NAFLD - Nonalcoholic Fatty Liver DiseaseNCT05218538
Liver Cirrhosis Diagnosis Prioritizing Algorithm Based on Electronic Health Records.
An observational study of NAFLD - Nonalcoholic Fatty Liver Disease and Fibrosis, Liver, run by HaEmek Medical Center, Israel at 1 site in Israel.
Trial summary
The investigators use machine learning capabilities on massive electronic health records for the purpose of developing a model that prioritizes individuals at high risk of progressing to liver cirrhosis, and validating it with participants that the model found to be at high risk. Read more about this study
- Who is it for?
- Ages 40 to 75 years
- Conditions the study is for
- NAFLD - Nonalcoholic Fatty Liver Disease, Fibrosis, Liver, Cirrhosis, Liver
- What is being tested?
- Fibroscan (non interventional)
- How many people
- 120 participants
- Where
- 1 siteIsrael
- Study dates
- 15 Mar 2022to Dec 2023
- 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 40 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This study has two main populations: * The entire population within Clalit Healthcare's electronic records database. * Community based healthy subjects without known viral or autoimmune hepatitis, and without right heart failure.
Who may be able to join
- Subjects with electronic health records from Clalit Healthcare's North district
- Ages 40-75
Who cannot join
- No lab test results for Hemoglobin, platelet, or white blood cell count.
- Known Viral Hepatitis (HBV, HCV, HDV).
- Known liver cirrhosis.
- Known lipidoses.
- Known alpha-1-antitrypsin deficiency.
- Known hemochromatosis.
Show all 15Show fewer
- Known disorders of copper metabolism.
- Known Budd-Chiari syndrome.
- Known alcoholic fatty liver.
- Known diagnosis for alcohol abuse.
- Known Autoimmune Hepatitis.
- Known biliary cirrhosis.
- Known cholangitis.
- Undergone liver replacement by transplant.
- Right Heart Failure.
Trial locations and contacts
1 site in 1 country.
Israel1 site, 1 recruiting
- Recruiting
Haemek medical center
Afula, North, 1834111
Rawi Hazzan, Dr, 972-4-649-5629, ravih@clalit.org.il
About this study
The investigators use machine learning capabilities on massive electronic health records for the purpose of developing a model that prioritizes individuals at high risk of progressing to liver cirrhosis, and validating it with participants that the model found to be at high risk.
constructing and validating a reliable model, with sufficient accuracy to justify further and expensive means of detection, will enable treating patients with damaged liver at an early enough stage to allow improvement of the liver condition.
Read the detailed description
In this study the investigators harness modern capabilities of machine learning in the field of hepatology for developing a model that can identify prioritize individuals at high risk of progressing to liver cirrhosis at an early and treatable stage.
Cirrhosis is an advanced state of liver disease that usually manifest when the liver is already severely damaged, without many treatment options and gloomy prognosis.
There are currently 2 means for diagnosis, the first is liver biopsy that is costly and inflicts pain to the patients, and has its own risks. The second is a designated imaging test, such as Fibroscan, which is safe and painless but also too expensive than can be doable as a broad screening tool.
Scores that calculates higher probability for a liver disease have already been developed, but with lower predictive strength than suitable to justify further examination towards detection.
The study comprises of 4 distinct phases:
- Model development. A machine learning model predicting time-to-event for liver cirrhosis diagnosis will be developed based on Electronic Health Records. Records are anonymized and all work is performed on a designated server.
- Anonymized Electronic Health Records latest lab test results and diagnoses from Clalit healthcare's North district will be obtained. On those records the trained model from phase 1 will run to predict time-to-event for liver cirrhosis diagnosis. Via predictions individuals will be ordered by risk. Via the deanonymized records, available only to clinicians, 20 individuals of highest risk will be observed. This includes measuring latest FIB-4 scores, viewing prior diagnoses and tests, as well as textual information from physicians. These individuals are not invited to a visit and are only viewed retrospectively through their records.
- Upon results from phase 2 the machine learning model from phase 1 will be revised. This includes possible alterations such as revisions of inclusion/exclusion criteria, change of lab tests given as input to the model, etc.
- As in phase 2, updated anonymized Electronic Health Records from Clalit healthcare's North district will be obtained. The updated model from phase 3 will run to predict time-to-event for liver cirrhosis diagnosis. In addition, all individuals will have their FIB-4 score computed. A ranked list of the top individuals with highest predicted risk predicted by the model from phase 3 and top individuals with the highest FIB-4 score will be constructed. Approximately a fourth of the individuals will come from the FIB-4 score group, age and gender matched to the prediction group. Group identity will remain unknown to clinicians, maintaining a double blinded study. Individuals will be invited to the clinic for checks. At the clinic individuals will undergo Fibroscan, height and weight measurements, answer the WHO Alcohol Use Disorders Identification Test (AUDIT) questionnaire. Furthermore, their files within the Electronic Health Records will be open and existing diagnoses, lab tests and medication prescriptions be collected.
- Being tested
- Fibroscan (non interventional)
- Keywords
- Fibroscan, Model, Machine learning
- Medical subject headings
- Non-alcoholic Fatty Liver Disease, Liver Cirrhosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Fibroscan (non interventional)Diagnostic test
An elastography test that includes an ultrasound wave imaging of the liver to estimate liver fatness, in combination with a proprioty examination of the liver stiffness.
| Study group | Role | What they receive |
|---|---|---|
| Learning e-cohort | Includes the entire population within Clalit Healthcare's electronic records database which spans from the year 2000 to 2021. | |
| FIB-4 score group | One of the two validation population invited to the clinic. The FIB-4 score group are individuals invited by their score. Receives: Diagnostic Test: Fibroscan (non interventional) | |
| Model based group | One of the two validation population invited to the clinic:
The Model based group are individuals invited by their predicted time-to-event to liver cirrhosis diagnosis. Receives: Diagnostic Test: Fibroscan (non interventional) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnosis of advanced liver fibrosis by Fibroscan (kPa measure as a grade between F3-F4).
measurement ranges from a min value of 0 to a max value of 75. For NAFLD above 7.5 is considered F2,above 10 F3 and above 14 high.
Measured: 6 - 12 months
Secondary outcomes (2)
Abnormal liver Fibroscan (kPa measure as a grade between F1-F2).
measurement ranges from a min value of 0 to a max value of 75. For NAFLD above 7.5 is considered F2,above 10 F3 and above 14 high.
Measured: 6 - 12 months
Fatty liver (score as measured in CAP)
measurement ranges from a min value of 100 to a max value of 400. Above 290 is considered high.
Measured: 6 - 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Feb 2022
- Study start15 Mar 2022
- Primary completionAug 2023 (estimated)
- Study completionDec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for NAFLD - Nonalcoholic Fatty Liver DiseaseStudies 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
- Prospective
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- HaEmek Medical Center, Israel
- Sponsor type
- University, hospital or other
- Collaborators
- Weizmann Institute of Science
- Responsible party
- Principal investigator, Ziv Neeman, HaEmek Medical Center, Israel
- Investigators
- Ziv Neeman, MD, Principal investigator, HaEmek Medical Center, Israel
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05218538
- Sponsor's protocol ID
- 0208-20-EMC
- Status last verified
- Mar 2022
- First submitted
- 20 Jan 2022
- Last updated
- 31 Mar 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Kalka IN, Hazzan R, Yacovzada NS, Igbaria S, Segal E, Weinberger A, Neeman Z. Fibro predict a machine learning risk score for advanced liver fibrosis in the general population using Israeli electronic health records. Sci Rep. 2025 Sep 1;15(1):32035. doi: 10.1038/s41598-025-17534-9.
PubMed 40887472
Study at a glance
From the registry record, last updated by the sponsor on 31 Mar 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Liver Cirrhosis Diagnosis Prioritizing Algorithm Based on Electronic Health Records.
- ClinicalTrials.gov ID
- NCT05218538
- Acronym / sponsor ID
- 0208-20-EMC
- Keywords
- Fibroscan, Model, Machine learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- HaEmek Medical Center, IsraelUniversity, hospital or other
- Collaborators
- Weizmann Institute of Science
- Study start
- 15 Mar 2022 (actual)
- Primary completion
- Aug 2023 (estimated)
- Study completion
- Dec 2023 (estimated)
- First posted
- 1 Feb 2022 (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 use machine learning capabilities on massive electronic health records for the purpose of developing a model that prioritizes individuals at high risk of progressing to liver cirrhosis, and validating it with participants that the model found to be at high risk.
constructing and validating a reliable model, with sufficient accuracy to justify further and expensive means of detection, will enable treating patients with damaged liver at an early enough stage to allow improvement of the liver condition.
Detailed description
In this study the investigators harness modern capabilities of machine learning in the field of hepatology for developing a model that can identify prioritize individuals at high risk of progressing to liver cirrhosis at an early and treatable stage.
Cirrhosis is an advanced state of liver disease that usually manifest when the liver is already severely damaged, without many treatment options and gloomy prognosis.
There are currently 2 means for diagnosis, the first is liver biopsy that is costly and inflicts pain to the patients, and has its own risks. The second is a designated imaging test, such as Fibroscan, which is safe and painless but also too expensive than can be doable as a broad screening tool.
Scores that calculates higher probability for a liver disease have already been developed, but with lower predictive strength than suitable to justify further examination towards detection.
The study comprises of 4 distinct phases:
- Model development. A machine learning model predicting time-to-event for liver cirrhosis diagnosis will be developed based on Electronic Health Records. Records are anonymized and all work is performed on a designated server.
- Anonymized Electronic Health Records latest lab test results and diagnoses from Clalit healthcare's North district will be obtained. On those records the trained model from phase 1 will run to predict time-to-event for liver cirrhosis diagnosis. Via predictions individuals will be ordered by risk. Via the deanonymized records, available only to clinicians, 20 individuals of highest risk will be observed. This includes measuring latest FIB-4 scores, viewing prior diagnoses and tests, as well as textual information from physicians. These individuals are not invited to a visit and are only viewed retrospectively through their records.
- Upon results from phase 2 the machine learning model from phase 1 will be revised. This includes possible alterations such as revisions of inclusion/exclusion criteria, change of lab tests given as input to the model, etc.
- As in phase 2, updated anonymized Electronic Health Records from Clalit healthcare's North district will be obtained. The updated model from phase 3 will run to predict time-to-event for liver cirrhosis diagnosis. In addition, all individuals will have their FIB-4 score computed. A ranked list of the top individuals with highest predicted risk predicted by the model from phase 3 and top individuals with the highest FIB-4 score will be constructed. Approximately a fourth of the individuals will come from the FIB-4 score group, age and gender matched to the prediction group. Group identity will remain unknown to clinicians, maintaining a double blinded study. Individuals will be invited to the clinic for checks. At the clinic individuals will undergo Fibroscan, height and weight measurements, answer the WHO Alcohol Use Disorders Identification Test (AUDIT) questionnaire. Furthermore, their files within the Electronic Health Records will be open and existing diagnoses, lab tests and medication prescriptions be collected.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Learning e-cohort | – | –Includes the entire population within Clalit Healthcare's electronic records database which spans from the year 2000 to 2021. |
| FIB-4 score group | – | Diagnostic Test: Fibroscan (non interventional)One of the two validation population invited to the clinic. The FIB-4 score group are individuals invited by their score. |
| Model based group | – | Diagnostic Test: Fibroscan (non interventional)One of the two validation population invited to the clinic: The Model based group are individuals invited by their predicted time-to-event to liver cirrhosis diagnosis. |
Who may qualify
- Ages
- 40 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study has two main populations: * The entire population within Clalit Healthcare's electronic records database. * Community based healthy subjects without known viral or autoimmune hepatitis, and without right heart failure.
Key inclusion criteria (2 listed)
- Subjects with electronic health records from Clalit Healthcare's North district
- Ages 40-75
Key exclusion criteria (15 listed)
- No lab test results for Hemoglobin, platelet, or white blood cell count.
- Known Viral Hepatitis (HBV, HCV, HDV).
- Known liver cirrhosis.
- Known lipidoses.
- Known alpha-1-antitrypsin deficiency.
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 |
|---|---|---|
| Fibroscan (non interventional)An elastography test that includes an ultrasound wave imaging of the liver to estimate liver fatness, in combination with a proprioty examination of the liver stiffness. | Diagnostic test | FIB-4 score group, Model based group |
Primary outcome
Diagnosis of advanced liver fibrosis by Fibroscan (kPa measure as a grade between F3-F4).
Time frame: 6 - 12 months
measurement ranges from a min value of 0 to a max value of 75. For NAFLD above 7.5 is considered F2,above 10 F3 and above 14 high.
2 secondary outcomes
Abnormal liver Fibroscan (kPa measure as a grade between F1-F2).
Time frame: 6 - 12 months
Fatty liver (score as measured in CAP)
Time frame: 6 - 12 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Mar 2022.
- Derived from this study: PubMed 40887472Kalka IN, Hazzan R, Yacovzada NS, Igbaria S, Segal E, Weinberger A, Neeman Z. Fibro predict a machine learning risk score for advanced liver fibrosis in the general population using Israeli electronic health records. Sci Rep. 2025 Sep 1;15(1):32035. doi: 10.1038/s41598-025-17534-9.
Related trials for NAFLD - Nonalcoholic Fatty Liver Disease
Active, not recruitingPhase 1/2
A Study to Evaluate ALN-CIDEB in Adult Participants With Metabolic Dysfunction-Associated Steatotic Liver Disease or With Metabolic Dysfunction-Associated Steatohepatitis (MASLD/MASH)
Metabolic Dysfunction-Associated Steatotic Liver Disease, Metabolic Dysfunction-Associated Steatohepatitis
Active, not recruiting
Target of Suv420h1/2 in Hepatocytes
NAFLD
Active, not recruitingPhase 3
A Phase 3 Study Evaluating Efficacy and Safety of Lanifibranor Followed by an Active Treatment Extension in Adult Patients With (NASH) and Fibrosis Stages F2 and F3 ( NATiV3 )
NASH - Nonalcoholic Steatohepatitis
Not yet recruitingPhase 2/3
Evaluating the Safety and Efficacy of Roflumilast in the Treatment of Nonalcoholic Fatty Liver Disease Patients
NAFLD (Nonalcoholic Fatty Liver Disease)
Not yet recruitingPhase 2/3
Evaluating the Safety and Efficacy of Rupatadine in the Treatment of Nonalcoholic Fatty Liver Disease Patients.
NAFLD (Nonalcoholic Fatty Liver Disease)
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.
Ziv Neeman, MD principal investigator
HaEmek Medical Center, Israel
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 NAFLD - Nonalcoholic Fatty Liver Disease and Fibrosis, Liver, run by HaEmek Medical Center, Israel at 1 site in Israel.
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 15 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 31 Mar 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 Israel. 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 Rawi Hazzan, MD (+972-4649-5629, ravih@clalit.org.il), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnosis of advanced liver fibrosis by Fibroscan (kPa measure as a grade between F3-F4). (time frame: 6 - 12 months). 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 NCT05218538, which also lists 1 linked publication.
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 31 Mar 2022.