NCT03793231

Brief Summary

This national Australian study will validate and implement an effective approach to real-time electronic surveillance of fungal infections in patients with blood cancers using technology based on artificial intelligence. It will establish metrics for antifungal stewardship allowing benchmarking of these programs; provide decision support for radiologist interpretation of chest imaging and improve reporting, audit and feedback practices in hospitals where these infections are managed.

Trial Health

43
At Risk

Trial Health Score

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

Trial has exceeded expected completion date
Enrollment
1,000

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jan 2019

Geographic Reach
1 country

1 active site

Status
unknown

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

First Submitted

Initial submission to the registry

December 15, 2018

Completed
17 days until next milestone

Study Start

First participant enrolled

January 1, 2019

Completed
3 days until next milestone

First Posted

Study publicly available on registry

January 4, 2019

Completed
2 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 30, 2020

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

December 30, 2020

Completed
Last Updated

October 23, 2020

Status Verified

August 1, 2020

Enrollment Period

2 years

First QC Date

December 15, 2018

Last Update Submit

October 22, 2020

Conditions

Fungal Infections

Keywords

Fungal infectionsDeep learningElectronic surveillanceAntifungal stewardshipImage classification

Outcome Measures

Primary Outcomes (1)

  • Accuracy of electronic surveillance using fungalAi natural language processing compared to active manual methods for detection of fungal pneumonia

    Sensitivity, specificity, ROC, Area under precision-recall curve of Ai assisted surveillance for fungal pneumonia using natural language processing of imaging reports compared to active manual surveillance

    12 months

Secondary Outcomes (3)

  • Accuracy of disease classification of deep learning based image analysis for fungal pneumonia at scan level.

    12 months

  • Accuracy of feature detection of fungal pneumonia using deep learning based image analysis of chest CT compared to radiologist expertise.

    12 months

  • Accuracy of disease classification of an expert system integrating microbiology and antifungal drug prescriptions with text and image analysis compared to active manual surveillance.

    12 months

Study Arms (2)

Fungal Cases

Patients with confirmed invasive fungal infections according to internationally accepted criteria identified by active manual surveillance. Clinical data will be sent to fungalAi platform technology for disease classification.

Combination Product: fungalAi platform technology

Control patients

Patients without invasive fungal infections.Clinical data will be sent to fungalAi platform technology for disease classification.

Combination Product: fungalAi platform technology

Interventions

fungalAi platform technologyCOMBINATION_PRODUCT

Electronic surveillance and radiologic diagnosis of invasive fungal infections using fungalAi and associated methodologies.

Control patientsFungal Cases

Eligibility Criteria

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

Adults and children with blood cancers under the haematology service at participating sites inclusive of inpatient and ambulatory care patients.

You may qualify if:

  • Adults and children
  • Under the haematology service at participating sites
  • Inpatient and ambulatory patients.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Alfred Health

Melbourne, Victoria, Australia

Location

MeSH Terms

Conditions

Mycoses

Condition Hierarchy (Ancestors)

Bacterial Infections and MycosesInfections

Study Officials

  • Michelle Dr Ananda-Rajah

    The Alfred

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER GOV
Responsible Party
SPONSOR

Study Record Dates

First Submitted

December 15, 2018

First Posted

January 4, 2019

Study Start

January 1, 2019

Primary Completion

December 30, 2020

Study Completion

December 30, 2020

Last Updated

October 23, 2020

Record last verified: 2020-08

Data Sharing

IPD Sharing
Will share

De-identified individual participant data will be made available in aggregate form for reports, presentations and publications.

Shared Documents
STUDY PROTOCOL, SAP, CSR
Time Frame
Data will be made available within 6-12 months after study completion.
Access Criteria
Access to study protocol, SAP, CSR will be made publicly available. Access to IPD including individual labelled data will be reviewed by an external independent review panel to ensure that all ethical issues have been met.
More information

Locations

AU(1)