Inflammatory Bowel DiseasesNCT04296500
A Development of Inflammatory Bowel Disease Pattern Identification Algorithm Using Case Series Data
An observational study of Inflammatory Bowel Diseases, run by Hyangsook Lee, KMD, PhD at 1 site in South Korea.
Trial summary
This study aimed to identify inflammatory bowel disease (IBD) patterns based on presenting symptoms and to suggest algorithms for determining pattern and herbal prescriptions for corresponding patterns. Read more about this study
- Who is it for?
- Ages 15 to 65 years
- Conditions the study is for
- Inflammatory Bowel Diseases
- What is being tested?
- Decision tree modeling
- How many people
- 67 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Nov 2007to 28 Oct 2015
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 15 to 65 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients who had been diagnosed with IBD by gastroenterologist and achieved clinical remission after treatment with herbal medicine prescriptions
Who may be able to join
- Diagnosis of IBD by gastroenterologist
- Patients have achieved and maintained clinical remission of IBD symptoms after they took herbal prescriptions
- Patients have provided written informed consent
Who cannot join
- Details regarding any of 25 symptoms were omitted
Trial locations and contacts
1 site in 1 country.
South Korea1 site
Acupuncture & Meridian Science Research Centre
Seoul, 02447
About this study
Official title: Herbal Medicine for Inflammatory Bowel Diseases: a Development of Pattern Identification Algorithm by Retrospective Analysis of Case Series Data
This study aimed to identify inflammatory bowel disease (IBD) patterns based on presenting symptoms and to suggest algorithms for determining pattern and herbal prescriptions for corresponding patterns. The investigators collected symptom data of 67 IBD patients who achieved and maintained clinical remissions after they had taken herbal medicine prescriptions. Prescriptions were categorised into 5 patterns, which were named after main features and symptoms of included patients. Associations between presenting symptoms and patterns were visualised using a term frequency inverse document frequency (TF-IDF) method. Determining IBD patterns from symptoms of patients was analysed and charted by decision tree modeling.
Read the detailed description
Herbal prescriptions are one of the most sought complementary and alternative medicine treatment strategies for inflammatory bowel disease patients. However, variability in pattern identification of Traditional Chinese Medicine (TCM)/Traditional East Asian Medicine (TEAM) has been criticised. Using data of patients who achieved and maintained clinical remission after TCM/TEAM herbal medicine prescription, the investigators aimed to develop treatment algorithms refined by identified pattern and key symptoms which practitioners can easily discriminate.
Based on herbal prescriptions which induced clinical remission, IBD patients were divided into 5 patterns, i.e., Large intestine type, Water-dampness type, Respiratory type, Upper gastrointestinal (GI) tract type, and Coldness type. By term frequency-inverse document frequency (TF-IDF) method, the association between 22 symptoms that were described as indications of the herbal medicine prescriptions and 5 patterns were analysed. Decision tree modeling was used for prediction of relevant patterns from symptoms.
- Conditions
- Inflammatory Bowel Diseases
- Being tested
- Decision tree modeling
- Keywords
- Herbal prescription, Herbal medicine, Pattern identification, Algorithm, Decision tree, TF-IDF
- Medical subject headings
- Inflammatory Bowel Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Decision tree modelingOther
A decision tree analysis was employed to explore the process of decision-making on types of pattern based on the existence or nonexistence of a symptom. At the end of tree presented is the proportion of patients who are categorised into each pattern.
In this study, the classification was performed by applying the classification and regression tree (CART) algorithm using Scikit-learn package of Python, which performs a division using the Gini coefficient or the decrement of dispersion. The Gini coefficient is one of the tools for measuring entropy or diversity in each node and it measures the decrement by comparing the information entropy before and after separation. To avoid overfitting, the maximum number of leaf nodes was limited to four and the pruning method which complied with the principle of minimum description length was applied.
| Study group | Role | What they receive |
|---|---|---|
| Decision tree algorithm training/testing | The investigators divided data of 67 patients into 5 groups to do 5 fold cross validation. Four groups were used to train decision tree algorithm and one group was used to test it. Receives: Other: Decision tree modeling |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of pattern identification algorithm
Pattern identification algorithm was suggested using a decision tree method. Decision tree method was employed to explore the process of decision making on types of pattern based on clinical features of patients.
Measured: Oct 2015
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Mar 2020
- Study start1 Nov 2007
- Primary completion28 Feb 2015
- Study completion28 Oct 2015
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Inflammatory Bowel DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 67 participants (actual)
Sponsor and investigators
- Lead sponsor
- Hyangsook Lee, KMD, PhD
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Hyangsook Lee, KMD, PhD, Kyunghee University
- Investigators
- Hyangsook Lee, Doctor, Principal investigator, Kyunghee University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04296500
- Sponsor's protocol ID
- KyungheeU
- Status last verified
- Mar 2020
- First submitted
- 3 Mar 2020
- Last updated
- 6 Mar 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Bernstein CN, Fried M, Krabshuis JH, Cohen H, Eliakim R, Fedail S, Gearry R, Goh KL, Hamid S, Khan AG, LeMair AW, Malfertheiner, Ouyang Q, Rey JF, Sood A, Steinwurz F, Thomsen OO, Thomson A, Watermeyer G. World Gastroenterology Organization Practice Guidelines for the diagnosis and management of IBD in 2010. Inflamm Bowel Dis. 2010 Jan;16(1):112-24. doi: 10.1002/ibd.21048.
PubMed 19653289Panaccione R, Rutgeerts P, Sandborn WJ, Feagan B, Schreiber S, Ghosh S. Review article: treatment algorithms to maximize remission and minimize corticosteroid dependence in patients with inflammatory bowel disease. Aliment Pharmacol Ther. 2008 Sep 15;28(6):674-88. doi: 10.1111/j.1365-2036.2008.03753.x.
PubMed 18532990Abitbol V, Lahmek P, Buisson A, Olympie A, Poupardin C, Chaussade S, Lesgourgues B, Nahon S. Impact of complementary and alternative medicine on the quality of life in inflammatory bowel disease: results from a French national survey. Eur J Gastroenterol Hepatol. 2014 Mar;26(3):288-94. doi: 10.1097/MEG.0000000000000040.
PubMed 24407360Lee MS, Lee JA, Alraek T, Bian ZX, Birch S, Goto H, Jung J, Kao ST, Moon SK, Park B, Park KM, You S, Yun KJ, Zaslawski C. Current research and future directions in pattern identification: Results of an international symposium. Chin J Integr Med. 2016 Dec;22(12):947-955. doi: 10.1007/s11655-014-1833-3. Epub 2014 Jun 18.
PubMed 24938445Yu F, Takahashi T, Moriya J, Kawaura K, Yamakawa J, Kusaka K, Itoh T, Morimoto S, Yamaguchi N, Kanda T. Traditional Chinese medicine and Kampo: a review from the distant past for the future. J Int Med Res. 2006 May-Jun;34(3):231-9. doi: 10.1177/147323000603400301.
PubMed 16866016Liu B, Zhou X, Wang Y, Hu J, He L, Zhang R, Chen S, Guo Y. Data processing and analysis in real-world traditional Chinese medicine clinical data: challenges and approaches. Stat Med. 2012 Mar 30;31(7):653-60. doi: 10.1002/sim.4417. Epub 2011 Dec 9.
PubMed 22161304
Study at a glance
From the registry record, last updated by the sponsor on 6 Mar 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Herbal Medicine for Inflammatory Bowel Diseases: a Development of Pattern Identification Algorithm by Retrospective Analysis of Case Series Data
- ClinicalTrials.gov ID
- NCT04296500
- Acronym / sponsor ID
- KyungheeU
- Conditions
- Inflammatory Bowel Diseases
- Keywords
- Herbal prescription, Herbal medicine, Pattern identification, Algorithm, Decision tree, TF-IDF
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 67 participants (actual)
- Lead sponsor
- Hyangsook Lee, KMD, PhDUniversity, hospital or other
- Study start
- 1 Nov 2007 (actual)
- Primary completion
- 28 Feb 2015 (actual)
- Study completion
- 28 Oct 2015 (actual)
- First posted
- 5 Mar 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aimed to identify inflammatory bowel disease (IBD) patterns based on presenting symptoms and to suggest algorithms for determining pattern and herbal prescriptions for corresponding patterns. The investigators collected symptom data of 67 IBD patients who achieved and maintained clinical remissions after they had taken herbal medicine prescriptions. Prescriptions were categorised into 5 patterns, which were named after main features and symptoms of included patients. Associations between presenting symptoms and patterns were visualised using a term frequency inverse document frequency (TF-IDF) method. Determining IBD patterns from symptoms of patients was analysed and charted by decision tree modeling.
Detailed description
Herbal prescriptions are one of the most sought complementary and alternative medicine treatment strategies for inflammatory bowel disease patients. However, variability in pattern identification of Traditional Chinese Medicine (TCM)/Traditional East Asian Medicine (TEAM) has been criticised. Using data of patients who achieved and maintained clinical remission after TCM/TEAM herbal medicine prescription, the investigators aimed to develop treatment algorithms refined by identified pattern and key symptoms which practitioners can easily discriminate.
Based on herbal prescriptions which induced clinical remission, IBD patients were divided into 5 patterns, i.e., Large intestine type, Water-dampness type, Respiratory type, Upper gastrointestinal (GI) tract type, and Coldness type. By term frequency-inverse document frequency (TF-IDF) method, the association between 22 symptoms that were described as indications of the herbal medicine prescriptions and 5 patterns were analysed. Decision tree modeling was used for prediction of relevant patterns from symptoms.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Decision tree algorithm training/testing | – | Other: Decision tree modelingThe investigators divided data of 67 patients into 5 groups to do 5 fold cross validation. Four groups were used to train decision tree algorithm and one group was used to test it. |
Who may qualify
- Ages
- 15 to 65 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients who had been diagnosed with IBD by gastroenterologist and achieved clinical remission after treatment with herbal medicine prescriptions
Key inclusion criteria (3 listed)
- Diagnosis of IBD by gastroenterologist
- Patients have achieved and maintained clinical remission of IBD symptoms after they took herbal prescriptions
- Patients have provided written informed consent
Key exclusion criteria (1 listed)
- Details regarding any of 25 symptoms were omitted
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Decision tree modelingA decision tree analysis was employed to explore the process of decision-making on types of pattern based on the existence or nonexistence of a symptom. At the end of tree presented is the proportion of patients who are categorised into each pattern. In this study, the classification was performed by applying the classification and regression tree (CART) algorithm using Scikit-learn package of Python, which performs a division using the Gini coefficient or the decrement of dispersion. The Gini coefficient is one of the tools for measuring entropy or diversity in each node and it measures the decrement by comparing the information entropy before and after separation. To avoid overfitting, the maximum number of leaf nodes was limited to four and the pruning method which complied with the principle of minimum description length was applied. | Other | Decision tree algorithm training/testing |
Primary outcome
Accuracy of pattern identification algorithm
Time frame: Oct 2015
Pattern identification algorithm was suggested using a decision tree method. Decision tree method was employed to explore the process of decision making on types of pattern based on clinical features of patients.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Mar 2020.
- Background: PubMed 19653289Bernstein CN, Fried M, Krabshuis JH, Cohen H, Eliakim R, Fedail S, Gearry R, Goh KL, Hamid S, Khan AG, LeMair AW, Malfertheiner, Ouyang Q, Rey JF, Sood A, Steinwurz F, Thomsen OO, Thomson A, Watermeyer G. World Gastroenterology Organization Practice Guidelines for the diagnosis and management of IBD in 2010. Inflamm Bowel Dis. 2010 Jan;16(1):112-24. doi: 10.1002/ibd.21048.
- Background: PubMed 18532990Panaccione R, Rutgeerts P, Sandborn WJ, Feagan B, Schreiber S, Ghosh S. Review article: treatment algorithms to maximize remission and minimize corticosteroid dependence in patients with inflammatory bowel disease. Aliment Pharmacol Ther. 2008 Sep 15;28(6):674-88. doi: 10.1111/j.1365-2036.2008.03753.x.
- Background: PubMed 24407360Abitbol V, Lahmek P, Buisson A, Olympie A, Poupardin C, Chaussade S, Lesgourgues B, Nahon S. Impact of complementary and alternative medicine on the quality of life in inflammatory bowel disease: results from a French national survey. Eur J Gastroenterol Hepatol. 2014 Mar;26(3):288-94. doi: 10.1097/MEG.0000000000000040.
- Background: PubMed 24938445Lee MS, Lee JA, Alraek T, Bian ZX, Birch S, Goto H, Jung J, Kao ST, Moon SK, Park B, Park KM, You S, Yun KJ, Zaslawski C. Current research and future directions in pattern identification: Results of an international symposium. Chin J Integr Med. 2016 Dec;22(12):947-955. doi: 10.1007/s11655-014-1833-3. Epub 2014 Jun 18.
- Background: PubMed 16866016Yu F, Takahashi T, Moriya J, Kawaura K, Yamakawa J, Kusaka K, Itoh T, Morimoto S, Yamaguchi N, Kanda T. Traditional Chinese medicine and Kampo: a review from the distant past for the future. J Int Med Res. 2006 May-Jun;34(3):231-9. doi: 10.1177/147323000603400301.
- Background: PubMed 22161304Liu B, Zhou X, Wang Y, Hu J, He L, Zhang R, Chen S, Guo Y. Data processing and analysis in real-world traditional Chinese medicine clinical data: challenges and approaches. Stat Med. 2012 Mar 30;31(7):653-60. doi: 10.1002/sim.4417. Epub 2011 Dec 9.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Hyangsook Lee, Doctor principal investigator
Kyunghee University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Inflammatory Bowel Diseases, run by Hyangsook Lee, KMD, PhD at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 6 Mar 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of pattern identification algorithm (time frame: Oct 2015). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04296500, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Mar 2020.