DepressionDCATNCT01781013
Diabetes-Depression Care-management Adoption Trial
A study testing Technology-supported care in people with Depression and Diabetes Mellitus, run by University of Southern California at 8 sites in United States.
Trial summary
The specific aims of the proposed study are to: 1. Develop the innovative depression care management technology, including the speech recognition technology for automated monitoring and patient prompts over time, automatic integration of the responses into the patient registry, and evidence-based decision-support… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Depression, Diabetes Mellitus
- What is being tested?
- Technology-supported care
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,485 participants
- Where
- 8 sitesUnited States
- Study dates
- Jun 2010to Sep 2013
- Phase
- Interventional
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- age equal to or greater than 18 years
- receiving primary care at DHS safety net clinics
- having a current diagnosis of type 2 diabetes mellitus (non-gestational).
- have a working telephone or cellular phone.
Who cannot join
- current suicidal ideation;
- inability to speak either English or Spanish;
- a score of 2 or greater on the CAGE (4M) alcohol assessment;
- having schizophrenia, schizoaffective disorder, manic-depressive, or needing lithium;
- and cognitive impairment precluding ability to give informed consent or participating in the intervention, i.e., Short Portable Mental Status Questionnaire(SPMSQ) score of 6 or more errors.
- Provider and administrator inclusion criteria are: practicing or managing at one of the eight study sites; involved with diabetes or depression care
- No specific exclusion criteria will be applied to providers and administrators.
Trial locations and contacts
8 sites in 1 country.
8 sites in 1 country
United States8 sites
El Monte Comprehensive Health Center
El Monte, California, 91731
High Desert Comprehensive Health Center
Lancaster, California, 93536
Long Beach Comprehensive Health Center
Long Beach, California, 90813
H. Claude Hudson Comprehensive Health Center
Los Angeles, California, 90007
Roybal Comprehensive Health Center
Los Angeles, California, 90022
Olive View-UCLA Medical Center Diabetes Clinic
Sylmar, California, 91342
Mid-Valley Comprehensive Health Center
Van Nuys, California, 91405
Harbor Comprehensive Health Center
Wilmington, California, 90744
About this study
Official title: Care Management Technology to Facilitate Depression Care in Safety Net Diabetes Clinics
The specific aims of the proposed study are to:
- Develop the innovative depression care management technology, including the speech recognition technology for automated monitoring and patient prompts over time, automatic integration of the responses into the patient registry, and evidence-based decision-support algorithms for care actions;
- Conduct the quasi-experiment in eight Los Angeles County Department of Health Services (LAC-DHS) clinics to test the interventions;
- Use mixed-method evaluation to assess the extent of the implementation of the interventions, the acceptance to the providers and to the patients, and the impact on adoption of depression screening and treatment management over time, utilization, and cost of healthcare services, and patient health outcomes; and
- Conduct a cost-effectiveness analysis of the three study arms. Successful completion of the study will demonstrate which Comparative Effectiveness Research (CER) adoption strategies are successful and why, their comparative cost-effectiveness, as well as which strategies are successful under which circumstances to inform system-wide implementation of same.
Read more
Hypotheses of the Proposed Study
The following are the main hypotheses of the study:
- There will be statistically significant difference in the adoption of depression care screening and management over time among the three study groups. 1.1. The adoption rate will be Technology-supported care (TC) > Supported Care (SC) > Usual Care (UC).
- There will be statistically significant difference in the depression symptom reduction, and better functional status, and quality of life among the three study groups. 2.1. The difference between the TC and the SC will not be statistically significant, but both will be greater than the UC group.
- There will be statistically significant difference in the diabetes care process and outcomes among the three study groups. 3.1. The difference between the TC and the SC will not be statistically significant, but both will be greater than the UC group.
- There will also be statistically significant differences in healthcare utilization among the three study groups, with least utilization in the TC group where the greatest level of technology is applied.
- Of the three groups compared, the TC group will be the most cost-effective approach for accelerating adoption of the CER depression care results.
Read the detailed description
In addition, the study will aim to answer the secondary research questions listed below:
- What is medical provider satisfaction with the technology used in the TC (Technology Care) group?
- What is patient acceptance with the technology used in the TC group?
- What factors are identified by medical providers and clinic administrators as related to satisfaction, barriers, and sustaining the intervention post-trial?
- What are patients' reported satisfaction and facilitating factors and barriers to receipt and acceptance of depression care?
- Conditions
- Depression, Diabetes Mellitus
- Being tested
- Technology-supported care
- Keywords
- Depression screening, Depression monitoring, Chronic illness, Behavioral health, Care management, Automatic telephone assessment, Clinical decision support, Suicide alert
- Medical subject headings
- Depression, Diabetes Mellitus, Chronic Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Technology-supported careOther
The depression care-management technology that will interact with patients is the Automated Speech Recognition (ASR) for remote monitoring data collection. The ASR will use automated telephone calls to reach out to patients to repeat depression screening using PHQ-9, triggered either by calendar date or upcoming appointments, and to remind patients of their appointments in pre-determined time. In addition, the ASR will apply a structured script to conduct automatic follow-up with patients regarding their depression treatment adherence and side effects in order to provide data to help primary medical providers promptly and optimally adapt treatment. The ASR script will also include structured relapse prevention prompts. For providers and administrators, the depression care-management technology aimed to improve their workflow regarding depression care is Enhanced Disease Registry (EDR)..
| Study group | Role | What they receive |
|---|---|---|
| Technology-supported care | Experimental | This arm consists of Clinic Resource Management (CRM) clinics and serves as our intervention arm where the tested technology is implemented. Our overarching aim in these comparisons is to assess the potential effects of technology-facilitated depression symptom monitoring, relapse prevention, and medication adjustments and to examine depression care receipt and symptom improvement, patient/provider acceptance, and cost. Receives: Other: Technology-supported care |
| Supported-Care | No intervention | This arm consists of CRM (Clinic Resource Management) clinics and serves as one of the two control arms in the study. |
| Usual Care | No intervention | This arm consists of non-CRM (Clinic Resource Management) clinics and serves as one of the two control arms in the study. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Change from baseline in depression outcome at 6-months
Depression is measured using depression scales Patient Health Questionnaire (PHQ)-9. Major depression is classified as PHQ-9>=10.
Measured: 6-months from enrollment
Secondary outcomes (1)
Change from baseline in diabetes self-care score in 6 months
Diabetes self-care is measured using the Toolbert diabetes self-care scale.
Measured: 6 months from enrollment
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Jan 2013 (estimated)
- Study startJun 2010
- Primary completionSep 2013
- Study completionSep 2013
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (16)Papers the sponsor has linked to this study.
- Other trials for DepressionStudies of the same condition, open and under way.
- More trials run by University of Southern CaliforniaThe sponsor's other registered studies.
- 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 1,485 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Southern California
- Sponsor type
- University, hospital or other
- Collaborators
- Department of Health and Human Services
- Responsible party
- Principal investigator, Shinyi Wu, University of Southern California
- Investigators
- Shinyi Wu, PhD, Principal investigator, University of Southern California
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT01781013
- Sponsor's protocol ID
- RFA-AE-10-001
- Acronym
- DCAT
- Status last verified
- Dec 2014
- First submitted
- 29 Jan 2013
- Last updated
- 5 Dec 2014
- Results posted
- No
- Data monitoring committee
- Yes
Publications
Wells KB, Stewart A, Hays RD, Burnam MA, Rogers W, Daniels M, Berry S, Greenfield S, Ware J. The functioning and well-being of depressed patients. Results from the Medical Outcomes Study. JAMA. 1989 Aug 18;262(7):914-9.
PubMed 2754791Katon WJ. The comorbidity of diabetes mellitus and depression. Am J Med. 2008 Nov;121(11 Suppl 2):S8-15. doi: 10.1016/j.amjmed.2008.09.008.
PubMed 18954592Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care. 2001 Jun;24(6):1069-78. doi: 10.2337/diacare.24.6.1069.
PubMed 11375373Golden SH, Lazo M, Carnethon M, Bertoni AG, Schreiner PJ, Diez Roux AV, Lee HB, Lyketsos C. Examining a bidirectional association between depressive symptoms and diabetes. JAMA. 2008 Jun 18;299(23):2751-9. doi: 10.1001/jama.299.23.2751.
PubMed 18560002Lin EH, Katon W, Von Korff M, Rutter C, Simon GE, Oliver M, Ciechanowski P, Ludman EJ, Bush T, Young B. Relationship of depression and diabetes self-care, medication adherence, and preventive care. Diabetes Care. 2004 Sep;27(9):2154-60. doi: 10.2337/diacare.27.9.2154.
PubMed 15333477U.S. Preventive Services Task Force. Screening for depression in adults: U.S. preventive services task force recommendation statement. Ann Intern Med. 2009 Dec 1;151(11):784-92. doi: 10.7326/0003-4819-151-11-200912010-00006.
PubMed 19949144Anderson RJ, Gott BM, Sayuk GS, Freedland KE, Lustman PJ. Antidepressant pharmacotherapy in adults with type 2 diabetes: rates and predictors of initial response. Diabetes Care. 2010 Mar;33(3):485-9. doi: 10.2337/dc09-1466. Epub 2009 Dec 23.
PubMed 20032276Ell K, Xie B, Quon B, Quinn DI, Dwight-Johnson M, Lee PJ. Randomized controlled trial of collaborative care management of depression among low-income patients with cancer. J Clin Oncol. 2008 Sep 20;26(27):4488-96. doi: 10.1200/JCO.2008.16.6371.
PubMed 18802161Cabassa LJ, Hansen MC, Palinkas LA, Ell K. Azucar y nervios: explanatory models and treatment experiences of Hispanics with diabetes and depression. Soc Sci Med. 2008 Jun;66(12):2413-24. doi: 10.1016/j.socscimed.2008.01.054. Epub 2008 Mar 12.
PubMed 18339466Katon W, Robinson P, Von Korff M, Lin E, Bush T, Ludman E, Simon G, Walker E. A multifaceted intervention to improve treatment of depression in primary care. Arch Gen Psychiatry. 1996 Oct;53(10):924-32. doi: 10.1001/archpsyc.1996.01830100072009.
PubMed 8857869Jin H, Wu S. Text Messaging as a Screening Tool for Depression and Related Conditions in Underserved, Predominantly Minority Safety Net Primary Care Patients: Validity Study. J Med Internet Res. 2020 Mar 26;22(3):e17282. doi: 10.2196/17282.
PubMed 32213473Hay JW, Lee PJ, Jin H, Guterman JJ, Gross-Schulman S, Ell K, Wu S. Cost-Effectiveness of a Technology-Facilitated Depression Care Management Adoption Model in Safety-Net Primary Care Patients with Type 2 Diabetes. Value Health. 2018 May;21(5):561-568. doi: 10.1016/j.jval.2017.11.005. Epub 2017 Dec 6.
PubMed 29753353Ramirez M, Wu S, Jin H, Ell K, Gross-Schulman S, Myerchin Sklaroff L, Guterman J. Automated Remote Monitoring of Depression: Acceptance Among Low-Income Patients in Diabetes Disease Management. JMIR Ment Health. 2016 Jan 25;3(1):e6. doi: 10.2196/mental.4823.
PubMed 26810139Ell K, Katon W, Lee PJ, Guterman J, Wu S. Demographic, clinical and psychosocial factors identify a high-risk group for depression screening among predominantly Hispanic patients with Type 2 diabetes in safety net care. Gen Hosp Psychiatry. 2015 Sep-Oct;37(5):414-9. doi: 10.1016/j.genhosppsych.2015.05.010. Epub 2015 May 29.
PubMed 26059979Wu S, Vidyanti I, Liu P, Hawkins C, Ramirez M, Guterman J, Gross-Schulman S, Sklaroff LM, Ell K. Patient-centered technological assessment and monitoring of depression for low-income patients. J Ambul Care Manage. 2014 Apr-Jun;37(2):138-47. doi: 10.1097/JAC.0000000000000027.
PubMed 24525531
Study at a glance
From the registry record, last updated by the sponsor on 5 Dec 2014. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Care Management Technology to Facilitate Depression Care in Safety Net Diabetes Clinics
- ClinicalTrials.gov ID
- NCT01781013
- Acronym / sponsor ID
- DCAT / RFA-AE-10-001
- Conditions
- Depression, Diabetes Mellitus
- Keywords
- Depression screening, Depression monitoring, Chronic illness, Behavioral health, Care management, Automatic telephone assessment, Clinical decision support, Suicide alert
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 1,485 participants (actual)
- Lead sponsor
- University of Southern CaliforniaUniversity, hospital or other
- Collaborators
- Department of Health and Human Services
- Study start
- Jun 2010
- Primary completion
- Sep 2013 (actual)
- Study completion
- Sep 2013 (actual)
- First posted
- 31 Jan 2013 (estimated)
- 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.
The specific aims of the proposed study are to:
- Develop the innovative depression care management technology, including the speech recognition technology for automated monitoring and patient prompts over time, automatic integration of the responses into the patient registry, and evidence-based decision-support algorithms for care actions;
- Conduct the quasi-experiment in eight Los Angeles County Department of Health Services (LAC-DHS) clinics to test the interventions;
- Use mixed-method evaluation to assess the extent of the implementation of the interventions, the acceptance to the providers and to the patients, and the impact on adoption of depression screening and treatment management over time, utilization, and cost of healthcare services, and patient health outcomes; and
- Conduct a cost-effectiveness analysis of the three study arms. Successful completion of the study will demonstrate which Comparative Effectiveness Research (CER) adoption strategies are successful and why, their comparative cost-effectiveness, as well as which strategies are successful under which circumstances to inform system-wide implementation of same.
Hypotheses of the Proposed Study
The following are the main hypotheses of the study:
- There will be statistically significant difference in the adoption of depression care screening and management over time among the three study groups. 1.1. The adoption rate will be Technology-supported care (TC) > Supported Care (SC) > Usual Care (UC).
- There will be statistically significant difference in the depression symptom reduction, and better functional status, and quality of life among the three study groups. 2.1. The difference between the TC and the SC will not be statistically significant, but both will be greater than the UC group.
- There will be statistically significant difference in the diabetes care process and outcomes among the three study groups. 3.1. The difference between the TC and the SC will not be statistically significant, but both will be greater than the UC group.
- There will also be statistically significant differences in healthcare utilization among the three study groups, with least utilization in the TC group where the greatest level of technology is applied.
- Of the three groups compared, the TC group will be the most cost-effective approach for accelerating adoption of the CER depression care results.
Detailed description
In addition, the study will aim to answer the secondary research questions listed below:
- What is medical provider satisfaction with the technology used in the TC (Technology Care) group?
- What is patient acceptance with the technology used in the TC group?
- What factors are identified by medical providers and clinic administrators as related to satisfaction, barriers, and sustaining the intervention post-trial?
- What are patients' reported satisfaction and facilitating factors and barriers to receipt and acceptance of depression care?
Study design
- Primary purpose
- Health services research
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Technology-supported care | Experimental | Other: Technology-supported careThis arm consists of Clinic Resource Management (CRM) clinics and serves as our intervention arm where the tested technology is implemented. Our overarching aim in these comparisons is to assess the potential effects of technology-facilitated depression symptom monitoring, relapse prevention, and medication adjustments and to examine depression care receipt and symptom improvement, patient/provider acceptance, and cost. |
| Supported-Care | No intervention | –This arm consists of CRM (Clinic Resource Management) clinics and serves as one of the two control arms in the study. |
| Usual Care | No intervention | –This arm consists of non-CRM (Clinic Resource Management) clinics and serves as one of the two control arms in the study. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- age equal to or greater than 18 years
- receiving primary care at DHS safety net clinics
- having a current diagnosis of type 2 diabetes mellitus (non-gestational).
- have a working telephone or cellular phone.
Key exclusion criteria (7 listed)
- current suicidal ideation;
- inability to speak either English or Spanish;
- a score of 2 or greater on the CAGE (4M) alcohol assessment;
- having schizophrenia, schizoaffective disorder, manic-depressive, or needing lithium;
- and cognitive impairment precluding ability to give informed consent or participating in the intervention, i.e., Short Portable Mental Status Questionnaire(SPMSQ) score of 6 or more errors.
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 |
|---|---|---|
| Technology-supported careThe depression care-management technology that will interact with patients is the Automated Speech Recognition (ASR) for remote monitoring data collection. The ASR will use automated telephone calls to reach out to patients to repeat depression screening using PHQ-9, triggered either by calendar date or upcoming appointments, and to remind patients of their appointments in pre-determined time. In addition, the ASR will apply a structured script to conduct automatic follow-up with patients regarding their depression treatment adherence and side effects in order to provide data to help primary medical providers promptly and optimally adapt treatment. The ASR script will also include structured relapse prevention prompts. For providers and administrators, the depression care-management technology aimed to improve their workflow regarding depression care is Enhanced Disease Registry (EDR).. | Other | Technology-supported care |
Primary outcome
Change from baseline in depression outcome at 6-months
Time frame: 6-months from enrollment
Depression is measured using depression scales Patient Health Questionnaire (PHQ)-9. Major depression is classified as PHQ-9>=10.
1 secondary outcome
Change from baseline in diabetes self-care score in 6 months
Time frame: 6 months from enrollment
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 5 Dec 2014.
- Background: PubMed 2754791Wells KB, Stewart A, Hays RD, Burnam MA, Rogers W, Daniels M, Berry S, Greenfield S, Ware J. The functioning and well-being of depressed patients. Results from the Medical Outcomes Study. JAMA. 1989 Aug 18;262(7):914-9.
- Background: PubMed 18954592Katon WJ. The comorbidity of diabetes mellitus and depression. Am J Med. 2008 Nov;121(11 Suppl 2):S8-15. doi: 10.1016/j.amjmed.2008.09.008.
- Background: PubMed 11375373Anderson RJ, Freedland KE, Clouse RE, Lustman PJ. The prevalence of comorbid depression in adults with diabetes: a meta-analysis. Diabetes Care. 2001 Jun;24(6):1069-78. doi: 10.2337/diacare.24.6.1069.
- Background: PubMed 18560002Golden SH, Lazo M, Carnethon M, Bertoni AG, Schreiner PJ, Diez Roux AV, Lee HB, Lyketsos C. Examining a bidirectional association between depressive symptoms and diabetes. JAMA. 2008 Jun 18;299(23):2751-9. doi: 10.1001/jama.299.23.2751.
- Background: PubMed 15333477Lin EH, Katon W, Von Korff M, Rutter C, Simon GE, Oliver M, Ciechanowski P, Ludman EJ, Bush T, Young B. Relationship of depression and diabetes self-care, medication adherence, and preventive care. Diabetes Care. 2004 Sep;27(9):2154-60. doi: 10.2337/diacare.27.9.2154.
- Background: PubMed 19949144U.S. Preventive Services Task Force. Screening for depression in adults: U.S. preventive services task force recommendation statement. Ann Intern Med. 2009 Dec 1;151(11):784-92. doi: 10.7326/0003-4819-151-11-200912010-00006.
- Background: PubMed 20032276Anderson RJ, Gott BM, Sayuk GS, Freedland KE, Lustman PJ. Antidepressant pharmacotherapy in adults with type 2 diabetes: rates and predictors of initial response. Diabetes Care. 2010 Mar;33(3):485-9. doi: 10.2337/dc09-1466. Epub 2009 Dec 23.
- Background: PubMed 18802161Ell K, Xie B, Quon B, Quinn DI, Dwight-Johnson M, Lee PJ. Randomized controlled trial of collaborative care management of depression among low-income patients with cancer. J Clin Oncol. 2008 Sep 20;26(27):4488-96. doi: 10.1200/JCO.2008.16.6371.
- Background: PubMed 18339466Cabassa LJ, Hansen MC, Palinkas LA, Ell K. Azucar y nervios: explanatory models and treatment experiences of Hispanics with diabetes and depression. Soc Sci Med. 2008 Jun;66(12):2413-24. doi: 10.1016/j.socscimed.2008.01.054. Epub 2008 Mar 12.
- Background: PubMed 8857869Katon W, Robinson P, Von Korff M, Lin E, Bush T, Ludman E, Simon G, Walker E. A multifaceted intervention to improve treatment of depression in primary care. Arch Gen Psychiatry. 1996 Oct;53(10):924-32. doi: 10.1001/archpsyc.1996.01830100072009.
- Derived from this study: PubMed 32213473Jin H, Wu S. Text Messaging as a Screening Tool for Depression and Related Conditions in Underserved, Predominantly Minority Safety Net Primary Care Patients: Validity Study. J Med Internet Res. 2020 Mar 26;22(3):e17282. doi: 10.2196/17282.
- Derived from this study: PubMed 29753353Hay JW, Lee PJ, Jin H, Guterman JJ, Gross-Schulman S, Ell K, Wu S. Cost-Effectiveness of a Technology-Facilitated Depression Care Management Adoption Model in Safety-Net Primary Care Patients with Type 2 Diabetes. Value Health. 2018 May;21(5):561-568. doi: 10.1016/j.jval.2017.11.005. Epub 2017 Dec 6.
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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.
Shinyi Wu, PhD principal investigator
University of Southern California
8 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Technology-supported care in people with Depression and Diabetes Mellitus, run by University of Southern California at 8 sites in United States.
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 4 inclusion and 7 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 5 Dec 2014. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 8 sites in United States. 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?
Change from baseline in depression outcome at 6-months (time frame: 6-months from enrollment). 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 NCT01781013, which also lists 16 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 5 Dec 2014; AzTrials last refreshed its copy on 2 Nov 2026.