Dataset Information

Development and validation of an equation to predict the incidence of coronary heart disease in patients with type 2 diabetes in Japan.

ABSTRACT:

Objective

In the diabetes treatment policy after the Kumamoto Declaration 2013, it is difficult to accurately predict the incidence of complications in patients using the JJ risk engine. This study was conducted to develop a prediction equation suitable for the current diabetes treatment policy using patient data from Kitasato University Kitasato Institute Hospital (Hospital A) and to externally validate the developed equation using patient data from Kitasato University Hospital (Hospital B). Outlier tests were performed on the patient data from Hospital A to exclude the outliers. Prediction equation was developed using the patient data excluding the outliers and was subjected to external validation.

Results

By excluding outlier data, we could develop a new prediction equation for the incidence of coronary heart disease (CHD) as a complication of type 2 diabetes, incorporating the use of antidiabetic drugs with a high risk of hypoglycemia. This is the first prediction equation in Japan that incorporates the use of antidiabetic drugs. We believe that it will be useful in preventive medicine for treatment for people at high risk of CHD as a complication of diabetes or other diseases. In the future, we would like to confirm the accuracy of this equation at other facilities.

SUBMITTER: Yamashita Y

PROVIDER: S-EPMC8613942 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Publications

Development and validation of an equation to predict the incidence of coronary heart disease in patients with type 2 diabetes in Japan.

Yamashita Yasunari Y Inoue Gaku G Nozaki Yoichi Y Kitajima Rina R Matsubara Kiyoshi K Horii Takeshi T Mohri Junichi J Atsuda Koichiro K Matsubara Hajime H

BMC research notes 20211125 1

<h4>Objective</h4>In the diabetes treatment policy after the Kumamoto Declaration 2013, it is difficult to accurately predict the incidence of complications in patients using the JJ risk engine. This study was conducted to develop a prediction equation suitable for the current diabetes treatment policy using patient data from Kitasato University Kitasato Institute Hospital (Hospital A) and to externally validate the developed equation using patient data from Kitasato University Hospital (Hospita ...[more]

PMID: 34823578

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Project description:PurposeSnoring or obstructive sleep apnea, with or without uncontrolled hypertension, is common and significantly increases the risk of coronary heart disease (CHD). The aim of this study was to develop and validate a prognostic model to predict and identify high-risk patients for CHD among snorers with uncontrolled hypertension.MethodsRecords from 1,822 snorers with uncontrolled hypertension were randomly divided into a training set (n = 1,275, 70%) and validation set (n = 547, 30%). Predictors for CHD were extracted to construct a nomogram model based on multivariate Cox regression analysis. We performed a single-split verification and 1,000 bootstraps resampling internal validation to assess the discrimination and consistency of the prediction model using area under the receiver operating characteristic curve (AUC) and calibration plots. Based on the linear predictors, a risk classifier for CHD could be set.ResultsAge, waist circumference (WC), and high- and low-density lipoprotein cholesterol (HDL-C and LDL-C) were extracted as the predictors to generate this nomogram model. The C-index was 0.720 (95% confidence interval 0.663-0.777) in the derivation cohort and 0.703 (0.630-0.776) in the validation cohort. The AUC was 0.757 (0.626-0.887), 0.739 (0.647-0.831), and 0.732 (0.665-0.799) in the training set and 0.689 (0.542-0.837), 0.701 (0.606-0.796), and 0.712 (0.615-0.808) in the validation set at 3, 5, and 8 years, respectively. The calibration plots showed acceptable consistency between the probability of CHD-free survival and the observed CHD-free survival in the training and validation sets. A total of more than 134 points in the nomogram can be used in the identification of high-risk patients for CHD among snorers with uncontrolled hypertension.ConclusionWe developed a CHD risk prediction model in snorers with uncontrolled hypertension, which includes age, WC, HDL-C, and LDL-C, and can help clinicians with early and quick identification of patients with a high risk for CHD.

Dataset Information

Development and validation of an equation to predict the incidence of coronary heart disease in patients with type 2 diabetes in Japan.

Objective

Results

Publications

Development and validation of an equation to predict the incidence of coronary heart disease in patients with type 2 diabetes in Japan.

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