FindTrial
Sign In

Development of a prediction models for clinical outcomes of patients with peritoneal dialysis based on home-based monitoring : Using deep learning methods

Development of a prediction models for clinical outcomes of patients with peritoneal dialysis based on home-based monitoring : Using deep learning methods

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0005608
Enrollment
100
Registered
2020-11-19
Start date
2020-11-30
Completion date
Unknown
Last updated
2020-12-02

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

Conditions

None listed

Interventions

None listed

Sponsors

Yonsei University Health System, Severance Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Those who have the ability to consent in writing to the research among patients undergoing automatic peritoneal dialysis

Exclusion criteria

Exclusion criteria: Patients who disagree with collecting clinical information

Design outcomes

Primary

MeasureTime frame
The effectiveness of the predictive models to estimate dialysis adequacy in APD patients by using ShareSource;The possibility of the predictive models to evaluate the nutritional status in APD patients by using ShareSource;The potentials of the predictive models to assess the risk of dialysis related infection by using ShareSource—

Secondary

MeasureTime frame
Risk modification of unexpected hospital visits or hospitalizations in accordance with remote monitoring;Rate of transition to hemodialysis;All cause mortality—

Countries

Korea, Republic of

Contacts

Public ContactHyung Woo Kim

Yonsei University Health System, Severance Hospital

Outcome results

None listed

Source: CRIS (via WHO ICTRP) · Data processed: Feb 4, 2026