Heart Failure With Preserved Ejection Fraction
Conditions
Keywords
Heart failure with preserved ejection fraction, large language model, diagnosis
Brief summary
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.
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.
Interventions
DIAGNOSTIC_TESTMultimodal Large Language Model Diagnosis
Diagnosis for heart failure with preserved ejection fraction (HFpEF) using the multimodal large language model MedGuide-72B.
DIAGNOSTIC_TESTRoutine diagnostic and therapeutic procedure
Routine diagnostic and therapeutic procedure
Sponsors
Tianjin Medical University General Hospital
The First Hospital of Hebei Medical University
Qianfoshan Hospital
Qingdao Municipal Hospital
Peking University Third Hospital
Study design
Observational model
CASE_CROSSOVER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| dignostic specificity | through study completion, an average of 8 months | dianostic specificity comparison between routine diagnosis and therapy and large language model diagnosis |
| dignostic sensitivity | through study completion, an average of 8 months | dianostic sensitivity comparison between routine diagnosis and therapy and large language model diagnosis |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| patient satisfaction | through study completion, an average of 8 months | comparison of patient satisfaction between routine diagnosis and therapy and large language model diagnosis by questionnaire |
| economic cost analysis | 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 |
| consistency rate | through study completion, an average of 8 months | consistency rate between routine diagnosis and therapy and large language model diagnosis |
| physician workload assessment | 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 |
| diagnosis efficiency | through study completion, an average of 8 months | The probability of accuracy compared to the final diagnosis of the patient's visit |
| false discovery rate | through study completion, an average of 8 months | comparison of false discovery rate between routine diagnosis and therapy and large language model diagnosis |
| time spent on diagnosis | through study completion, an average of 8 months | comparison of time spent on diagnosis between routine diagnosis and therapy and large language model diagnosis |
Countries
China
Contacts
Primary ContactXiangbin Meng
Outcome results
None listed