Skin DiseasesNCT04562168
Using AI as a Diagnostic Decision Support Tool to Help the Diagnosis of Skin Disease in Primary Healthcare in Catalonia
A study testing Autoderm® dermatology search engine (ML model) testing in people with Skin Diseases, run by Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina at 1 site in Spain.
Trial summary
Background: Dermatological conditions are a relevant health problem. Machine learning models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, specially for skin cancer detection and classification. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Skin Diseases
- What is being tested?
- Autoderm® dermatology search engine (ML model) testing
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- 1 siteSpain
- Study dates
- 15 Jan 2021to 31 Dec 2021
- 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
- Patients who have cutaneous disease reason-for-visit.
- Patients who provide written informed consent.
- Patients who are 18 years of age or older.
Who cannot join
- Patients with advanced dementia.
- Patients with a cutaneous lesion which can't be photographed with a smartphone and images with poor quality.
- Patients who have conditions associated with risk of poor protocol compliance.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Using Artificial Intelligence as a Diagnostic Decision Support Tool to Help the Diagnosis of Skin Disease in Primary Healthcare in Catalonia
Background: Dermatological conditions are a relevant health problem. Machine learning models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, specially for skin cancer detection and classification.
Objective: The objective of this study is to perform a prospective validation of an image analysis ML model, which is capable of screening 44 different skin disease types, comparing its diagnostic capacity with that of General Practitioners (GPs) and dermatologists.
Methods: In this prospective study 100 consecutive patients who visit a participant GP with a skin problem in central Catalonia will be recruited, data collection is planned to last 7 months. Skin diseases anonymized pictures will be taken and introduced in the ML model interface, which will return top 5 accuracy diagnosis. The same image will be also sent as a teledermatology consultation, following the current workflow. GP, ML model and dermatologist/s assessments will be compared to calculate the precision, sensitivity, specificity and accuracy of the ML model.
Read the detailed description
A secure anonymous stand alone web interface that is compatible to any mobile device will be integrated with the Autoderm API. The study conducted in this project will consist in a prospective study aimed to evaluate the ML model performance, comparing its diagnostic capacity with GPs and dermatologists.
To conduct the study the following procedure will be executed until the required number of samples is reached:
- A suitable patient with skin concern is asked to participate and sign the patient's study agreement.
- GP will diagnose the skin condition.
- GP (or nurse) will take one good quality image of the skin condition.
- GP will send the photograph as a teledermatology consultation following the current workflow.
- The image is entered in the Autoderm ML interface.
- Dermatologist will diagnose the skin condition.
The study will be conducted in primary care centers managed by the Catalan Health Institute. Participant PCP will be located in rural and metropolitan areas in Central Catalonia, which includes the regions of Anoia, Bages, Moianès, Berguedà and Osona. The reference population included in the study will be about 512,050. The recruitment of prospective subjects will consist on a consecutive basis.
General practitioners will collect data from consecutive patients who meet the inclusion criteria after obtaining written informed consent. Collected data will be reported exclusively in case report form (attached at Annex V and VI).
The GP will diagnose the skin condition and will fill the "Face-to-face assessment by GP". For each patient, the GP using a smartphone camera will take a close up good quality image of the skin problem. The image will be anonymous and it will be not possible to identify patients. The GP will use the Autoderm ML interface to upload the anonymized image and will fill the "Assessment provided by the ML model" questionnaire with the top 3 diagnoses generated by the ML model.
In order to get a second opinion, the GP will incorporate the anonymized image and an accurate description of the skin lesion into the patient's medical history following the current teledermatology flow. The GP will fill "Assessment by teledermatology" questionnaire after receiving the information, being response time about 2-7 days.
In case of dermatology referral, the GP will fill "Assessment by in person dermatologist", by accessing electronic health records as they become available, being the average waiting time for referral from 30 to 90 days.
Questionnaire case number will be the same for all questionnaires and it will not be possible to identify the patient, as case number will be predefined before the initiation of the data collection phase.
To compare the performance of the ML model with that of the GPs and dermatologists, it will be required a sample size of 100 images of skin diseases from patients who meet the inclusion criteria. The proposed sample size is based on sample size calculation used in similar research.
- Conditions
- Skin Diseases
- Being tested
- Autoderm® dermatology search engine (ML model) testing
- Keywords
- Machine Learning, Artificial Intelligence, Data accuracy, Computed Assisted Diagnosis, Neural Network Computer
- Medical subject headings
- Skin Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Autoderm® dermatology search engine (ML model) testingDiagnostic test
GP using a smartphone camera will take an image of the skin problem and will use the Autoderm ML interface to upload the anonymized image. The obtained predicted diagnosis will be recorded in case report form.
| Study group | Role | What they receive |
|---|---|---|
| Diagnostic Test: ML model | Experimental | The diagnostic capacity of the ML model will be compared with that of the general practitioners and with dermatologist. Receives: Diagnostic Test: Autoderm® dermatology search engine (ML model) testing |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of the ML model
True positive rate of the ML model
Measured: 1 year
Specificity of the ML model
True negative rate of the ML model
Measured: 1 year
Accuracy of the ML model
Ratio of number of correct predictions to the total number of input samples
Measured: 1 year
Area under the receiver operating characteristic curve of the ML model
Diagnostic ability of the ML model
Measured: 1 year
Secondary outcomes (1)
Rate of the eligible participants who agree to participate in the study
Frequency of patients who agree to participate in the clinical trial and are eligible.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Sep 2020
- Study start15 Jan 2021
- Primary completion31 Dec 2021
- Study completion31 Dec 2021
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 Skin 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 100 participants (actual)
Sponsor and investigators
- Lead sponsor
- Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina
- Sponsor type
- University, hospital or other
- Collaborators
- iDoc24, Institut Català de la Salut
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04562168
- Sponsor's protocol ID
- P20/159-P
- Status last verified
- Mar 2022
- First submitted
- 28 Aug 2020
- Last updated
- 5 May 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The protocol will be published.
Publications
Lim HW, Collins SAB, Resneck JS Jr, Bolognia JL, Hodge JA, Rohrer TA, Van Beek MJ, Margolis DJ, Sober AJ, Weinstock MA, Nerenz DR, Smith Begolka W, Moyano JV. The burden of skin disease in the United States. J Am Acad Dermatol. 2017 May;76(5):958-972.e2. doi: 10.1016/j.jaad.2016.12.043. Epub 2017 Mar 1.
PubMed 28259441Schofield JK, Fleming D, Grindlay D, Williams H. Skin conditions are the commonest new reason people present to general practitioners in England and Wales. Br J Dermatol. 2011 Nov;165(5):1044-50. doi: 10.1111/j.1365-2133.2011.10464.x. Epub 2011 Sep 22.
PubMed 21692764Dokotor.se [Internet]. Survey Telemedicine statistics Dokotor.se, the % of queries that are dermatology related 2019 [cited 2019]
Activitat assistencial de la xarxa sanitària de Catalunya, any 2012: registre del conjunt mínim bàsic de dades (CMBD). Barcelona: Departament de Salut. 2013.
Lowell BA, Froelich CW, Federman DG, Kirsner RS. Dermatology in primary care: Prevalence and patient disposition. J Am Acad Dermatol. 2001 Aug;45(2):250-5. doi: 10.1067/mjd.2001.114598.
PubMed 11464187Porta N, San Juan J, Grasa MP, Simal E, Ara M, Querol MA. [Diagnostic agreement between primary care physicians and dermatologists in the health area of a referral hospital]. Actas Dermosifiliogr. 2008 Apr;99(3):207-12. Spanish.
PubMed 18358196Lopez Segui F, Franch Parella J, Girones Garcia X, Mendioroz Pena J, Garcia Cuyas F, Adroher Mas C, Garcia-Altes A, Vidal-Alaball J. A Cost-Minimization Analysis of a Medical Record-based, Store and Forward and Provider-to-provider Telemedicine Compared to Usual Care in Catalonia: More Agile and Efficient, Especially for Users. Int J Environ Res Public Health. 2020 Mar 18;17(6):2008. doi: 10.3390/ijerph17062008.
PubMed 32197434Borve A, Dahlen Gyllencreutz J, Terstappen K, Johansson Backman E, Aldenbratt A, Danielsson M, Gillstedt M, Sandberg C, Paoli J. Smartphone teledermoscopy referrals: a novel process for improved triage of skin cancer patients. Acta Derm Venereol. 2015 Feb;95(2):186-90. doi: 10.2340/00015555-1906.
PubMed 24923283Ferrer RT, Bezares AP, Manes AL, Mas AV, Gutierrez IT, Llado CN, Estaras GM. [Diagnostic reliability of an asynchronous teledermatology consultation]. Aten Primaria. 2009 Oct;41(10):552-7. doi: 10.1016/j.aprim.2008.11.012. Epub 2009 Jun 5. Spanish.
PubMed 19500880Gomolin A, Netchiporouk E, Gniadecki R, Litvinov IV. Artificial Intelligence Applications in Dermatology: Where Do We Stand? Front Med (Lausanne). 2020 Mar 31;7:100. doi: 10.3389/fmed.2020.00100. eCollection 2020.
PubMed 32296706Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Feb 2;542(7639):115-118. doi: 10.1038/nature21056. Epub 2017 Jan 25.
PubMed 28117445Liu Y, Jain A, Eng C, Way DH, Lee K, Bui P, Kanada K, de Oliveira Marinho G, Gallegos J, Gabriele S, Gupta V, Singh N, Natarajan V, Hofmann-Wellenhof R, Corrado GS, Peng LH, Webster DR, Ai D, Huang SJ, Liu Y, Dunn RC, Coz D. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020 Jun;26(6):900-908. doi: 10.1038/s41591-020-0842-3. Epub 2020 May 18.
PubMed 32424212Kamulegeya LH, Okello M, Bwanika JM, Musinguzi D, Lubega W, Rusoke D, et al. Using artificial intelligence on dermatology conditions in Uganda: A case for diversity in training data sets for machine learning. bioRxiv [Internet]. 2019 Jan 1;826057. Available from: http://biorxiv.org/content/early/2019/10/31/826057.abstract .
Evaluation of the diagnostic accuracy of an online artificial intelligence app for skin disease diagnosis. Alexander Larson, Degree Project in Medicine, Sahlgrenska University Hospital Department of Dermatology and Venereology, Gothenburg, Sweden 2018.
Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, Schilling B, Haferkamp S, Schadendorf D, Holland-Letz T, Utikal JS, von Kalle C; Collaborators. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J Cancer. 2019 May;113:47-54. doi: 10.1016/j.ejca.2019.04.001. Epub 2019 Apr 10.
PubMed 30981091
Study at a glance
From the registry record, last updated by the sponsor on 5 May 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using Artificial Intelligence as a Diagnostic Decision Support Tool to Help the Diagnosis of Skin Disease in Primary Healthcare in Catalonia
- ClinicalTrials.gov ID
- NCT04562168
- Acronym / sponsor ID
- P20/159-P
- Conditions
- Skin Diseases
- Keywords
- Machine Learning, Artificial Intelligence, Data accuracy, Computed Assisted Diagnosis, Neural Network Computer
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 100 participants (actual)
- Lead sponsor
- Fundacio d'Investigacio en Atencio Primaria Jordi Gol i GurinaUniversity, hospital or other
- Collaborators
- iDoc24, Institut Català de la Salut
- Study start
- 15 Jan 2021 (actual)
- Primary completion
- 31 Dec 2021 (actual)
- Study completion
- 31 Dec 2021 (actual)
- First posted
- 24 Sep 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.
Background: Dermatological conditions are a relevant health problem. Machine learning models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, specially for skin cancer detection and classification.
Objective: The objective of this study is to perform a prospective validation of an image analysis ML model, which is capable of screening 44 different skin disease types, comparing its diagnostic capacity with that of General Practitioners (GPs) and dermatologists.
Methods: In this prospective study 100 consecutive patients who visit a participant GP with a skin problem in central Catalonia will be recruited, data collection is planned to last 7 months. Skin diseases anonymized pictures will be taken and introduced in the ML model interface, which will return top 5 accuracy diagnosis. The same image will be also sent as a teledermatology consultation, following the current workflow. GP, ML model and dermatologist/s assessments will be compared to calculate the precision, sensitivity, specificity and accuracy of the ML model.
Detailed description
A secure anonymous stand alone web interface that is compatible to any mobile device will be integrated with the Autoderm API. The study conducted in this project will consist in a prospective study aimed to evaluate the ML model performance, comparing its diagnostic capacity with GPs and dermatologists.
To conduct the study the following procedure will be executed until the required number of samples is reached:
- A suitable patient with skin concern is asked to participate and sign the patient's study agreement.
- GP will diagnose the skin condition.
- GP (or nurse) will take one good quality image of the skin condition.
- GP will send the photograph as a teledermatology consultation following the current workflow.
- The image is entered in the Autoderm ML interface.
- Dermatologist will diagnose the skin condition.
The study will be conducted in primary care centers managed by the Catalan Health Institute. Participant PCP will be located in rural and metropolitan areas in Central Catalonia, which includes the regions of Anoia, Bages, Moianès, Berguedà and Osona. The reference population included in the study will be about 512,050. The recruitment of prospective subjects will consist on a consecutive basis.
General practitioners will collect data from consecutive patients who meet the inclusion criteria after obtaining written informed consent. Collected data will be reported exclusively in case report form (attached at Annex V and VI).
The GP will diagnose the skin condition and will fill the "Face-to-face assessment by GP". For each patient, the GP using a smartphone camera will take a close up good quality image of the skin problem. The image will be anonymous and it will be not possible to identify patients. The GP will use the Autoderm ML interface to upload the anonymized image and will fill the "Assessment provided by the ML model" questionnaire with the top 3 diagnoses generated by the ML model.
In order to get a second opinion, the GP will incorporate the anonymized image and an accurate description of the skin lesion into the patient's medical history following the current teledermatology flow. The GP will fill "Assessment by teledermatology" questionnaire after receiving the information, being response time about 2-7 days.
In case of dermatology referral, the GP will fill "Assessment by in person dermatologist", by accessing electronic health records as they become available, being the average waiting time for referral from 30 to 90 days.
Questionnaire case number will be the same for all questionnaires and it will not be possible to identify the patient, as case number will be predefined before the initiation of the data collection phase.
To compare the performance of the ML model with that of the GPs and dermatologists, it will be required a sample size of 100 images of skin diseases from patients who meet the inclusion criteria. The proposed sample size is based on sample size calculation used in similar research.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Diagnostic Test: ML model | Experimental | Diagnostic Test: Autoderm® dermatology search engine (ML model) testingThe diagnostic capacity of the ML model will be compared with that of the general practitioners and with dermatologist. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Patients who have cutaneous disease reason-for-visit.
- Patients who provide written informed consent.
- Patients who are 18 years of age or older.
Key exclusion criteria (3 listed)
- Patients with advanced dementia.
- Patients with a cutaneous lesion which can't be photographed with a smartphone and images with poor quality.
- Patients who have conditions associated with risk of poor protocol compliance.
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 |
|---|---|---|
| Autoderm® dermatology search engine (ML model) testingGP using a smartphone camera will take an image of the skin problem and will use the Autoderm ML interface to upload the anonymized image. The obtained predicted diagnosis will be recorded in case report form. | Diagnostic test | Diagnostic Test: ML model |
Primary outcome
Sensitivity of the ML model
Time frame: 1 year
True positive rate of the ML model
Specificity of the ML model
Time frame: 1 year
True negative rate of the ML model
Accuracy of the ML model
Time frame: 1 year
Ratio of number of correct predictions to the total number of input samples
Area under the receiver operating characteristic curve of the ML model
Time frame: 1 year
Diagnostic ability of the ML model
1 secondary outcome
Rate of the eligible participants who agree to participate in the study
Time frame: 1 year
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 May 2022.
- Results publication: PubMed 28259441Lim HW, Collins SAB, Resneck JS Jr, Bolognia JL, Hodge JA, Rohrer TA, Van Beek MJ, Margolis DJ, Sober AJ, Weinstock MA, Nerenz DR, Smith Begolka W, Moyano JV. The burden of skin disease in the United States. J Am Acad Dermatol. 2017 May;76(5):958-972.e2. doi: 10.1016/j.jaad.2016.12.043. Epub 2017 Mar 1.
- Results publication: PubMed 21692764Schofield JK, Fleming D, Grindlay D, Williams H. Skin conditions are the commonest new reason people present to general practitioners in England and Wales. Br J Dermatol. 2011 Nov;165(5):1044-50. doi: 10.1111/j.1365-2133.2011.10464.x. Epub 2011 Sep 22.
- Results publicationDokotor.se [Internet]. Survey Telemedicine statistics Dokotor.se, the % of queries that are dermatology related 2019 [cited 2019]
- Results publicationActivitat assistencial de la xarxa sanitària de Catalunya, any 2012: registre del conjunt mínim bàsic de dades (CMBD). Barcelona: Departament de Salut. 2013.
- Results publication: PubMed 11464187Lowell BA, Froelich CW, Federman DG, Kirsner RS. Dermatology in primary care: Prevalence and patient disposition. J Am Acad Dermatol. 2001 Aug;45(2):250-5. doi: 10.1067/mjd.2001.114598.
- Results publication: PubMed 18358196Porta N, San Juan J, Grasa MP, Simal E, Ara M, Querol MA. [Diagnostic agreement between primary care physicians and dermatologists in the health area of a referral hospital]. Actas Dermosifiliogr. 2008 Apr;99(3):207-12. Spanish.
- Results publication: PubMed 32197434Lopez Segui F, Franch Parella J, Girones Garcia X, Mendioroz Pena J, Garcia Cuyas F, Adroher Mas C, Garcia-Altes A, Vidal-Alaball J. A Cost-Minimization Analysis of a Medical Record-based, Store and Forward and Provider-to-provider Telemedicine Compared to Usual Care in Catalonia: More Agile and Efficient, Especially for Users. Int J Environ Res Public Health. 2020 Mar 18;17(6):2008. doi: 10.3390/ijerph17062008.
- Results publication: PubMed 24923283Borve A, Dahlen Gyllencreutz J, Terstappen K, Johansson Backman E, Aldenbratt A, Danielsson M, Gillstedt M, Sandberg C, Paoli J. Smartphone teledermoscopy referrals: a novel process for improved triage of skin cancer patients. Acta Derm Venereol. 2015 Feb;95(2):186-90. doi: 10.2340/00015555-1906.
- Results publication: PubMed 19500880Ferrer RT, Bezares AP, Manes AL, Mas AV, Gutierrez IT, Llado CN, Estaras GM. [Diagnostic reliability of an asynchronous teledermatology consultation]. Aten Primaria. 2009 Oct;41(10):552-7. doi: 10.1016/j.aprim.2008.11.012. Epub 2009 Jun 5. Spanish.
- Results publication: PubMed 32296706Gomolin A, Netchiporouk E, Gniadecki R, Litvinov IV. Artificial Intelligence Applications in Dermatology: Where Do We Stand? Front Med (Lausanne). 2020 Mar 31;7:100. doi: 10.3389/fmed.2020.00100. eCollection 2020.
- Results publication: PubMed 28117445Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017 Feb 2;542(7639):115-118. doi: 10.1038/nature21056. Epub 2017 Jan 25.
- Results publication: PubMed 32424212Liu Y, Jain A, Eng C, Way DH, Lee K, Bui P, Kanada K, de Oliveira Marinho G, Gallegos J, Gabriele S, Gupta V, Singh N, Natarajan V, Hofmann-Wellenhof R, Corrado GS, Peng LH, Webster DR, Ai D, Huang SJ, Liu Y, Dunn RC, Coz D. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020 Jun;26(6):900-908. doi: 10.1038/s41591-020-0842-3. Epub 2020 May 18.
- Individual participant dataSharing planned: Yes. The protocol will be published.
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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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Autoderm® dermatology search engine (ML model) testing in people with Skin Diseases, run by Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina at 1 site in Spain.
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 3 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 May 2022. 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 Spain. 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?
Sensitivity of the ML model (time frame: 1 year). The study lists 4 primary outcomes in all. 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 NCT04562168, 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 May 2022.