scholarly journals Guest Editorial: Commentary: risk prediction models for people with Type 2 diabetes

2012 ◽  
Vol 4 (3) ◽  
pp. 180
Author(s):  
Kamlesh Khunti
Keyword(s):  
Type 2 Diabetes ◽  
Risk Prediction ◽  
Guest Editorial ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Editorial Commentary

scholarly journals The Construction of Risk Prediction Models Using GWAS Data and Its Application to a Type 2 Diabetes Prospective Cohort

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e92549 ◽  
Author(s):  
Daichi Shigemizu ◽  
Testuo Abe ◽  
Takashi Morizono ◽  
Todd A. Johnson ◽  
Keith A. Boroevich ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Risk Prediction ◽  
Prospective Cohort ◽  
Prediction Models ◽  
Gwas Data ◽  
Risk Prediction Models

scholarly journals Testing the performance of risk prediction models to determine progression to referable diabetic retinopathy in an Irish type 2 diabetes cohort

2021 ◽  
pp. bjophthalmol-2020-318570
Author(s):  
John J Smith ◽  
David M Wright ◽  
Irene M Stratton ◽  
Peter Henry Scanlon ◽  
Noemi Lois
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Risk Prediction ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Risk Prediction Models ◽  
Receiver Operating Characteristic Curves ◽  
Risk Of Progression ◽  
Using Data

Background /AimsTo evaluate the performance of existing prediction models to determine risk of progression to referable diabetic retinopathy (RDR) using data from a prospective Irish cohort of people with type 2 diabetes (T2D).MethodsA cohort of 939 people with T2D followed prospectively was used to test the performance of risk prediction models developed in Gloucester, UK, and Iceland. Observed risk of progression to RDR in the Irish cohort was compared with that derived from each of the prediction models evaluated. Receiver operating characteristic curves assessed models’ performance.ResultsThe cohort was followed for a total of 2929 person years during which 2906 screening episodes occurred. Among 939 individuals followed, there were 40 referrals (4%) for diabetic maculopathy, pre-proliferative DR and proliferative DR. The original Gloucester model, which includes results of two consecutive retinal screenings; a model incorporating, in addition, systemic biomarkers (HbA1c and serum cholesterol); and a model including results of one retinopathy screening, HbA1c, total cholesterol and duration of diabetes, had acceptable discriminatory power (area under the curve (AUC) of 0.69, 0.76 and 0.77, respectively). The Icelandic model, which combined retinopathy grading, duration and type of diabetes, HbA1c and systolic blood pressure, performed very similarly (AUC of 0.74).ConclusionIn an Irish cohort of people with T2D, the prediction models tested had an acceptable performance identifying those at risk of progression to RDR. These risk models would be useful in establishing more personalised screening intervals for people with T2D.

scholarly journals Development and validation of resource-driven risk prediction models for incident chronic kidney disease in type 2 diabetes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sarega Gurudas ◽  
Manjula Nugawela ◽  
A. Toby Prevost ◽  
Thirunavukkarasu Sathish ◽  
Rohini Mathur ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
Study Cohort ◽  
Risk Models ◽  
Risk Prediction Models ◽  
Development And Validation

AbstractPrediction models for population-based screening need, for global usage, to be resource-driven, involving predictors that are affordably resourced. Here, we report the development and validation of three resource-driven risk models to identify people with type 2 diabetes (T2DM) at risk of stage 3 CKD defined by a decline in estimated glomerular filtration rate (eGFR) to below 60 mL/min/1.73m2. The observational study cohort used for model development consisted of data from a primary care dataset of 20,510 multi-ethnic individuals with T2DM from London, UK (2007–2018). Discrimination and calibration of the resulting prediction models developed using cox regression were assessed using the c-statistic and calibration slope, respectively. Models were internally validated using tenfold cross-validation and externally validated on 13,346 primary care individuals from Wales, UK. The simplest model was simplified into a risk score to enable implementation in community-based medicine. The derived full model included demographic, laboratory parameters, medication-use, cardiovascular disease history (CVD) and sight threatening retinopathy status (STDR). Two less resource-intense models were developed by excluding CVD and STDR in the second model and HbA1c and HDL in the third model. All three 5-year risk models had good internal discrimination and calibration (optimism adjusted C-statistics were each 0.85 and calibration slopes 0.999–1.002). In Wales, models achieved excellent discrimination(c-statistics ranged 0.82–0.83). Calibration slopes at 5-years suggested models over-predicted risks, however were successfully updated to accommodate reduced incidence of stage 3 CKD in Wales, which improved their alignment with the observed rates in Wales (E/O ratios near to 1). The risk score demonstrated similar model performance compared to direct evaluation of the cox model. These resource-driven risk prediction models may enable universal screening for Stage 3 CKD to enable targeted early optimisation of risk factors for CKD.

scholarly journals Racial Differences in the Performance of Existing Risk Prediction Models for Incident Type 2 Diabetes: The CARDIA Study

Diabetes Care ◽  
2015 ◽  
pp. dc150509 ◽  
Author(s):  
Mary E. Lacy ◽  
Gregory A. Wellenius ◽  
Mercedes R. Carnethon ◽  
Eric B. Loucks ◽  
April P. Carson ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Racial Differences ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Incident Type ◽  
Incident Type 2 Diabetes

scholarly journals Development and validation of risk prediction models for multiple cardiovascular diseases and Type 2 diabetes

PLoS ONE ◽  
2020 ◽  
Vol 15 (7) ◽  
pp. e0235758 ◽  
Author(s):  
Mehrdad Rezaee ◽  
Igor Putrenko ◽  
Arsia Takeh ◽  
Andrea Ganna ◽  
Erik Ingelsson
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Diseases ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models ◽  
Development And Validation
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