Congruence between the Indian Diabetes Risk Score and Australian Type 2 Diabetes Risk Assessment tool screening in Asian-Indians

2013 ◽  
Vol 21 (2) ◽  
pp. 36-39
Author(s):  
Ritin S Fernandez ◽  
Steven Frost
Keyword(s):  
Type 2 Diabetes ◽  
Risk Assessment ◽  
Risk Score ◽  
Asian Indians ◽  
Assessment Tool ◽  
Diabetes Risk ◽  
Risk Assessment Tool ◽  
Diabetes Risk Score

AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures

2010 ◽  
Vol 192 (4) ◽  
pp. 197-202 ◽  
Author(s):  
Lei Chen ◽  
Dianna J Magliano ◽  
Beverley Balkau ◽  
Stephen Colagiuri ◽  
Paul Z Zimmet ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Assessment ◽  
Assessment Tool ◽  
Diabetes Risk ◽  
Risk Assessment Tool ◽  
Anthropometric Measures

A Risk Assessment Tool for Identifying Osteoporosis in Older Women with Type 2 Diabetes Mellitus

2021 ◽  
Author(s):  
Fenghui Pan ◽  
Wenxia Cui ◽  
Lei Gao ◽  
Xiaoting Shi ◽  
Mingrui Zhang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Risk Assessment ◽  
Type 2 Diabetes Mellitus ◽  
Older Women ◽  
Risk Score ◽  
Assessment Tool ◽  
Risk Assessment Tool ◽  
Characteristic Analysis

Abstract Purpose To develop a simple and clinically useful assessment tool for osteoporosis in older women with type 2 diabetes mellitus (T2DM). Methods A total of 601 women over 60 years of age with T2DM were enrolled in this study. The levels of serum sex hormones and bone metabolism markers were compared between the osteoporosis and non-osteoporosis groups. The least absolute shrinkage and selection operator regularization (LASSO) model was applied to generate a risk assessment tool. The risk score formula was evaluated using receiver operating characteristic analysis and the relationship between the risk score and the bone mineral density (BMD) and T-value were investigated. Results Serum sex hormone-binding globulin (SHBG), cross-linked C-telopeptide of type 1 collagen (CTX), and osteocalcin (OC) were significantly higher in the osteoporosis group. After adjustment for age and body mass index (BMI), SHBG was found to be correlated with the T-value or BMD. Then, a risk score was specifically generated with age, BMI, SHBG, and CTX using the LASSO model. The risk score was significantly negatively correlated with the T-value and BMD of the lumbar spine, femoral neck, and total hip (all P<0.05). Conclusion A risk score using age, BMI, SHBG, and CTX performs well for identifying osteoporosis in older women with T2DM.

AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures

2010 ◽  
Vol 192 (5) ◽  
pp. 274-274 ◽  
Author(s):  
Lei Chen ◽  
Dianna J Magliano ◽  
Beverley Balkau ◽  
Stephen Colagiuri ◽  
Paul Z Zimmet ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Assessment ◽  
Assessment Tool ◽  
Diabetes Risk ◽  
Risk Assessment Tool ◽  
Anthropometric Measures

scholarly journals Validity of the Finnish Diabetes Risk Score for Detecting Undiagnosed Type 2 Diabetes among General Medical Outpatients in Botswana

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Bernard Omech ◽  
Julius Chacha Mwita ◽  
Jose-Gaby Tshikuka ◽  
Billy Tsima ◽  
Oathokwa Nkomazna ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Predictive Value ◽  
Total Population ◽  
Diabetes Risk ◽  
Undiagnosed Diabetes ◽  
Haemoglobin A1c ◽  
Diabetes Risk Score ◽  
Medical Outpatients

This was a cross-sectional study designed to assess the validity of the Finnish Diabetes Risk Score for detecting undiagnosed type 2 diabetes among general medical outpatients in Botswana. Participants aged ≥20 years without previously diagnosed diabetes were screened by (1) an 8-item Finnish diabetes risk assessment questionnaire and (2) Haemoglobin A1c test. Data from 291 participants were analyzed (74.2% were females). The mean age of the participants was 50.1 (SD = ±11) years, and the prevalence of undiagnosed diabetes was 42 (14.4%) with no significant differences between the gender (20% versus 12.5%,P=0.26). The area under curve for detecting undiagnosed diabetes was 0.63 (95% CI 0.55–0.72) for the total population, 0.65 (95% CI: 0.56–0.75) for women, and 0.67 (95% CI: 0.52–0.83) for men. The optimal cut-off point for detecting undiagnosed diabetes was 17 (sensitivity = 48% and specificity = 73%) for the total population, 17 (sensitivity = 56% and specificity = 66%) for females, and 13 (sensitivity = 53% and specificity = 77%) for males. The positive predictive value and negative predictive value were 20% and 89.5%, respectively. The findings indicate that the Finnish questionnaire was only modestly effective in predicting undiagnosed diabetes among outpatients in Botswana.

Comparison of IDRS and ADA Diabetes Risk Assessment Tools Reveals Different Rsk Score in Same Population: A Cross-Sectional Analysis in a Tertiary Care Hospital

2021 ◽  
Vol 5 (1) ◽  
pp. 10-18
Author(s):  
N. Akter ◽  
N.K. Qureshi
Keyword(s):  
Type 2 Diabetes ◽  
Risk Assessment ◽  
High Risk ◽  
Risk Score ◽  
Diabetes Risk ◽  
Assessment Tools ◽  
Cross Sectional ◽  
Risk Assessment Tools ◽  
Predicted Risk

Background: To identify individuals at high risk of developing type2 diabetes (T2DM), use of a validated risk-assessment tool is currently recommended. Nevertheless, recent studies have shown that risk scores that are developed in the same country can lead to different results of an individual. The Objective of study was to reveal whether two different risk-assessment tools predict similar or dissimilar high-risk score in same population. Method: This cross-sectional analytical study was carried upon 336 non-diabetic adults visiting the outpatient department (OPD) of Medicine, MARKS Medical College & Hospital, Bangladesh from October 2018 to March 2019. Woman having previous history of Gestational Diabetes Mellitus (GDM) were also included. Both the Indian Diabetes risk Score (IDRS) and the American Diabetes (ADA) Risk Score questionnaire were used to collect the data on demographic and clinical characteristics, different risk factors of an individual subject, and to calculate predicted risk score for developing T2DM. Results: Among 336 subjects, 53.6% were female. The mean (±SD) age of the study subjects was 38.25±1.12 years. The average IDRS predicted risk score of developing T2DM was more in female subjects than male [p<0.05]. Whereas the ADA predicted increased risk score of developing type 2 diabetes was more in male subjects than female (p<0.05). IDRS categorized 37.2 % of individuals at high risk for developing diabetes; [p=0.10], while the ADA risk tool categorized 20.2% subjects in high risk group; [p<0.001]. Conclusions: The results indicate that risk for developing type 2 diabetes varies considerably according to the scoring system used. To adequately prevent T2DM, risk scoring systems must be validated for each population considered.

scholarly journals The Efficacy of New Chinese Diabetes Risk Score in Screening Undiagnosed Type 2 Diabetes and Prediabetes: A Community-Based Cross-Sectional Study in Eastern China

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Tao Mao ◽  
Jiayan Chen ◽  
Haijian Guo ◽  
Chen Qu ◽  
Chu He ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Score ◽  
Eastern China ◽  
Cross Sectional Study ◽  
Diabetes Risk ◽  
Undiagnosed Diabetes ◽  
Cross Sectional ◽  
Community Residents ◽  
Diabetes Risk Score

The New Chinese Diabetes Risk Score (NCDRS) is one of the recommended tools for screening undiagnosed type 2 diabetes in China. However, its performance in detecting undiagnosed diabetes needs to be verified in different community populations. Also, it is unknown whether NCDRS can be used in detecting prediabetes. In the present study, we aimed to evaluate the performance of NCDRS in detecting undiagnosed diabetes and prediabetes among the community residents in eastern China. We applied NCDRS in 7675 community residents aged 18-65 years old in Jiangsu Province. The results showed that the participants with undiagnosed diabetes reported the highest NCDRS value, followed by those with prediabetes (P<0.001). The best cut-off points of NCDRS for detecting undiagnosed diabetes and prediabetes were 27 (with a sensitivity of 78.0% and a specificity of 57.7%) and 27 (with a sensitivity of 66.0% and a specificity of 62.9%). The AUCs of NCDRS for identifying undiagnosed diabetes and prediabetes were 0.749 (95% CI: 0.739~0.759) and 0.694 (95% CI: 0.683~0.705). These results demonstrate the excellent performance of NCDRS in screening undiagnosed diabetes in the community population in eastern China and further provide evidence for using NCDRS in detecting prediabetes.

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