Retrospective artificial intelligence analysis to predict ventilatory impairment from ECG data

Retrospective artificial intelligence analysis to predict ventilatory impairment from ECG data - Retrospective artificial intelligence analysis to predict ventilatory impairment from ECG data

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000045265

Enrollment

100000

Registered

2021-08-25

Start date

2021-10-01

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Individual who has undergone examinations

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

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.

Exclusion criteria

Exclusion criteria: Age < 20 year old

Design outcomes

Primary

Measure	Time frame
AUC values of algorithms for predicting ventilation failure from ECG data.	—

Countries

Japan

Contacts

Public ContactNobuyuki Horita

Yokohama City University Hospital Department of pulmonology

horitano@yokohama-cu.ac.jp0457872800

Outcome results