Acute AppendicitisNCT06175169
IA Model for Acute Appendicitis in CT
A study testing Information of Appendix (IA) model in people with Acute Appendicitis, run by Hallym University Medical Center at 1 site in South Korea.
Trial summary
the investigators's study group has developed a fully automated 3D convolutional neural network (CNN)-based diagnostic framework using information of appendix (IA) model to identify non-appendicitis and simple and complicated appendicitis on CT scan images based on the two-stage binary classification algorithm, as a… Read more about this study
- Who is it for?
- Ages 12 years and older
- Conditions the study is for
- Acute Appendicitis
- What is being tested?
- Information of Appendix (IA) model
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 568 participants
- Where
- 1 siteSouth Korea
- Study dates
- 4 Jul 2023to 31 Dec 2025
- 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 12 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- Inclusion criteria for broad eligibility were applied to reflect that the CT utilization rate in the emergency room has rapidly expanded, presumably in many other institutions where physicians maintain a reasonably sensitive standpoint in raising a clinical suspicion of appendicitis as a cause of abdominal pain and then use CT as an imaging test to confirm or rule out appendicitis.
- When the imaging protocol parameters were as follows: abdomen or pelvis (intravenous contrast, 2 mg/kg, maximum 160 mL), scan timing (portal venous phase), range (from 4 cm above the liver dome to 1 cm below the ischial tuberosity), radiation dose (tube potential, KVP from 100 to 120), pitch 1.75:1, and reconstruction (5 mm, cut slice for adults; 3 mm, cut slice for children under 12 years old), anonymized CT images of patients were referred to a randomized dataset.
Who cannot join
- Patients who did not fulfill the CT imaging protocol were excluded in detail as follows:
- Failure to meet the CT protocol criteria of this study: liver CT, biliary CT, etc. (if contrast phase was different); ureter CT, etc. (if contrast media was not used and the reconstruction method was different); non-enhanced CT (when contrast media was not used); and appendix CT or low-dose CT (when radiation dose was low).
- When the quality of the CT image is significantly reduced, as follows: when blurring occurs (motion artifact) or metal artifact (when internal fixation is performed due to spinal surgery).
- when it was evident from the medical record review that clinical information suggested that APCT was performed due to the suspicion of a condition other than appendicitis, as follows: suspected acute cholecystitis due to RUQ tenderness and Murphy's sign; suspected urolithiasis due to flank pain and gross hematuria; suspected pancreatitis due to a history of pancreatitis; alcohol abuse; and suspected gynecological diseases due to vaginal discharge. Suspected panperitonitis due to whole abdominal tenderness, rebound tenderness, and unstable vital signs. Patients with acute cholecystitis, ureteral stones, pancreatitis, or acute peritonitis due to small bowel or colon perforation were also excluded.
- Patients younger than 10 years were excluded. Adolescent patients from 11 to 18 years old were included in the study if the exclusion criteria were not applicable.
- diagnosed by ultrasound sonography vi) Patients who were transferred to the emergency department after a diagnosis of appendicitis at an outside hospital or ambulatory care were excluded.
- Patients with appendicitis who did not undergo surgical treatment because of the enrollment protocol of other ongoing studies.
- patients who had undergone an appendectomy
Trial locations and contacts
1 site in 1 country.
South Korea1 site, 1 recruiting
- Recruiting
Hallym University Medical Center
Anyang-si, Gyeonggi-do, 14068
About this study
Official title: Prospective Randomized Control Study for Exclusion of Negative Appendicitis; Deep Learning Model, Information of Appendix (IA) Versus Non-radiologists
the investigators's study group has developed a fully automated 3D convolutional neural network (CNN)-based diagnostic framework using information of appendix (IA) model to identify non-appendicitis and simple and complicated appendicitis on CT scan images based on the two-stage binary classification algorithm, as a clinician does for deciding treatment. The dataset was built from a large population of patients visiting emergency departments who underwent intravenous contrast-enhanced abdominopelvic CT examinations to evaluate abdominal pain in the right or lower quadrant area as the chief complaint. Recently, the IA model was externally validated using a dataset of multicenter institutions through data exfiltration. In this study, the investigators hypothesized that the IA model would show a comparable negative appendicitis rate of <10% non-inferior margins compared to non-radiologists with a shorter interpretation time in a prospectively randomized dataset.
Read the detailed description
Development of information of appendix model
- This study used a pretrained information of appendix (IA) model based on a fully automated diagnostic framework to predict three classes with probability and feature mapping: non-appendicitis, simple appendicitis, and complicated appendicitis.
The pipeline of IA model embedding parameters learned from the 3D CT image of 7,147 patients consisted of a two-stage binary algorithm connected to transfer learning with three 3D CNN models: DenseNet, EfficientNet, and ResNet.
- A two-stage binary algorithm for transfer learning was applied to learn inherent patterns from the 3D images of three true classes, as way a clinician does when deciding treatment for appendicitis. In the first step of the pipeline, a Stage 1 classification model was developed to identify non-appendicitis vs. appendicitis. In the second step of the pipeline, the Stage 2 classification model identified simple vs. complicated appendicitis from the data transferred with the trainable parameters learned in Stage 1.
Currently, the final IA model has been externally validated using a never-before-seen dataset from an outside institution with broad eligibility criteria for patients who visited the emergency room with abdominal pain and underwent abdominopelvic CT due to clinical suspicion of acute appendicitis.
Intervention This is a comparative study between human and IA models for the negative appendectomy rate (NAR) in non-, simple, and complicated appendicitis of a prospective randomized dataset, which was collected from CT scans of patients who visited the emergency room with acute abdominal pain. The participants in the control arm were ten non-radiologists.
The IA model is not allowed to impede the preoperative decision process of clinicians in ongoing clinical practice regarding the legal and ethical aspects of interrogating the diagnostic frameworks of the IA model on the mathematical reasoning for an outcome in terms of protecting human subjects.
Considering the minimal risk to human subjects and the ethical and legal aspects of artificial intelligence, the study design strictly allocates CT images to non-radiologists and IA models after completion of treatment for appendicitis with or without pathologic confirmation.
Data allocation and performance evaluation
- To compare the IA model to humans and non-radiologists equally, the interpretation condition for a non-radiologist should meet all criteria as follows: Only CT images without such variables as age, sex, and laboratory values should be randomly assigned; affiliation of both the non-radiologist and patient of the CT image should not be the same; the allocation ratio for non-appendicitis, simple, and complicated appendicitis assigned to the non-radiologist should be blinded; and the range of the CT image for interpretation should be identical to the range of the ROI for the appendix, which is generated by automatic localization of the IA model. To evaluate the diagnostic performance in humans, four test items were set up by a non-radiologist as follows: (1) visualization of the appendix; (2) exclusion of appendicitis; (3) complications; and (4) diagnosis of CT images.
The primary and secondary outcomes were assessed using the results of the four questions for the participants and the diagnostic performance of the IA model in the same dataset.
Ethical statement According to the "Guide to Utilizing Healthcare Data" and "Guide to Handling Pseudonym Information" of the Personal Information Protection Commission of the Ministry of Health and Welfare, data exfiltration with security assurance of the data archive was conducted with the approval of the Data Review Board at participating institutions. The central institutional review board (IRB) and IRBs of all participating institutions approved this study.
Sample size and statistical methods The investigators's prior study, which completed external validation of the IA model through data exfiltration of external institutions, showed an NAR of 12.4% in the external dataset. The standard reference value for non-radiologists for NAR has been reported as 10.5% ± 5%. Therefore, the acceptable difference in NAR between the IA model and non-radiologists for clinical application should be less than 10.0% of the non-inferiority margin.
The sample size for the randomized controlled dataset for a single institution was 263 assuming a 95% confidence interval, statistical power of 90%, two-tails sided α = 0.05, significance level = 0.05, and effect size = 0.2 at the Z-test. Two external institutions participated in this study. Due to data heterogeneity, normalization, and prevalence differences across regions between the two external institutions, the sample size was calculated for each institution. Therefore, assuming 263 patients per institution with a follow-up loss of 10%, the total sample size was 568.
- Conditions
- Acute Appendicitis
- Being tested
- Information of Appendix (IA) model
- Keywords
- deep learning
- Medical subject headings
- Appendicitis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Information of Appendix (IA) modelDevice
Deep learning model
| Study group | Role | What they receive |
|---|---|---|
| IA model | Active comparator | A fully automated diagnostic framework based on 3D-CNN model to predict non-appendicitis, simple and complicated appendicitis Receives: Device: Information of Appendix (IA) model |
| Non-radiologist | No intervention | Ten non-radiologists participated in this study. CT image same to IA model was allocated to radiologist randomly. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Negative appendectomy rate
Description: False positive rate = FP /FP +TN FP: false positive, TN true negative Assessment of outcomes: To evaluate diagnostic performance in humans, four test items for non-radiologists were set up as follows: (1) Appendix visualization: Can you find the location of the appendix? ① Yes ② No (2) Exclusion of appendicitis: Can appendicitis be excluded from the CT images? ① Yes (non-appendicitis) ② No (simple or complicated appendicitis) (3) If your choice is "no (simple or complicated appendicitis)," is appendicitis accompanied by complication? ① Yes (complicated appendicitis) ② No (simple appendicitis) (4) Finally, what is the radiologic diagnosis based on CT images in patients presenting with acute right or lower abdominal pain in the ER? ① Non-appendicitis ② simple appendicitis ③ complicated appendicitis The NAR was calculated as the primary endpoint using the outcomes of (2) the answer sheet of the non-radiologist and the Stage 1 IA model-yielding class.
Measured: Outcome measurement for four test items of non-radiologists will be assessed for an average of one year through study completion.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Dec 2023
- Study start4 Jul 2023
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Acute AppendicitisStudies 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
- Randomised
- Intervention model
- Parallel groups
- Model description
- A deep learning model, "Information of Appendix (IA)," based on a fully automated 3D convolutional neural network using transfer learning, has been developed to diagnose appendicitis in an abdominopelvic CT scan. The IA model was trained, validated, and tested on 3D-DenseNet, 3D-EfficientNet, and 3D-ResNet. External validation was completed through the data exfiltration of external institutions. The IA model yielded the probability of three classifications: non-appendicitis, simple appendicitis, and complicated appendicitis for patients who presented to the emergency room with abdominal pain and underwent abdominopelvic CT for suspected acute appendicitis. In this study, we confirmed that the intervention did not affect the included patients. Considering the minimal risk to human subjects and the ethical and legal aspects of artificial intelligence, we excluded the involvement of the IA model in all treatment processes.
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 568 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hallym University Medical Center
- Sponsor type
- University, hospital or other
- Collaborators
- Doheon Institute for Digital Innovation In Medicine, Medical A.I. Center of Hallym University
- Responsible party
- Principal investigator, Iltae Son, Hallym University Medical Center
- Investigators
- Iltae Son, Principal investigator, Hallym University Medical Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06175169
- Sponsor's protocol ID
- Project MHo4
- Status last verified
- Dec 2023
- First submitted
- 30 Nov 2023
- Last updated
- 18 Dec 2023
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 Dec 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Randomized Control Study for Exclusion of Negative Appendicitis; Deep Learning Model, Information of Appendix (IA) Versus Non-radiologists
- ClinicalTrials.gov ID
- NCT06175169
- Acronym / sponsor ID
- Project MHo4
- Conditions
- Acute Appendicitis
- Keywords
- deep learning
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 568 participants (estimated)
- Lead sponsor
- Hallym University Medical CenterUniversity, hospital or other
- Collaborators
- Doheon Institute for Digital Innovation In Medicine, Medical A.I. Center of Hallym University
- Study start
- 4 Jul 2023 (actual)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 18 Dec 2023 (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.
the investigators's study group has developed a fully automated 3D convolutional neural network (CNN)-based diagnostic framework using information of appendix (IA) model to identify non-appendicitis and simple and complicated appendicitis on CT scan images based on the two-stage binary classification algorithm, as a clinician does for deciding treatment. The dataset was built from a large population of patients visiting emergency departments who underwent intravenous contrast-enhanced abdominopelvic CT examinations to evaluate abdominal pain in the right or lower quadrant area as the chief complaint. Recently, the IA model was externally validated using a dataset of multicenter institutions through data exfiltration. In this study, the investigators hypothesized that the IA model would show a comparable negative appendicitis rate of <10% non-inferior margins compared to non-radiologists with a shorter interpretation time in a prospectively randomized dataset.
Detailed description
Development of information of appendix model
- This study used a pretrained information of appendix (IA) model based on a fully automated diagnostic framework to predict three classes with probability and feature mapping: non-appendicitis, simple appendicitis, and complicated appendicitis.
The pipeline of IA model embedding parameters learned from the 3D CT image of 7,147 patients consisted of a two-stage binary algorithm connected to transfer learning with three 3D CNN models: DenseNet, EfficientNet, and ResNet.
- A two-stage binary algorithm for transfer learning was applied to learn inherent patterns from the 3D images of three true classes, as way a clinician does when deciding treatment for appendicitis. In the first step of the pipeline, a Stage 1 classification model was developed to identify non-appendicitis vs. appendicitis. In the second step of the pipeline, the Stage 2 classification model identified simple vs. complicated appendicitis from the data transferred with the trainable parameters learned in Stage 1.
Currently, the final IA model has been externally validated using a never-before-seen dataset from an outside institution with broad eligibility criteria for patients who visited the emergency room with abdominal pain and underwent abdominopelvic CT due to clinical suspicion of acute appendicitis.
Intervention This is a comparative study between human and IA models for the negative appendectomy rate (NAR) in non-, simple, and complicated appendicitis of a prospective randomized dataset, which was collected from CT scans of patients who visited the emergency room with acute abdominal pain. The participants in the control arm were ten non-radiologists.
The IA model is not allowed to impede the preoperative decision process of clinicians in ongoing clinical practice regarding the legal and ethical aspects of interrogating the diagnostic frameworks of the IA model on the mathematical reasoning for an outcome in terms of protecting human subjects.
Considering the minimal risk to human subjects and the ethical and legal aspects of artificial intelligence, the study design strictly allocates CT images to non-radiologists and IA models after completion of treatment for appendicitis with or without pathologic confirmation.
Data allocation and performance evaluation
- To compare the IA model to humans and non-radiologists equally, the interpretation condition for a non-radiologist should meet all criteria as follows: Only CT images without such variables as age, sex, and laboratory values should be randomly assigned; affiliation of both the non-radiologist and patient of the CT image should not be the same; the allocation ratio for non-appendicitis, simple, and complicated appendicitis assigned to the non-radiologist should be blinded; and the range of the CT image for interpretation should be identical to the range of the ROI for the appendix, which is generated by automatic localization of the IA model. To evaluate the diagnostic performance in humans, four test items were set up by a non-radiologist as follows: (1) visualization of the appendix; (2) exclusion of appendicitis; (3) complications; and (4) diagnosis of CT images.
The primary and secondary outcomes were assessed using the results of the four questions for the participants and the diagnostic performance of the IA model in the same dataset.
Ethical statement According to the "Guide to Utilizing Healthcare Data" and "Guide to Handling Pseudonym Information" of the Personal Information Protection Commission of the Ministry of Health and Welfare, data exfiltration with security assurance of the data archive was conducted with the approval of the Data Review Board at participating institutions. The central institutional review board (IRB) and IRBs of all participating institutions approved this study.
Sample size and statistical methods The investigators's prior study, which completed external validation of the IA model through data exfiltration of external institutions, showed an NAR of 12.4% in the external dataset. The standard reference value for non-radiologists for NAR has been reported as 10.5% ± 5%. Therefore, the acceptable difference in NAR between the IA model and non-radiologists for clinical application should be less than 10.0% of the non-inferiority margin.
The sample size for the randomized controlled dataset for a single institution was 263 assuming a 95% confidence interval, statistical power of 90%, two-tails sided α = 0.05, significance level = 0.05, and effect size = 0.2 at the Z-test. Two external institutions participated in this study. Due to data heterogeneity, normalization, and prevalence differences across regions between the two external institutions, the sample size was calculated for each institution. Therefore, assuming 263 patients per institution with a follow-up loss of 10%, the total sample size was 568.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 2
- Model description
- A deep learning model, "Information of Appendix (IA)," based on a fully automated 3D convolutional neural network using transfer learning, has been developed to diagnose appendicitis in an abdominopelvic CT scan. The IA model was trained, validated, and tested on 3D-DenseNet, 3D-EfficientNet, and 3D-ResNet. External validation was completed through the data exfiltration of external institutions. The IA model yielded the probability of three classifications: non-appendicitis, simple appendicitis, and complicated appendicitis for patients who presented to the emergency room with abdominal pain and underwent abdominopelvic CT for suspected acute appendicitis. In this study, we confirmed that the intervention did not affect the included patients. Considering the minimal risk to human subjects and the ethical and legal aspects of artificial intelligence, we excluded the involvement of the IA model in all treatment processes.
| Arm | Type | Receives |
|---|---|---|
| IA model | Active comparator | Device: Information of Appendix (IA) modelA fully automated diagnostic framework based on 3D-CNN model to predict non-appendicitis, simple and complicated appendicitis |
| Non-radiologist | No intervention | –Ten non-radiologists participated in this study. CT image same to IA model was allocated to radiologist randomly. |
Who may qualify
- Ages
- 12 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (2 listed)
- Inclusion criteria for broad eligibility were applied to reflect that the CT utilization rate in the emergency room has rapidly expanded, presumably in many other institutions where physicians maintain a reasonably sensitive standpoint in raising a clinical suspicion of appendicitis as a cause of abdominal pain and then use CT as an imaging test to confirm or rule out appendicitis.
- When the imaging protocol parameters were as follows: abdomen or pelvis (intravenous contrast, 2 mg/kg, maximum 160 mL), scan timing (portal venous phase), range (from 4 cm above the liver dome to 1 cm below the ischial tuberosity), radiation dose (tube potential, KVP from 100 to 120), pitch 1.75:1, and reconstruction (5 mm, cut slice for adults; 3 mm, cut slice for children under 12 years old), anonymized CT images of patients were referred to a randomized dataset.
Key exclusion criteria (8 listed)
- Failure to meet the CT protocol criteria of this study: liver CT, biliary CT, etc. (if contrast phase was different); ureter CT, etc. (if contrast media was not used and the reconstruction method was different); non-enhanced CT (when contrast media was not used); and appendix CT or low-dose CT (when radiation dose was low).
- When the quality of the CT image is significantly reduced, as follows: when blurring occurs (motion artifact) or metal artifact (when internal fixation is performed due to spinal surgery).
- when it was evident from the medical record review that clinical information suggested that APCT was performed due to the suspicion of a condition other than appendicitis, as follows: suspected acute cholecystitis due to RUQ tenderness and Murphy's sign; suspected urolithiasis due to flank pain and gross hematuria; suspected pancreatitis due to a history of pancreatitis; alcohol abuse; and suspected gynecological diseases due to vaginal discharge. Suspected panperitonitis due to whole abdominal tenderness, rebound tenderness, and unstable vital signs. Patients with acute cholecystitis, ureteral stones, pancreatitis, or acute peritonitis due to small bowel or colon perforation were also excluded.
- Patients younger than 10 years were excluded. Adolescent patients from 11 to 18 years old were included in the study if the exclusion criteria were not applicable.
- diagnosed by ultrasound sonography vi) Patients who were transferred to the emergency department after a diagnosis of appendicitis at an outside hospital or ambulatory care were excluded.
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 |
|---|---|---|
| Information of Appendix (IA) modelDeep learning model | Device | IA model |
Primary outcome
Negative appendectomy rate
Time frame: Outcome measurement for four test items of non-radiologists will be assessed for an average of one year through study completion.
Description: False positive rate = FP /FP +TN FP: false positive, TN true negative Assessment of outcomes: To evaluate diagnostic performance in humans, four test items for non-radiologists were set up as follows: (1) Appendix visualization: Can you find the location of the appendix? ① Yes ② No (2) Exclusion of appendicitis: Can appendicitis be excluded from the CT images? ① Yes (non-appendicitis) ② No (simple or complicated appendicitis) (3) If your choice is "no (simple or complicated appendicitis)," is appendicitis accompanied by complication? ① Yes (complicated appendicitis) ② No (simple appendicitis) (4) Finally, what is the radiologic diagnosis based on CT images in patients presenting with acute right or lower abdominal pain in the ER? ① Non-appendicitis ② simple appendicitis ③ complicated appendicitis The NAR was calculated as the primary endpoint using the outcomes of (2) the answer sheet of the non-radiologist and the Stage 1 IA model-yielding class.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Dec 2023.
- Medical A.I. Center of Hallym UniversityLink provided by the sponsor.
- Individual participant dataSharing planned: No
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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.
Iltae Son principal investigator
Hallym University Medical Center
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Information of Appendix (IA) model in people with Acute Appendicitis, run by Hallym University Medical Center 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 2 inclusion and 8 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: status not confirmed, as of the sponsor's last update on 18 Dec 2023. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Iltae Son (+82-10-2373-4783, 1tae99@hanmail.net), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Negative appendectomy rate (time frame: Outcome measurement for four test items of non-radiologists will be assessed for an average of one year through study completion.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 18 Dec 2023; AzTrials last refreshed its copy on 28 Oct 2026.