374-P: External Validation and Clinical Application of the Risk Prediction Model for Severe Hypoglycemia in Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 374-P
Author(s):  
YU-BAE AHN ◽  
JAE-SEUNG YUN ◽  
JUNG-MIN LEE ◽  
KI-HO SONG ◽  
SEUNG-HYUN KO
Keyword(s):  
Type 2 Diabetes ◽  
Prediction Model ◽  
Risk Prediction ◽  
Clinical Application ◽  
External Validation ◽  
Severe Hypoglycemia ◽  
Risk Prediction Model

scholarly journals A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus

2019 ◽  
Vol 43 (3) ◽  
pp. 275-283 ◽  
Author(s):  
Brent A. Williams ◽  
Daniela Geba ◽  
Jeanine M. Cordova ◽  
Sharash S. Shetty
Keyword(s):  
Diabetes Mellitus ◽  
Heart Failure ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Heart Failure Hospitalization

Cardiovascular risk prediction model for Omanis with type 2 diabetes

2018 ◽  
Vol 12 (2) ◽  
pp. 105-110 ◽  
Author(s):  
Abdul Hakeem Alrawahi ◽  
Patricia Lee ◽  
Zaher A.M. Al-Anqoudi ◽  
Muna Alrabaani ◽  
Ahmed Al-Busaidi ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Cardiovascular Risk Prediction

scholarly journals Diabetes after pregnancy: a study protocol for the derivation and validation of a risk prediction model for 5-year risk of diabetes following pregnancy

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Stephanie H. Read ◽  
Laura C. Rosella ◽  
Howard Berger ◽  
Denice S. Feig ◽  
Karen Fleming ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Study Protocol ◽  
Large Population ◽  
External Validation ◽  
Population Based ◽  
Lifestyle Changes ◽  
Risk Prediction Model ◽  
Type 2 Dm

Abstract Background Pregnancy offers a unique opportunity to identify women at higher future risk of type 2 diabetes mellitus (DM). In pregnancy, a woman has greater engagement with the healthcare system, and certain conditions are more apt to manifest, such as gestational DM (GDM) that are important markers for future DM risk. This study protocol describes the development and validation of a risk prediction model (RPM) for estimating a woman’s 5-year risk of developing type 2 DM after pregnancy. Methods Data will be obtained from existing Ontario population-based administrative datasets. The derivation cohort will consist of all women who gave birth in Ontario, Canada between April 2006 and March 2014. Pre-specified predictors will include socio-demographic factors (age at delivery, ethnicity), maternal clinical factors (e.g., body mass index), pregnancy-related events (gestational DM, hypertensive disorders of pregnancy), and newborn factors (birthweight percentile). Incident type 2 DM will be identified by linkage to the Ontario Diabetes Database. Weibull accelerated failure time models will be developed to predict 5-year risk of type 2 DM. Measures of predictive accuracy (Nagelkerke’s R2), discrimination (C-statistics), and calibration plots will be generated. Internal validation will be conducted using a bootstrapping approach in 500 samples with replacement, and an optimism-corrected C-statistic will be calculated. External validation of the RPM will be conducted by applying the model in a large population-based pregnancy cohort in Alberta, and estimating the above measures of model performance. The model will be re-calibrated by adjusting baseline hazards and coefficients where appropriate. Discussion The derived RPM may help identify women at high risk of developing DM in a 5-year period after pregnancy, thus facilitate lifestyle changes for women at higher risk, as well as more frequent screening for type 2 DM after pregnancy.

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