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The Diagnostic Value of Subharmonic Imaging Technology Combined With Liver Stiffness and Platelet Count for High-risk Esophageal and Gastric Varices in Patients With Liver Cirrhosis

The Diagnostic Value of Subharmonic Imaging Technology Combined With Liver Stiffness and Platelet Count for High-risk Esophageal and Gastric Varices in Patients With Liver Cirrhosis

Status
Recruiting
Phases
Unknown
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT07151885
Enrollment
380
Registered
2025-09-03
Start date
2025-08-31
Completion date
2028-07-31
Last updated
2026-05-05

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Liver Cirrhosis, Esophageal and Gastric Varices

Keywords

Liver Cirrhosis, Esophageal and Gastric Varices, Subharmonic Aided Pressure Estimation

Brief summary

To evaluate the diagnostic value of the combined model of subharmonic-assisted pressure estimation (SHAPE), liver stiffness (LSM), and platelet count (PLT) for high-risk esophageal and gastric varices (HRV)

Interventions

DIAGNOSTIC_TESTSHAPE

Use an ultrasound probe to scan the liver to locate the portal vein and hepatic vein. In the angiography mode, the portal vein and hepatic vein of the same depth were selected for measurement respectively. Ultrasound contrast agent was injected through the elbow vein to observe the changes of sub-harmonic signals in the portal vein and hepatic vein. Collect the sub-harmonic signal data of the portal vein and hepatic vein, and calculate the difference between the two, that is, the SHAPE gradient.

Sponsors

The First Hospital of Jilin University
Lead SponsorOTHER

Study design

Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE

Masking description

The SHAPE operator and the analyst, the LSM operator, and the gastroscopy operator blinded each other (unaware of each other's results and clinical data), and the SHAPE images were independently analyzed by two ultrasound physicians.

Intervention model description

This study proposes for the first time a multimodal model integrating SHAPE, LSM and PLT, aiming to overcome the shortcomings of traditional tools, improve the prediction accuracy of high-risk EGV, and provide a new path for optimizing non-invasive screening strategies.

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* ① Be at least 18 years old② Clinically diagnosed as liver cirrhosis (based on medical history, physical signs, laboratory tests, imaging or liver biopsy)③ Underwent a gastroscopy④ The informed consent form has been signed

Exclusion criteria

* ① Previous EV bleeding or having received TIPS/ endoscopic treatment.② History of concurrent liver cancer, portal vein thrombosis, and splenectomy.③ Having used drugs that affect platelet count, liver function or coagulation function in the body within one week, and having a recent history of blood product infusion.

Design outcomes

Primary

MeasureTime frameDescription
HV-PV (dB)Within two weeks of admissionThe difference in the average harmonic signal between the hepatic vein and the portal vein
Measurement of liver stiffness(kPa)Within two weeks of admissionThe patient lies on their back, with their right hand placed behind their head. The right upper limb is fully abducted to expose the intercostal Spaces in the right lobe of the liver. The area usually enclosed by the horizontal line of the xiphoid process, the midline of the right axilla and the lower edge of the rib is taken as the testing area. The probe is vertically and closely attached to the skin, and the measurement position is selected in the intercostal space.
blood platelet count(×10⁹/L)Within two weeks of admissionThe peripheral blood of the patient was tested by a conventional blood analyzer to obtain the platelet count.
sensitivity and specificityThe one-year period from enrollment to the end of the groupThe sensitivity measures the ability of the model to correctly identify patients with HRV (i.e., the true positive rate), and the specificity measures the ability of the model to correctly exclude patients without HRV (i.e., the true negative rate).
Positive predictive value (PPV) and negative predictive value (NPV)The one-year period from enrollment to the end of the groupPPV represents the probability that patients identified as high-risk by the model actually have HRV, while NPV represents the probability that patients identified as low-risk by the model do not actually have HRV
AUCThe one-year period from enrollment to the end of the groupComprehensively reflect the overall discriminative ability of the model under different thresholds

Countries

China

Contacts

CONTACTDezhi Zhang, doctorate
dezhi@jlu.edu.cn0431-88782190

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: May 6, 2026