NCT04408976

Brief Summary

This study evaluates the implementation of a machine learning based clinical decision support system for treatment of patients presenting with an urinary tract infection in general practice. The software was developed to support general practitioners in the choice of antibiotic regimen.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
16,824

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Nov 2017

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

November 13, 2017

Completed
6 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

May 16, 2018

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

May 16, 2018

Completed
2 years until next milestone

First Submitted

Initial submission to the registry

May 26, 2020

Completed
6 days until next milestone

First Posted

Study publicly available on registry

June 1, 2020

Completed
Last Updated

June 1, 2020

Status Verified

May 1, 2020

Enrollment Period

6 months

First QC Date

May 26, 2020

Last Update Submit

May 26, 2020

Conditions

Urinary Tract Infections

Keywords

Machine learningClinical Decision Support Systems

Outcome Measures

Primary Outcomes (1)

  • Non-recurrent UTI

    A subsequent period of time after treatment, in which no new UTI treatment was needed

    28 days

Study Arms (2)

Software practices

Patients with urinary tract infection in practices using the clinical decision support software

Control practices

Patients with urinary tract infection in practices not using the clinical decision support software

Eligibility Criteria

Age12 Years+
Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Patients with a urinary tract infection diagnosed by the general practitioner

You may qualify if:

  • Patients with a urinary tract infection in general practice
  • Age \> 12 years.

You may not qualify if:

  • No.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Netherlands Institute for Health Services Research

Utrecht, Netherlands

Location

Related Publications (1)

  • Herter WE, Khuc J, Cina G, Knottnerus BJ, Numans ME, Wiewel MA, Bonten TN, de Bruin DP, van Esch T, Chavannes NH, Verheij RA. Impact of a Machine Learning-Based Decision Support System for Urinary Tract Infections: Prospective Observational Study in 36 Primary Care Practices. JMIR Med Inform. 2022 May 4;10(5):e27795. doi: 10.2196/27795.

    PMID: 35507396DERIVED

MeSH Terms

Conditions

Urinary Tract Infections

Condition Hierarchy (Ancestors)

InfectionsUrologic DiseasesFemale Urogenital DiseasesFemale Urogenital Diseases and Pregnancy ComplicationsUrogenital DiseasesMale Urogenital Diseases

Study Officials

  • Willem Herter

    Leiden University Medical Center

    STUDY DIRECTOR

  • Tobias Bonten

    Leiden University Medical Center

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Assistant Professor

Study Record Dates

First Submitted

May 26, 2020

First Posted

June 1, 2020

Study Start

November 13, 2017

Primary Completion

May 16, 2018

Study Completion

May 16, 2018

Last Updated

June 1, 2020

Record last verified: 2020-05

Data Sharing

IPD Sharing
Will not share

Locations

NL(1)