Application of a Multimodal Large Language Model to Assist Diagnosis for Heart Failure With Preserved Ejection Fraction
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 80
- 试验地点
- 1
- 主要终点
- dignostic sensitivity
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Crossover
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age 18-80 years, male or female;
- •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);
- •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;
- •Voluntary participation and signed informed consent.
排除标准
- •Acute heart failure or acute worsening of chronic heart failure;
- •Severe coronary stenosis (≥75% stenosis) without revascularization;
- •Patients who are unable to perform exercise stress echocardiography or have contraindications to the test;
- •are participating in other clinical trials;
- •Those with severe organic pathologies of the liver, kidney, or hematologic system or those with chronic diseases;
- •Those who are unable to follow the trial procedures;
- •Those who refuse to sign the informed consent.
结局指标
主要结局
dignostic sensitivity
时间窗: through study completion, an average of 8 months
dianostic sensitivity comparison between routine diagnosis and therapy and large language model diagnosis
dignostic specificity
时间窗: through study completion, an average of 8 months
dianostic specificity comparison between routine diagnosis and therapy and large language model diagnosis
次要结局
- consistency rate(through study completion, an average of 8 months)
- economic cost analysis(through study completion, an average of 8 months)
- time spent on diagnosis(through study completion, an average of 8 months)
- patient satisfaction(through study completion, an average of 8 months)
- false discovery rate(through study completion, an average of 8 months)
- diagnosis efficiency(through study completion, an average of 8 months)
- physician workload assessment(through study completion, an average of 8 months)
研究者
Tang Yida
Professor
Peking University Third Hospital