Clinical trial NCT06486649: Application of Multimodal Large Language Model in HFpEF
Official title: Application of a Multimodal Large Language Model to Assist Diagnosis for Heart Failure With Preserved Ejection Fraction
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Heart Failure With Preserved Ejection Fraction
Keywords: Heart failure with preserved ejection fraction
large language model
diagnosis
Lead sponsor: Peking University Third Hospital
Sponsor class: OTHER
Collaborators: name: Tianjin Medical University General Hospital
name: The First Hospital of Hebei Medical University
name: Qianfoshan Hospital
name: Qingdao Municipal Hospital
First posted: 2024-07-03
Enrollment: 80
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CROSSOVER
Time perspective: PROSPECTIVE
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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: dignostic specificity
timeFrame: through study completion, an average of 8 months
measure: dignostic sensitivity
timeFrame: through study completion, an average of 8 months
Secondary outcomes:
measure: consistency rate
timeFrame: through study completion, an average of 8 months
measure: time spent on diagnosis
timeFrame: through study completion, an average of 8 months
measure: patient satisfaction
timeFrame: through study completion, an average of 8 months
measure: economic cost analysis
timeFrame: through study completion, an average of 8 months
measure: false discovery rate
timeFrame: through study completion, an average of 8 months
measure: physician workload assessment
timeFrame: through study completion, an average of 8 months
measure: diagnosis efficiency
timeFrame: through study completion, an average of 8 months
Other outcomes:
Locations:
city: Beijing
country: China
facility: Peking UniversityThird Hospital
state: Beijing
Study officials:
References:
citation: Kittleson MM, Panjrath GS, Amancherla K, Davis LL, Deswal A, Dixon DL, Januzzi JL Jr, Yancy CW. 2023 ACC Expert Consensus Decision Pathway on Management of Heart Failure With Preserved Ejection Fraction: A Report of the American College of Cardiology Solution Set Oversight Committee. J Am Coll Cardiol. 2023 May 9;81(18):1835-1878. doi: 10.1016/j.jacc.2023.03.393. Epub 2023 Apr 19. No abstract available.
citation: Wang X, Cunningham JW. Restoring balance in heart failure with preserved ejection fraction. Eur J Heart Fail. 2022 Aug;24(8):1415-1417. doi: 10.1002/ejhf.2599. Epub 2022 Jul 18. No abstract available.
citation: Sicari R. Phenotyping heart failure with preserved ejection fraction with exercise stress echocardiography. Eur Heart J Cardiovasc Imaging. 2022 Jul 21;23(8):1053-1054. doi: 10.1093/ehjci/jeac053. No abstract available.
citation: Omote K, Verbrugge FH, Borlaug BA. Heart Failure with Preserved Ejection Fraction: Mechanisms and Treatment Strategies. Annu Rev Med. 2022 Jan 27;73:321-337. doi: 10.1146/annurev-med-042220-022745. Epub 2021 Aug 11.
citation: Margulies KB. DELIVERing Progress in Heart Failure with Preserved Ejection Fraction. N Engl J Med. 2022 Sep 22;387(12):1138-1140. doi: 10.1056/NEJMe2210177. Epub 2022 Aug 27. No abstract available.
citation: Ventura HO, Lavie CJ, Mehra MR. Heart Failure With Preserved Ejection Fraction: Separating the Wheat From the Chaff. J Am Coll Cardiol. 2020 Jan 28;75(3):255-257. doi: 10.1016/j.jacc.2019.11.027. No abstract available.
citation: Reddy YNV, Borlaug BA. Heart Failure With Preserved Ejection Fraction: Where Do We Stand? Mayo Clin Proc. 2020 Apr;95(4):629-631. doi: 10.1016/j.mayocp.2020.02.015. No abstract available.
citation: Donal E, L'official G, Kosmala W. Heart Failure With Preserved Ejection Fraction: Defining Phenotypes. J Card Fail. 2020 Nov;26(11):929-931. doi: 10.1016/j.cardfail.2020.09.013. Epub 2020 Sep 19. No abstract available.
citation: Ahmad T, Desai NR, Januzzi JL. Heart Failure With Preserved Ejection Fraction: Many Emperors With Many Clothes. JACC Heart Fail. 2020 Mar;8(3):185-187. doi: 10.1016/j.jchf.2019.11.004. Epub 2020 Jan 8. No abstract available.