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Trial details imported from ClinicalTrials.gov

For full trial details, please see the original record at https://clinicaltrials.gov/show/NCT03793231




Registration number
NCT03793231
Ethics application status
Date submitted
15/12/2018
Date registered
4/01/2019
Date last updated
28/06/2019

Titles & IDs
Public title
fungalAi for Fungal Surveillance & Antifungal Stewardship
Scientific title
Innovative Use of fungalAi for Antifungal Stewardship in Haematology-oncology Patients
Secondary ID [1] 0 0
012018
Secondary ID [2] 0 0
43127/MonH-2018-152967
Universal Trial Number (UTN)
Trial acronym
fungalAi
Linked study record

Health condition
Health condition(s) or problem(s) studied:
Fungal Infections 0 0
Condition category
Condition code
Infection 0 0 0 0
Other infectious diseases

Intervention/exposure
Study type
Observational
Patient registry
Target follow-up duration
Target follow-up type
Description of intervention(s) / exposure
Combination Product - fungalAi platform technology

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.

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


Combination Product: fungalAi platform technology
Electronic surveillance and radiologic diagnosis of invasive fungal infections using fungalAi and associated methodologies.

Intervention code [1] 0 0
Combination Product
Comparator / control treatment
Control group

Outcomes
Primary outcome [1] 0 0
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
Timepoint [1] 0 0
12 months
Secondary outcome [1] 0 0
Accuracy of disease classification of deep learning based image analysis for fungal pneumonia at scan level. - Sensitivity, specificity, ROC of deep learning based image analysis at scan level compared to active manual surveillance.
Timepoint [1] 0 0
12 months
Secondary outcome [2] 0 0
Accuracy of feature detection of fungal pneumonia using deep learning based image analysis of chest CT compared to radiologist expertise. - Sensitivity, error rate (false positives, false negatives) at pixel level of deep learning based image analysis compared to radiologist labels.
Timepoint [2] 0 0
12 months
Secondary outcome [3] 0 0
Accuracy of disease classification of an expert system integrating microbiology and antifungal drug prescriptions with text and image analysis compared to active manual surveillance. - Sensitivity, specificity, ROC, Area under precision-recall curve of Ai assisted surveillance compared to active manual surveillance that will only be performed at Alfred Health.
Timepoint [3] 0 0
12 months

Eligibility
Key inclusion criteria
- Adults and children

- Under the haematology service at participating sites

- Inpatient and ambulatory patients.
Minimum age
No limit
Maximum age
No limit
Gender
Both males and females
Can healthy volunteers participate?
Yes
Key exclusion criteria
No exclusion criteria

Study design
Purpose
Duration
Selection
Timing
Prospective
Statistical methods / analysis

Recruitment
Recruitment status
Recruiting
Data analysis
Reason for early stopping/withdrawal
Other reasons
Date of first participant enrolment
Anticipated
Actual
Date of last participant enrolment
Anticipated
Actual
Date of last data collection
Anticipated
Actual
Sample size
Target
Accrual to date
Final
Recruitment in Australia
Recruitment state(s)
VIC
Recruitment hospital [1] 0 0
Alfred Health - Melbourne
Recruitment postcode(s) [1] 0 0
- Melbourne

Funding & Sponsors
Primary sponsor type
Other
Name
Bayside Health
Address
Country
Other collaborator category [1] 0 0
Other
Name [1] 0 0
Monash University
Address [1] 0 0
Country [1] 0 0
Other collaborator category [2] 0 0
Other
Name [2] 0 0
Monash Health
Address [2] 0 0
Country [2] 0 0
Other collaborator category [3] 0 0
Other
Name [3] 0 0
Eastern Health
Address [3] 0 0
Country [3] 0 0
Other collaborator category [4] 0 0
Other
Name [4] 0 0
Western Sydney Local Health District
Address [4] 0 0
Country [4] 0 0
Other collaborator category [5] 0 0
Other
Name [5] 0 0
SA Health
Address [5] 0 0
Country [5] 0 0
Other collaborator category [6] 0 0
Other
Name [6] 0 0
Fremantle Hospital and Health Service
Address [6] 0 0
Country [6] 0 0
Other collaborator category [7] 0 0
Other
Name [7] 0 0
Singhealth Foundation
Address [7] 0 0
Country [7] 0 0

Ethics approval
Ethics application status

Summary
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 website
https://clinicaltrials.gov/show/NCT03793231
Trial related presentations / publications
Public notes

Contacts
Principal investigator
Name 0 0
Michelle Dr Ananda-Rajah
Address 0 0
The Alfred
Country 0 0
Phone 0 0
Fax 0 0
Email 0 0
Contact person for public queries
Name 0 0
Address 0 0
Country 0 0
Phone 0 0
Fax 0 0
Email 0 0
Contact person for scientific queries

Summary results
For IPD and results data, please see https://clinicaltrials.gov/show/NCT03793231