Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT05218538 |
| Other study ID # |
0208-20-EMC |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
March 15, 2022 |
| Est. completion date |
December 2023 |
Study information
| Verified date |
March 2022 |
| Source |
HaEmek Medical Center, Israel |
| Contact |
Rawi Hazzan, MD |
| Phone |
+972-4649-5629 |
| Email |
ravih[@]clalit.org.il |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical 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.
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.
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:
1. 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.
2. 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.
3. 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.
4. 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.
