Application of Multimodal Large Language Model in HFpEF (MeG-HFpEF)

June 27, 2024 updated by: Tang Yida, Peking University Third Hospital

Application of a Multimodal Large Language Model to Assist Diagnosis for Heart Failure With Preserved Ejection Fraction

This study will validate the effectiveness of a multimodal large language model to screen for heart failure with preserved ejection fraction (HFpEF), comparing it with the traditional clinical standardized assessment process.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Heart failure is a major complication of various heart diseases and is the leading lethal cause of cardiovascular death worldwide. Based on the left ventricular ejection fraction (LVEF), heart failure can be divided into heart failure with reduced ejection fraction (HFrEF), heart failure with preserved ejection fraction (HFpEF) and heart failure with mildly reduced ejection fraction (HFmrEF). Heart failure rehospitalization rates and in-hospital complications did not differ between HFrEF and HFpEF. However, over the past two decades, the survival rate of HFrEF has improved significantly, whereas HFpEF has remained stagnant. One of the major reasons for this is that the diagnostic process of HFpEF is complicated, and it is easy to cause missed diagnosis in the clinic, resulting in delayed treatment.

Multimodal large language models are capable of integrating and analyzing medical data from different sources, including textual data (e.g., medical records, medical literature), image data (e.g., electrocardiograms, CT scan images), and audio data (e.g., symptoms narrated by patients). This multimodal data integration capability is crucial for understanding complex medical scenarios, as it provides a more comprehensive view of the condition than a single data source.

The diagnosis of HFpEF faces many challenges and requires clinicians to make judgments on multi-dimensional data, which can easily lead to the underdiagnosis and misdiagnosis of the disease. As a generative artificial intelligence tool, a large language model is able to integrate and analyze data from different sources and has the ability to learn and evolve from existing clinical evidence. Based on this, this study intends to evaluate the effectiveness of multimodal large language model for screening for heart failure with preserved ejection fraction (HFpEF), comparing it with the traditional clinical standard assessment process.

Study Type

Observational

Enrollment (Estimated)

80

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Beijing
      • Beijing, Beijing, China
        • Recruiting
        • Peking UniversityThird Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Subjects will be recruited from patients routinely hospitalized in the cardiology department who also met the needs of this trial and are not recruited separately.

Description

Inclusion Criteria:

  1. Age 18-80 years, male or female;
  2. Cardiology inpatients with suspected heart failure with preserved ejection fraction (cardiac ultrasound suggestive of LVEF ≥50% with at least 1 of the following: 1, left ventricular hypertrophy and/or left atrial enlargement; and 2, abnormal diastolic cardiac function);
  3. Current or previous at least one symptom of heart failure, including dyspnea (including exertional dyspnea, nocturnal paroxysmal dyspnea, and telangiectasia), malaise, nausea, and bilateral lower extremity edema;
  4. Voluntary participation and signed informed consent.

Exclusion Criteria:

  1. Acute heart failure or acute worsening of chronic heart failure;
  2. Severe coronary stenosis (≥75% stenosis) without revascularization;
  3. Patients who are unable to perform exercise stress echocardiography or have contraindications to the test;
  4. are participating in other clinical trials;
  5. Those with severe organic pathologies of the liver, kidney, or hematologic system or those with chronic diseases;
  6. Those who are unable to follow the trial procedures;
  7. Those who refuse to sign the informed consent.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
single group

The routine consultation process was performed first: according to the process recommended by the 2023 edition of the Chinese Expert Consensus on the Diagnosis and Treatment of Heart Failure with Preserved Ejection Fraction, the attending cardiologist completed the subject's clinical criteria assessment and performed the HFpEF diagnosis (yes/no).

During the attending physician's checkup visit, the multimodal large language model screening system (MedGuide-72B) collected routine visit data, recorded relevant data and indicators during the patient's communication with MedGuide-72B and made the diagnosis.
Diagnostic test: Multimodal Large Language Model Diagnosis
Diagnosis for heart failure with preserved ejection fraction (HFpEF) using the multimodal large language model MedGuide-72B.
Diagnostic test: Routine diagnostic and therapeutic procedure
Routine diagnostic and therapeutic procedure

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
dignostic specificity
Time Frame: through study completion, an average of 8 months
dianostic specificity comparison between routine diagnosis and therapy and large language model diagnosis
through study completion, an average of 8 months
dignostic sensitivity
Time Frame: through study completion, an average of 8 months
dianostic sensitivity comparison between routine diagnosis and therapy and large language model diagnosis
through study completion, an average of 8 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
consistency rate
Time Frame: through study completion, an average of 8 months
consistency rate between routine diagnosis and therapy and large language model diagnosis
through study completion, an average of 8 months
time spent on diagnosis
Time Frame: through study completion, an average of 8 months
comparison of time spent on diagnosis between routine diagnosis and therapy and large language model diagnosis
through study completion, an average of 8 months
patient satisfaction
Time Frame: through study completion, an average of 8 months
comparison of patient satisfaction between routine diagnosis and therapy and large language model diagnosis by questionnaire
through study completion, an average of 8 months
economic cost analysis
Time Frame: through study completion, an average of 8 months
comparison of economic cost between routine diagnosis and therapy and large language model diagnosis by the total cost of treatment
through study completion, an average of 8 months
false discovery rate
Time Frame: through study completion, an average of 8 months
comparison of false discovery rate between routine diagnosis and therapy and large language model diagnosis
through study completion, an average of 8 months
physician workload assessment
Time Frame: through study completion, an average of 8 months
comparison of physician workload between routine diagnosis and therapy and large language model diagnosis according to counting the number of participants with treatment-related
through study completion, an average of 8 months
diagnosis efficiency
Time Frame: through study completion, an average of 8 months
The probability of accuracy compared to the final diagnosis of the patient's visit
through study completion, an average of 8 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Peking University Third Hospital

Collaborators

Qingdao Municipal Hospital
Tianjin Medical University General Hospital
The First Hospital of Hebei Medical University
Central hospital Affiliated to Shandong First Medical University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 20, 2023

Primary Completion (Estimated)

December 20, 2024

Study Completion (Estimated)

December 20, 2024

Study Registration Dates

First Submitted

December 22, 2023

First Submitted That Met QC Criteria

June 27, 2024

First Posted (Actual)

July 3, 2024

Study Record Updates

Last Update Posted (Actual)

July 3, 2024

Last Update Submitted That Met QC Criteria

June 27, 2024

Last Verified

June 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • M2023830

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Heart Failure With Preserved Ejection Fraction

Clinical Trials on Multimodal Large Language Model Diagnosis

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in United Kingdom

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe