Using the AUSDRISK score to screen for pre‐diabetes and diabetes in GP practices: a case‐finding approach

Abstract Objective: To identify the optimal AUSDRISK threshold score to screen for pre‐diabetes and diabetes. Methods: A total of 406 adult patients not diagnosed with diabetes were screened in General Practices (GP) between May and October 2019. All patients received a point of care (POC) HbA1c tes...

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Published in:Australian and New Zealand Journal of Public Health
Main Authors: Kerry Fleming, Natasha Weaver, Roseanne Peel, Alexis Hure, Mark McEvoy, Elizabeth Holliday, Martha Parsons, Shamasunder Acharya, Judy Luu, John Wiggers, Chris Rissel, Priyanga Ranasinghe, Ranil Jayawardena, Samir Samman, John Attia
Format: Article
Language:English
Published: Elsevier 2022-04-01
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Online Access:https://doi.org/10.1111/1753-6405.13181
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author Kerry Fleming
Natasha Weaver
Roseanne Peel
Alexis Hure
Mark McEvoy
Elizabeth Holliday
Martha Parsons
Shamasunder Acharya
Judy Luu
John Wiggers
Chris Rissel
Priyanga Ranasinghe
Ranil Jayawardena
Samir Samman
John Attia
author_facet Kerry Fleming
Natasha Weaver
Roseanne Peel
Alexis Hure
Mark McEvoy
Elizabeth Holliday
Martha Parsons
Shamasunder Acharya
Judy Luu
John Wiggers
Chris Rissel
Priyanga Ranasinghe
Ranil Jayawardena
Samir Samman
John Attia
author_sort Kerry Fleming
collection DOAJ
container_title Australian and New Zealand Journal of Public Health
description Abstract Objective: To identify the optimal AUSDRISK threshold score to screen for pre‐diabetes and diabetes. Methods: A total of 406 adult patients not diagnosed with diabetes were screened in General Practices (GP) between May and October 2019. All patients received a point of care (POC) HbA1c test. HbA1c test results were categorised into diabetes (≥6.5% or ≥48 mmol/mol), pre‐diabetes (5.7–6.4% or 39–47 mmol/mol), or normal (<5.7% or 39 mmol/mol). Results: Of these patients, 9 (2%) had undiagnosed diabetes and 60 (15%) had pre‐diabetes. A Receiver Operator Characteristic (ROC) curve was constructed to predict the presence of pre‐diabetes and diabetes; the area under the ROC curve was 0.72 (95%CI 0.65–0.78) indicating modest predictive ability. The optimal threshold cut point for AUSDRISK score was 17 (sensitivity 76%, specificity 61%, + likelihood ratio (LR) 1.96, ‐ likelihood ratio of 0.39) while the accepted cut point of 12 performed less well (sensitivity 94%, specificity 23%, +LR=1.22 ‐LR+0.26). Conclusions: The AUSDRISK tool has the potential to be used as a screening tool for pre‐diabetes/diabetes in GP practices. A cut point of ≥17 would potentially identify 75% of all people at risk and three in 10 sent for further testing would be positive for prediabetes or diabetes. Implications for public health: Routine case‐finding in high‐risk patients will enable GPs to intervene early and prevent further public health burden from the sequelae of diabetes.
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spelling doaj-art-c1f80ae3fa264dbeb372abec2d4fd7072025-08-19T21:51:01ZengElsevierAustralian and New Zealand Journal of Public Health1326-02001753-64052022-04-0146220320710.1111/1753-6405.13181Using the AUSDRISK score to screen for pre‐diabetes and diabetes in GP practices: a case‐finding approachKerry Fleming0Natasha Weaver1Roseanne Peel2Alexis Hure3Mark McEvoy4Elizabeth Holliday5Martha Parsons6Shamasunder Acharya7Judy Luu8John Wiggers9Chris Rissel10Priyanga Ranasinghe11Ranil Jayawardena12Samir Samman13John Attia14School of Medicine and Public Health, College of Health, Medicine and Wellbeing The University of Newcastle New South WalesSchool of Medicine and Public Health, College of Health, Medicine and Wellbeing The University of Newcastle New South WalesSchool of Medicine and Public Health, College of Health, Medicine and Wellbeing The University of Newcastle New South WalesSchool of Medicine and Public Health, College of Health, Medicine and Wellbeing The University of Newcastle New South WalesSchool of Medicine and Public Health, College of Health, Medicine and Wellbeing The University of Newcastle New South WalesSchool of Medicine and Public Health, College of Health, Medicine and Wellbeing The University of Newcastle New South WalesEndocrinology and Diabetes Service and Diabetes Alliance Hunter New England Health Local Health District (HNELHD) New south WalesSchool of Medicine and Public Health, College of Health, Medicine and Wellbeing The University of Newcastle New South WalesEndocrinology and Diabetes Service and Diabetes Alliance Hunter New England Health Local Health District (HNELHD) New south WalesSchool of Medicine and Public Health, College of Health, Medicine and Wellbeing The University of Newcastle New South WalesThe University of Sydney Camperdown New south WalesDepartment of Pharmacology, Faculty of Medicine University of Colombo Sri LankaDepartment of Pharmacology, Faculty of Medicine University of Colombo Sri LankaSchool of Life and Environmental Sciences University of Sydney New South WalesSchool of Medicine and Public Health, College of Health, Medicine and Wellbeing The University of Newcastle New South WalesAbstract Objective: To identify the optimal AUSDRISK threshold score to screen for pre‐diabetes and diabetes. Methods: A total of 406 adult patients not diagnosed with diabetes were screened in General Practices (GP) between May and October 2019. All patients received a point of care (POC) HbA1c test. HbA1c test results were categorised into diabetes (≥6.5% or ≥48 mmol/mol), pre‐diabetes (5.7–6.4% or 39–47 mmol/mol), or normal (<5.7% or 39 mmol/mol). Results: Of these patients, 9 (2%) had undiagnosed diabetes and 60 (15%) had pre‐diabetes. A Receiver Operator Characteristic (ROC) curve was constructed to predict the presence of pre‐diabetes and diabetes; the area under the ROC curve was 0.72 (95%CI 0.65–0.78) indicating modest predictive ability. The optimal threshold cut point for AUSDRISK score was 17 (sensitivity 76%, specificity 61%, + likelihood ratio (LR) 1.96, ‐ likelihood ratio of 0.39) while the accepted cut point of 12 performed less well (sensitivity 94%, specificity 23%, +LR=1.22 ‐LR+0.26). Conclusions: The AUSDRISK tool has the potential to be used as a screening tool for pre‐diabetes/diabetes in GP practices. A cut point of ≥17 would potentially identify 75% of all people at risk and three in 10 sent for further testing would be positive for prediabetes or diabetes. Implications for public health: Routine case‐finding in high‐risk patients will enable GPs to intervene early and prevent further public health burden from the sequelae of diabetes.https://doi.org/10.1111/1753-6405.13181diabetespre‐diabetespreventionprimary careAUSDRISK
spellingShingle Kerry Fleming
Natasha Weaver
Roseanne Peel
Alexis Hure
Mark McEvoy
Elizabeth Holliday
Martha Parsons
Shamasunder Acharya
Judy Luu
John Wiggers
Chris Rissel
Priyanga Ranasinghe
Ranil Jayawardena
Samir Samman
John Attia
Using the AUSDRISK score to screen for pre‐diabetes and diabetes in GP practices: a case‐finding approach
diabetes
pre‐diabetes
prevention
primary care
AUSDRISK
title Using the AUSDRISK score to screen for pre‐diabetes and diabetes in GP practices: a case‐finding approach
title_full Using the AUSDRISK score to screen for pre‐diabetes and diabetes in GP practices: a case‐finding approach
title_fullStr Using the AUSDRISK score to screen for pre‐diabetes and diabetes in GP practices: a case‐finding approach
title_full_unstemmed Using the AUSDRISK score to screen for pre‐diabetes and diabetes in GP practices: a case‐finding approach
title_short Using the AUSDRISK score to screen for pre‐diabetes and diabetes in GP practices: a case‐finding approach
title_sort using the ausdrisk score to screen for pre diabetes and diabetes in gp practices a case finding approach
topic diabetes
pre‐diabetes
prevention
primary care
AUSDRISK
url https://doi.org/10.1111/1753-6405.13181
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