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Development of a diagnostic prediction model for infective endocarditis according to 2023 Duke-International Society for Cardiovascular Infectious disease criteria: A multi-center based observational study

Development of a diagnostic prediction model for infective endocarditis according to 2023 Duke-International Society for Cardiovascular Infectious disease criteria: A multi-center based observational study - Development of a diagnostic prediction model for infective endocarditis according to 2023 Duke-International Society for Cardiovascular Infectious disease criteria: A multi-center based observational study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
JPRN
Registry ID
JPRN-UMIN000058353
Enrollment
300
Registered
2025-07-03
Start date
2025-04-03
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

Infective endocarditis

Interventions

None listed

Sponsors

Faculty of Medicine, Saga University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Patients 20 years of age or older with International Statistical Classification of Diseases and Related Health Problems-10th Revision code I33.0 for IE or R50.9 for fever of unknown origin recorded during hospitalization.

Exclusion criteria

Exclusion criteria: Patients with no fever above 37 degree prior to admission; with hospital-onset or referral for valvular surgery after treatment by a previous physician or with non-definite the results of 2023 Duke-ISCVID among patients with I33.0; with a diagnosis confirmed before admission, with R50.9 in which the diagnosis was confirmed prior to admission and in which chest X-ray, blood test, and urinalysis were not performed prior to admission; cases with 2023 Duke-ISCVID of definite with a confirmed diagnosis of noninfectious disease; and cases who declared nonparticipation in the study.

Design outcomes

Primary

MeasureTime frame
Area under the curve, shrinkage coefficient, and stratum-specific likelihood ratio of the reconstructed prediction model for IE among patients with undiagnosed fever

Countries

Japan

Contacts

Public ContactShun Yamashita

Faculty of Medicine, Saga University Education and Research Center for Community Medicine

sy.hospitalist.japan@gmail.com0952343238

Outcome results

None listed

Source: JPRN (via WHO ICTRP) · Data processed: Jul 3, 2026