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PROSAIC-DS Study (PROState AI in Cancer - Decision Support) (PROSAIC-DS)

Primary Purpose

Prostate Cancer

Status
Not yet recruiting
Phase
Not Applicable
Locations
United Kingdom
Study Type
Interventional
Intervention
Clinical decision support tool recommended outcome
Sponsored by
Guy's and St Thomas' NHS Foundation Trust
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional other trial for Prostate Cancer

Eligibility Criteria

35 Years - undefined (Adult, Older Adult)MaleAccepts Healthy Volunteers

Inclusion Criteria:

  • All patients referred to the GSTT and KCH Prostate MDT meetings where sufficient information is available for the MDT to make a treatment decision (approximately 40-50 per week) will be eligible for the study.

Exclusion Criteria:

  • If data available for patients is not adequate to make any treatment decisions they will be excluded. Non-consenting patients will be excluded.

Sites / Locations

  • Guys and St Thomas Hospitals
  • Kings College Hospital

Arms of the Study

Arm 1

Arm 2

Arm Type

Other

Other

Arm Label

Arm A: Visible to MDTM

Arm B: Not-visible to MDTM

Arm Description

Patients going through this arm have the decision support tool outcome visible to the MDTM

Patients going through this arm will not have the decision support tool outcome visible to the MDTM

Outcomes

Primary Outcome Measures

PROSAIC-DS as a triage tool
The % of patients the PROSAIC-DS tool can appropriately triage away non-complex Prostate Cancer cases from the MDTM with appropriate treatment plans as directed by approved guidelines (EAU, BAUS, NICE, AUA).
PROSAIC-DS influence on MDTM concordance with approved guidelines
Evaluation of PROSAIC-DS as a member of the MDTM via the impact of live PROSAIC-DS recommendations on MDTM decision concordance with approved guidelines (EAU, BAUS, NICE, AUA) on randomised patients discussed in the MDTM. This is measured through the difference in level of concordance between the MDTM with PROSAIC-DS switched off and the MDTM with PROSAIC-DS on when less complex cases (ones triaged away) are excluded.

Secondary Outcome Measures

Cost effectiveness of PROSAIC-DS
Estimate the financial savings that PROSAIC-DS brings by triaging non-complex cases away from the MDTM.
Qualitative Analysis: Patient acceptability
We will use qualitative methods, and data capture methods including self-administered questionnaires, focus groups, semi-structured interviews and video analysis, to explore patient and staff views about, including concerns and satisfaction with, the use of AI technology to support clinical decisions in prostate cancer, both prior to their engagement with PROSAIC-DS, during and after deployment of the tool of the technology to patients.

Full Information

First Posted
February 16, 2022
Last Updated
April 27, 2022
Sponsor
Guy's and St Thomas' NHS Foundation Trust
Collaborators
King's College Hospital NHS Trust, King's College London, Deontics Limited, Somerset NHS Foundation Trust, Prostate Cancer UK
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1. Study Identification

Unique Protocol Identification Number
NCT05355727
Brief Title
PROSAIC-DS Study (PROState AI in Cancer - Decision Support)
Acronym
PROSAIC-DS
Official Title
PROSAIC-DS (PROState AI in Cancer - Decision Support): Evaluation of the Deontics AI Platform for Evidence-based Treatment Planning in Multidisciplinary Cancer Care: Increasing Compliance and Streamlining MDTs in Prostate Cancer
Study Type
Interventional

2. Study Status

Record Verification Date
April 2022
Overall Recruitment Status
Not yet recruiting
Study Start Date
June 2022 (Anticipated)
Primary Completion Date
December 2022 (Anticipated)
Study Completion Date
December 2022 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Guy's and St Thomas' NHS Foundation Trust
Collaborators
King's College Hospital NHS Trust, King's College London, Deontics Limited, Somerset NHS Foundation Trust, Prostate Cancer UK

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No

5. Study Description

Brief Summary
Around 375,000 cancers are diagnosed in the UK annually, with this figure expected to reach 500,000 by 2035. As the number of different cancer treatment options and our scientific understanding continue to grow rapidly, it can be difficult for clinicians to keep up-to-date with best practice, causing unjustified variations in the quality of care and clinical outcomes for patients. Currently, when a patient has been referred to and seen by a clinician, their treatment is then discussed in a Multi-Disciplinary Team Meeting (MDTM). MDTM is a meeting of medical experts, including Surgeons, Oncologists, Nurses, and specialists in cancer, imaging and diagnosis. This is the case even if a treatment decision is straightforward. A nationwide review published by CRUK in 2017 highlighted the demands on cancer teams and the MDTM process: Increased caseloads are causing dramatic increases in the time spent by clinicians in MDTMs, leading to an unsustainable rise in costs: the cost in England has increased from £88m to £159m in 4 years; There is not enough time in the MDTM to discuss complex cases; There is a failure to involve patients in the decision-making process: around 75% of patients feel their views are unrepresented in MDTMs; In our study we are looking at the potential of technology - particularly Clinical Decision Support Systems (CDSS) - to improve MDTM decision making. Deontics has a CE marked AI-based CDSS that integrates individual patient data and preferences with evidence-based clinical guidelines. This dynamically and transparently generates best-practice, individualised treatment recommendations which can help determine treatment. Deontics' AI tool has already been shown to provide personalised recommendations concordant with UK best practice while incorporating patient values, and can be used to safely triage less complex patients straight to treatment with minimal clinical oversight. Our project partners with Deontics to develop PROSAIC-DS - A CDSS for prostate cancer.
Detailed Description
I

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Prostate Cancer

7. Study Design

Primary Purpose
Other
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Masking
ParticipantOutcomes Assessor
Allocation
Randomized
Enrollment
1040 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
Arm A: Visible to MDTM
Arm Type
Other
Arm Description
Patients going through this arm have the decision support tool outcome visible to the MDTM
Arm Title
Arm B: Not-visible to MDTM
Arm Type
Other
Arm Description
Patients going through this arm will not have the decision support tool outcome visible to the MDTM
Intervention Type
Other
Intervention Name(s)
Clinical decision support tool recommended outcome
Intervention Description
The PROSAIC-DS tool will take the variables and produce a suggested outcome. It will supply supporting evidence and best practice for its recommendations
Primary Outcome Measure Information:
Title
PROSAIC-DS as a triage tool
Description
The % of patients the PROSAIC-DS tool can appropriately triage away non-complex Prostate Cancer cases from the MDTM with appropriate treatment plans as directed by approved guidelines (EAU, BAUS, NICE, AUA).
Time Frame
6-9 months
Title
PROSAIC-DS influence on MDTM concordance with approved guidelines
Description
Evaluation of PROSAIC-DS as a member of the MDTM via the impact of live PROSAIC-DS recommendations on MDTM decision concordance with approved guidelines (EAU, BAUS, NICE, AUA) on randomised patients discussed in the MDTM. This is measured through the difference in level of concordance between the MDTM with PROSAIC-DS switched off and the MDTM with PROSAIC-DS on when less complex cases (ones triaged away) are excluded.
Time Frame
6-9 months
Secondary Outcome Measure Information:
Title
Cost effectiveness of PROSAIC-DS
Description
Estimate the financial savings that PROSAIC-DS brings by triaging non-complex cases away from the MDTM.
Time Frame
6-9 months - duration of data collection
Title
Qualitative Analysis: Patient acceptability
Description
We will use qualitative methods, and data capture methods including self-administered questionnaires, focus groups, semi-structured interviews and video analysis, to explore patient and staff views about, including concerns and satisfaction with, the use of AI technology to support clinical decisions in prostate cancer, both prior to their engagement with PROSAIC-DS, during and after deployment of the tool of the technology to patients.
Time Frame
12 months

10. Eligibility

Sex
Male
Minimum Age & Unit of Time
35 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
Eligibility Criteria
Inclusion Criteria: All patients referred to the GSTT and KCH Prostate MDT meetings where sufficient information is available for the MDT to make a treatment decision (approximately 40-50 per week) will be eligible for the study. Exclusion Criteria: If data available for patients is not adequate to make any treatment decisions they will be excluded. Non-consenting patients will be excluded.
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Danny Ruta, MBBS MSc
Phone
+44 (0)7969917016
Email
Danny.Ruta@gstt.nhs.uk
First Name & Middle Initial & Last Name or Official Title & Degree
Kate Dodgson, LLB LLM
Phone
+447704783880
Email
kate.dodgson@deontics.com
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Danny Ruta, MBBS MSc
Organizational Affiliation
Guys and St Thomas NHS Foundation Trust
Official's Role
Principal Investigator
Facility Information:
Facility Name
Guys and St Thomas Hospitals
City
London
ZIP/Postal Code
SE1 9RT
Country
United Kingdom
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Danny Ruta, MBBS MSc
Phone
+44 (0)7969917016
Email
Danny.Ruta@gstt.nhs.uk
Facility Name
Kings College Hospital
City
London
ZIP/Postal Code
SE5 9RS
Country
United Kingdom
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Jonathan Makanjuola, MBBS BSc
Phone
+447939512119
Email
jonathan.makanjuola@nhs.net

12. IPD Sharing Statement

Plan to Share IPD
No
Citations:
PubMed Identifier
30028418
Citation
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Results Reference
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PubMed Identifier
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Citation
Taylor C, Atkins L, Richardson A, Tarrant R, Ramirez AJ. Measuring the quality of MDT working: an observational approach. BMC Cancer. 2012 May 29;12:202. doi: 10.1186/1471-2407-12-202.
Results Reference
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PubMed Identifier
26365047
Citation
Munro AJ. Multidisciplinary Team Meetings in Cancer Care: An Idea Whose Time has Gone? Clin Oncol (R Coll Radiol). 2015 Dec;27(12):728-31. doi: 10.1016/j.clon.2015.08.008. Epub 2015 Sep 11. No abstract available.
Results Reference
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PubMed Identifier
22295234
Citation
Patkar V, Acosta D, Davidson T, Jones A, Fox J, Keshtgar M. Cancer multidisciplinary team meetings: evidence, challenges, and the role of clinical decision support technology. Int J Breast Cancer. 2011;2011:831605. doi: 10.4061/2011/831605. Epub 2011 Jul 17.
Results Reference
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PubMed Identifier
28341685
Citation
Miles A, Chronakis I, Fox J, Mayer A. Use of a computerised decision aid (DA) to inform the decision process on adjuvant chemotherapy in patients with stage II colorectal cancer: development and preliminary evaluation. BMJ Open. 2017 Mar 24;7(3):e012935. doi: 10.1136/bmjopen-2016-012935.
Results Reference
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PubMed Identifier
22734113
Citation
Patkar V, Acosta D, Davidson T, Jones A, Fox J, Keshtgar M. Using computerised decision support to improve compliance of cancer multidisciplinary meetings with evidence-based guidance. BMJ Open. 2012 Jun 25;2(3):e000439. doi: 10.1136/bmjopen-2011-000439. Print 2012.
Results Reference
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PubMed Identifier
17117181
Citation
Patkar V, Hurt C, Steele R, Love S, Purushotham A, Williams M, Thomson R, Fox J. Evidence-based guidelines and decision support services: A discussion and evaluation in triple assessment of suspected breast cancer. Br J Cancer. 2006 Dec 4;95(11):1490-6. doi: 10.1038/sj.bjc.6603470. Epub 2006 Nov 21.
Results Reference
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PubMed Identifier
24246343
Citation
Peleg M, Fox J, Patkar V, Glasspool D, Chronakis I, South M, Nassar S, Gaglia JL, Gharib H, Papini E, Paschke R, Duick DS, Valcavi R, Hegedus L, Garber JR. A Computer-Interpretable Version of the AACE, AME, ETA Medical Guidelines for Clinical Practice for the Diagnosis and Management of Thyroid Nodules. Endocr Pract. 2014 Apr;20(4):352-9. doi: 10.4158/EP13271.OR.
Results Reference
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PubMed Identifier
15953000
Citation
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Citation
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Citation
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Results Reference
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Links:
URL
https://www.cancerresearchuk.org/sites/default/files/full_report_meeting_patients_needs_improving_the_effectiveness_of_multidisciplinary_team_meetings_.pdf
Description
Meeting Patients Needs: Improving the effectiveness of Multidisciplinary Team Meetings in Cancer Services. 2018
URL
https://www.england.nhs.uk/wp-content/uploads/sites/6/2018/10/Transforming-MDTM-Martin-Gore-August-2017.pdf
Description
Transforming Multidisciplinary Team Meetings (MDTMs): Report by Prof Martin Gore (2017) for NHS England
URL
https://www.england.nhs.uk/wp-content/uploads/2020/01/multi-disciplinary-team-streamlining-guidance.pdf
Description
NHS England : Streamlining Multi-Disciplinary Team Meetings

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PROSAIC-DS Study (PROState AI in Cancer - Decision Support)

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