UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000045265 |
|---|---|
| Receipt number | R000051703 |
| Scientific Title | Retrospective artificial intelligence analysis to predict ventilatory impairment from ECG data |
| Date of disclosure of the study information | 2021/08/25 |
| Last modified on | 2021/08/25 17:37:32 |
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Basic information
Public title
Retrospective artificial intelligence analysis to predict ventilatory impairment from ECG data
Acronym
Retrospective artificial intelligence analysis to predict ventilatory impairment from ECG data
Scientific Title
Retrospective artificial intelligence analysis to predict ventilatory impairment from ECG data
Scientific Title:Acronym
Retrospective artificial intelligence analysis to predict ventilatory impairment from ECG data
Region
| Japan |
Condition
Condition
Individual who has undergone examinations
Classification by specialty
| Pneumology |
Classification by malignancy
Others
Genomic information
NO
Objectives
Narrative objectives1
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the world, and respiratory function tests are essential for the diagnosis of COPD to confirm obstructive respiratory impairment (one-second rate <70%). However, in order to perform a respiratory function test, a machine and a well-trained technician must be prepared.For this reason, COPD is difficult to diagnose in non-specialized facilities, and it is estimated that the majority of patients are undiagnosed. The same problem also arises for interstitial pneumonia. The purpose of this study is to build an algorithm to predict ventilation disorders from ECG data using machine learning.
Basic objectives2
Others
Basic objectives -Others
Diagnosis
Trial characteristics_1
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
AUC values of algorithms for predicting ventilation failure from ECG data.
Key secondary outcomes
Base
Study type
Observational
Study design
Basic design
Randomization
Randomization unit
Blinding
Control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
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
Eligibility
Age-lower limit
| 20 | years-old | <= |
Age-upper limit
| Not applicable |
Gender
Male and Female
Key inclusion criteria
Patients who underwent both electrocardiography and respiratory function tests at Yokohama City University Hospital within a one-year interval from 2010 to the date of Ethics Committee approval will be eligible. Patients will be recruited regardless of underlying disease, gender, or medical specialty. Age should be 20 years or older.
Key exclusion criteria
Age < 20 year old
Target sample size
100000
Research contact person
Name of lead principal investigator
| 1st name | Nobuyuki |
| Middle name | |
| Last name | Horita |
Organization
Yokohama City University Hospital
Division name
Department of pulmonology
Zip code
236-0004
Address
3-9, Kanazawa, Fukuura, Yokohama
TEL
0457872800
horitano@yokohama-cu.ac.jp
Public contact
Name of contact person
| 1st name | Nobuyuki |
| Middle name | |
| Last name | Horita |
Organization
Yokohama City University Hospital
Division name
Department of pulmonology
Zip code
236-0004
Address
3-9, Kanazawa, Fukuura, Yokohama
TEL
0457872800
Homepage URL
horitano@yokohama-cu.ac.jp
Sponsor or person
Institute
Yokohama City University Hospital
Institute
Department
Personal name
Funding Source
Organization
Yokohama City University Hospital
Organization
Division
Category of Funding Organization
Other
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
Yokohama City University Hospital
Address
3-9, Fukuura, Kanazawa, Yokohama
Tel
045-787-2800
horitano@yokohama-cu.ac.jp
Secondary IDs
Secondary IDs
NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW
Institutions
Institutions
Other administrative information
Date of disclosure of the study information
| 2021 | Year | 08 | Month | 25 | Day |
Related information
URL releasing protocol
Publication of results
Unpublished
Result
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
Progress
Recruitment status
Preinitiation
Date of protocol fixation
| 2021 | Year | 08 | Month | 25 | Day |
Date of IRB
Anticipated trial start date
| 2021 | Year | 10 | Month | 01 | Day |
Last follow-up date
| 2022 | Year | 10 | Month | 01 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
We will analyze 50,000 data of all adult patients who underwent electrocardiography and respiratory function tests at intervals of one year or less between 2010 and 2021 at Yokohama City University Hospital (hereinafter referred to as "the hospital"). Since the analysis will be conducted regardless of the department, a wide variety of patients will be included in the analysis, including respiratory medicine patients with various respiratory diseases, cardiology patients with various cardiac diseases, and patients with no conspicuous abnormalities in cardiopulmonary function who underwent ECG and respiratory function tests as a routine procedure before surgery. The system is linked to our electronic medical record.
The ECG and respiratory function data of the relevant patients are extracted from the physiological function test data storage space linked to the hospital's electronic medical record. (The ECG data uses the potential, duration, and axis angle of each wave for each induction calculated by an automatic analysis device (model number ECG-1550, Nihon Kohden).
Machine learning using deep learning is performed using the Keras library, which runs in the Python language. We divided the data of about 50,000 cases of the hospital into 30,000 cases of the development set, 10,000 cases of the validation set, and 10,000 cases of the test set, and adjusted hyperparameters such as layers of deep learning, number of nodes, and various settings to prevent overlearning in all two sets. We will continue to use the data from the back one for the test set to finally evaluate the performance of the algorithm.
Management information
Registered date
| 2021 | Year | 08 | Month | 25 | Day |
Last modified on
| 2021 | Year | 08 | Month | 25 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000051703
