UMIN-CTR Clinical Trial

Public title

Study for Checking the REal-world Echocardiography to indeNtify hidden Hypertrophic CardioMyopathy

Acronym

SCREEN-HCM

Scientific Title

Study for Checking the REal-world Echocardiography to indeNtify hidden Hypertrophic CardioMyopathy

Scientific Title:Acronym

SCREEN-HCM

Region

Japan

Condition

Patients who undergo echocardiography

Classification by specialty

Cardiology

Classification by malignancy

Others

Genomic information

NO

Narrative objectives1

Hypertrophic cardiomyopathy (HCM) is one of the major forms of cardiomyopathy and is defined as "a group of diseases characterized by left or right ventricular myocardial hypertrophy and diastolic dysfunction caused by the hypertrophy." Many cases remain asymptomatic, and cohort studies have reported a prevalence of 0.15% to 0.2%. However, a substantial number of undiagnosed patients with cardiac hypertrophy likely remain unrecognized due to the absence of detailed examinations. While the prognosis of HCM has traditionally been considered elatively favorable, the annual mortality rate has been reported to be 4-6%. In recent years, therapeutic advances, both pharmacological and non-pharmacological, have led to improved intracardiac pressure gradients, heart failure markers, and patient symptoms, suggesting the possibility that early diagnosis may lead to better outcomes in the future.
Echocardiography plays a central role in diagnosing and classifying HCM through structural assessments, as well as evaluating cardiac function, hemodynamics, and complications to determine disease severity. However, left ventricular wall thickness measurements may depend on operator experience, and accurate interpretation of echocardiographic images requires specialized knowledge. This raises concerns about inter-observer variability and potential oversight of HCM cases.
Recent advances in artificial intelligence (AI) have led to the development of software that enables fast, objective, and reproducible echocardiographic measurements, including wall thickness. Moreover, deep learning-based AI approaches may help directly predict the likelihood of HCM from echocardiographic images, potentially transforming diagnostic workflows and improving patient outcomes.

Basic objectives2

Others

Basic objectives -Others

This study aims to investigate whether screening for HCM can be performed earlier and more accurately by using AI-based automated echocardiographic analysis software (US2.ai), compared to relying solely on conventional manual measurements. We also seek to clarify the prevalence of potentially undiagnosed HCM cases and characterize the clinical features of patients who may have been previously overlooked.

Trial characteristics_1

Trial characteristics_2

Developmental phase

Primary outcomes

The number and prevalence of patients who were screened as having HCM by AI-based echocardiographic analysis software (US2.ai) but were not clinically identified as HCM based on manual echocardiographic measurements and past medical records.

Key secondary outcomes

Secondary outcome 1: The number of patients clinically identified as having HCM and those not identified, based on manual echocardiographic measurements and past medical records.
Secondary outcome 2: The number of patients identified as HCM by AI-based echocardiographic analysis and their clinical characteristics.
Secondary outcome 3: The number of patients clinically identified as having HCM based on manual echocardiographic measurements and past medical records, along with their clinical characteristics.
Secondary outcome 4: The clinical characteristics of patients identified as HCM by AI-based analysis but not by manual echocardiographic measurements.
Secondary outcome 5: Clinical performance of the AI-based analysis in diagnosing HCM, including positive predictive value, sensitivity, and specificity.
Secondary outcome 6: Accuracy of AI-based analysis in measuring echocardiographic parameters related to cardiac hypertrophy.

Study type

Observational

Basic design

Randomization

Randomization unit

Blinding

Control

Stratification

Dynamic allocation

Institution consideration

Blocking

Concealment

No. of arms

Purpose of intervention

Type of intervention

Interventions/Control_1

Interventions/Control_2

Interventions/Control_3

Interventions/Control_4

Interventions/Control_5

Interventions/Control_6

Interventions/Control_7

Interventions/Control_8

Interventions/Control_9

Interventions/Control_10

Age-lower limit

20

years-old

<=

Age-upper limit

Not applicable

Gender

Male and Female

Key inclusion criteria

Adults aged 20 years or older who underwent transthoracic echocardiography in the physiological examination department of our hospital between April 1, 2023, and September 30, 2023, and who did not meet any of the exclusion criteria.

Key exclusion criteria

Patients whose echocardiographic data are unavailable or inadequate for analysis.

Target sample size

9000

Name of lead principal investigator

1st name	Nobuyuki
Middle name
Last name	Kagiyama

Organization

Juntendo University Hospital

Division name

Department of Cardiovascular Biology and Medicine

Zip code

113-8431

Address

3-1-3 Hongo, Bunkyo-ku, Tokyo, Japan

TEL

03-3813-3111

Email

kgnb_27_hot@yahoo.co.jp

Name of contact person

1st name	Nobuyuki
Middle name
Last name	Kagiyama

Organization

Juntendo University Hospital

Division name

Department of Cardiovascular Biology and Medicine

Zip code

113-8431

Address

3-1-3 Hongo, Bunkyo-ku, Tokyo, Japan

TEL

03-3813-3111

Homepage URL

Email

kgnb_27_hot@yahoo.co.jp

Institute

Juntendo University Hospital

Institute

Department

Personal name

Organization

Bristol Myers Squibb K.K.

Organization

Division

Category of Funding Organization

Profit organization

Nationality of Funding Organization

United States of America

Co-sponsor

Name of secondary funder(s)

Organization

Research Ethics Committee, Faculty of Medicine, Juntendo University

Address

3-1-3 Hongo, Bunkyo-ku, Tokyo, Japan

Tel

03-3813-3111

Email

hongo-rinri@juntendo.ac.jp

Secondary IDs

NO

Study ID_1

Org. issuing International ID_1

Study ID_2

Org. issuing International ID_2

IND to MHLW

Institutions

順天堂大学医学部附属順天堂医院（東京都）

Date of disclosure of the study information

2025

Year

05

Month

10

Day

URL releasing protocol

Publication of results

Unpublished

URL related to results and publications

Number of participants that the trial has enrolled

Results

Results date posted

Results Delayed

Results Delay Reason

Date of the first journal publication of results

Baseline Characteristics

Participant flow

Adverse events

Outcome measures

Plan to share IPD

IPD sharing Plan description

Recruitment status

Preinitiation

Date of protocol fixation

2025

Year

02

Month

28

Day

Date of IRB

2025

Year

03

Month

13

Day

Anticipated trial start date

2025

Year

05

Month

10

Day

Last follow-up date

2026

Year

12

Month

31

Day

Date of closure to data entry

Date trial data considered complete

Date analysis concluded

Other related information

Study Methods and Duration
(1) Study period: From the date of approval to December 31, 2026.
(2) Study design: Single-center, retrospective, exploratory observational study.
(3) Data collection:
This study includes adult patients who underwent transthoracic echocardiography at our hospital between April 1 and September 30, 2023. Data will include echocardiographic parameters, demographics (age, sex, height, weight), medical history, current medications, and comorbidities.
Additional data will be obtained from electronic medical records, including diagnoses, vital signs, physical findings, symptom severity, ECG, imaging (CT, MRI, catheterization), and laboratory results (BNP, NT-proBNP, CBC, biochemistry).
AI-based predictions of HCM using Us2.ai will also be included.

This study is supported by Bristol Myers Squibb. No investigational product will be administered, and the study does not aim to collect safety data. Any adverse events related to their products will be reported per regulations, and noted in the final report if applicable.

Participant Criteria
(1) Eligible patients: Adults aged 20 years or older who meet the inclusion criteria and none of the exclusion criteria.
(2) Inclusion: Underwent transthoracic echocardiography between April 1 and September 30, 2023.
(3) Exclusion: Incomplete or unusable echocardiographic data.
(4) Discontinuation:
a. Participant withdrawal;
b. Study termination;
c. Investigator's judgment.

Registered date

2025

Year

05

Month

02

Day

Last modified on

2025

Year

05

Month

02

Day

Link to view the page

Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000065720