z., and X.W. conceived and designed the study and edited the final manuscript. J.K., Y.X., and R.Z. designed the research methodology. Z.X. and Y.H. developed the search strategy and performed data extraction. Z.K., H.C., and Y.Z. performed data analysis and wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version.

Additional Information

Zi Ke, Hongruyu Chen and Yong Zhao contributed equally to this work.

This article and its supplementary material files contain all the data that were produced or examined in this study. For further questions, the corresponding author can be contacted.

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