scholarly journals A Retrospective Study on The Usefulness of The JJ Risk Engine for Predicting Complications in Type 2 Diabetes Patients

2020 ◽  
Author(s):  
Yasunari Yamashita ◽  
Rina Kitajima ◽  
Kiyoshi Matsubara ◽  
Gaku Inoue ◽  
Hajime Matsubara
Keyword(s):  
Type 2 Diabetes ◽  
Coronary Heart Disease ◽  
Medical Records ◽  
Diabetes Complications ◽  
Low Frequency ◽  
High Dose ◽  
Retrospective Survey ◽  
C Statistic ◽  
Risk Engine

Abstract Objective: The JJ risk engine, developed in 2012, is an algorithm that predicts the 19 incidence of diabetes complications that may develop after 5 to 10 years. However, 20 studies validating the JJ risk engine have not yet been reported;we aimed to verify the 21 JJ risk engine. In 2013, we conducted a retrospective survey using medical records of 22 484 patients with type 2 diabetes. The observed value of coronary heart disease (CHD) 23 complicationsafter 5 years and the predicted value by the JJ risk engine as of 2013 were 24 compared and verified using the discrimination and calibration values.25Results: Among the total cases analyzed, the C-statistic was 0.588,and the calibration 26 was p <0.05; thus, the JJ risk engine could not correctly predict the risk of CHD. However, 27 in the group expected to have a low frequency of hypoglycemia, the C-statistic was 0.646; 28 the predictability of the JJ risk engine was relatively accurate. Further, in the group of 29 patients using high-dose insulin, segregated from the group expected to have a high 30 frequency of hypoglycemia, the C-statistic was 0.866; thus, the JJ risk engine correctly 31 predicted the risk of CHD. Hence, the above results were not consistent in trend.

scholarly journals A retrospective study on the usefulness of the JJ risk engine for predicting the incidence rate of coronary heart disease in type 2 diabetes patients

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yasunari Yamashita ◽  
Rina Kitajima ◽  
Kiyoshi Matsubara ◽  
Gaku Inoue ◽  
Hajime Matsubara
Keyword(s):  
Type 2 Diabetes ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Medical Records ◽  
Low Frequency ◽  
University Hospital ◽  
Diabetes Treatment ◽  
C Statistic ◽  
Risk Engine

Abstract Objective In 2018, we conducted a retrospective survey using the medical records of 484 patients with type 2 diabetes. The observed value of coronary heart disease (CHD) incidence after 5 years and the predicted value by the JJ risk engine as of 2013 were compared and verified using the discrimination and calibration values. Results Among the total cases analyzed, the C-statistic was 0.588, and the calibration was p < 0.05; thus, the JJ risk engine could not correctly predict the risk of CHD. However, in the group expected to have a low frequency of hypoglycemia, the C-statistic was 0.646; the predictability of the JJ risk engine was relatively accurate. Therefore, it is difficult to accurately predict the complication rate of patients using the JJ risk engine based on the diabetes treatment policy after the Kumamoto Declaration 2013. The JJ risk engine has several input items (variables), and it is difficult to satisfy them all unless the environment is well-equipped with testing facilities, such as a university hospital. Therefore, it is necessary to create a new risk engine that requires fewer input items than the JJ risk engine and is applicable to several patients.

scholarly journals Genetic Susceptibility for Coronary Heart Disease and Type 2 Diabetes Complications

2012 ◽  
Vol 58 (5) ◽  
pp. 818-820 ◽  
Author(s):  
Anastasia Z Kalea ◽  
Seamus C Harrison ◽  
Jeffrey W Stephens ◽  
Philippa J Talmud
Keyword(s):  
Type 2 Diabetes ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Genetic Susceptibility ◽  
Diabetes Complications

scholarly journals External validation of the UK Prospective Diabetes Study (UKPDS) risk engine in patients with type 2 diabetes identified in the national diabetes program in Iran

2021 ◽  
Author(s):  
Mehrdad Valipour ◽  
Davood Khalili ◽  
Masoud Soleymani Dodaran ◽  
Seyed Abbas Motevalian ◽  
Mohammad Ebrahim Khamseh ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Heart Disease ◽  
External Validation ◽  
Accurate Estimation ◽  
C Statistic ◽  
Chd Risk ◽  
The Uk ◽  
Risk Engine

Abstract Background Cardiovascular diseases are the first leading cause of mortality in the world. Practical guidelines recommend an accurate estimation of the risk of these events for effective treatment and care. The UK Prospective Diabetes Study (UKPDS) has a risk engine for predicting CHD risk in patients with type 2 diabetes, but in some countries, it has been shown that the risk of CHD is poorly estimated. Hence, we assessed the external validity of the UKPDS risk engine in patients with type 2 diabetes identified in the national diabetes program in Iran. Methods The cohort included 853 patients with type 2diabetes identified between March 21, 2007, and March 20, 2018 in Lorestan province of Iran. Patients were followed for the incidence of CHD. The performance of the models was assessed in terms of discrimination and calibration. Discrimination was examined using the c-statistic and calibration was assessed with the Hosmer–Lemeshow χ2 statistic (HLχ2) test and a calibration plot was depicted to show the predicted risks versus observed ones. Results During 7464.5 person-years of follow-up 170 first Coronary heart disease occurred. The median follow-up was 8.6 years. The UKPDS risk engine showed moderate discrimination for CHD (c-statistic was 0.72 for 10-year risk) and the calibration of the UKPDS risk engine was poor (HLχ2=69.9, p<0.001) and overestimated the risk of heart disease. Conclusion This study shows that the ability of the UKPDS Risk Engine to discriminate patients who got CHD events from those who did not was moderate and the ability of the risk prediction model to accurately predict the absolute risk of CHD (calibration) was poor and it overestimated the CHD risk. To improve the prediction of CHD in patients with type 2 diabetes, this model should be updated in the Iranian diabetic population.

Abstract # P-1: Use of Type 2 Diabetes Screening Guidelines in Primary Care: Concordance Between Physician Survey and Medical Records

2016 ◽  
Vol 22 ◽  
pp. 14
Author(s):  
Michelle Mocarski ◽  
Sandhya Mehta ◽  
Karin Gillespie ◽  
Tami Wisniewski ◽  
K.M. Venkat Narayan ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Medical Records ◽  
Physician Survey ◽  
Diabetes Screening ◽  
Screening Guidelines
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