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Prediction of heart failure mortality by machine learning

Machine Learning–Based Models vs Traditional Models for Predicting Mortality in Patients With Heart Failure

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400082726
Enrollment
Unknown
Registered
2024-04-07
Start date
2023-11-17
Completion date
Unknown
Last updated
2024-04-08

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

Conditions

heart failure

Interventions

Gold Standard:Clinical outcomes
Index test:Machine learning-based model for predicting mortality in patients with heart failure
traditional model for predicting mortality in patients with heart failure

Sponsors

The First Affiliated Hospital of Zhengzhou University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 100 Years

Inclusion criteria

Inclusion criteria: (1) Age =18 years old. (2) Diagnosed with heart failure.

Exclusion criteria

Exclusion criteria: (1) Died of cancer or other serious illness. (2) Lost a visit.

Design outcomes

Primary

MeasureTime frame
all-cause mortality;Area under ROC curve;—

Secondary

MeasureTime frame
sensitivity;specificity;positive predictive value;negative predictive value ;—

Countries

China

Contacts

Public ContactYingwei Chen

The First Affiliated Hospital of Zhengzhou University

zzyingweichen@126.com+86 186 3900 1891

Outcome results

None listed

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