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. 2020 Dec 18:13:5025-5036.
doi: 10.2147/DMSO.S273880. eCollection 2020.

Nomogram Based on Risk Factors for Type 2 Diabetes Mellitus Patients with Coronary Heart Disease

Rong Shi  1 , Birong Wu  1 , Zheyun Niu  1 , Hui Sun  1 , Fan Hu  1
Affiliations

Nomogram Based on Risk Factors for Type 2 Diabetes Mellitus Patients with Coronary Heart Disease

Rong Shi et al. Diabetes Metab Syndr Obes. .

Abstract

Introduction: This study aimed to study risk factors for coronary heart disease (CHD) in type 2 diabetes mellitus (T2DM) patients and establish a clinical prediction model.

Research design and methods: A total of 3402 T2DM patients were diagnosed by clinical doctors and recorded in the electronic medical record system (EMRS) of six Community Health Center Hospitals from 2015 to 2017, including the communities of Huamu, Jinyang, Yinhang, Siping, Sanlin and Daqiao. From September 2018 to September 2019, 3361 patients (41 patients were missing) were investigated using a questionnaire, physical examination, and biochemical index test. After excluding the uncompleted data, 3214 participants were included in the study and randomly divided into a training set (n = 2252) and a validation set (n = 962) at a ratio of 3:1. Through lead absolute shrinkage and selection operator (LASSO) regression analysis and logistic regression analysis of the training set, risk factors were determined and included in a nomogram. The C-index, receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were used to validate the distinction, calibration and clinical practicality of the model.

Results: Age, T2DM duration, hypertension (HTN), hyperuricaemia (HUA), body mass index (BMI), glycosylated haemoglobin A1c (HbA1c), high-density lipoprotein (HDL-C) and low-density lipoprotein (LDL-C) were significant factors in this study. The C-index was 0.750 (0.724-0.776) based on the training set and 0.767 (0.726-0.808) based on the validation set. Through ROC analysis, the set area was 0.750 for the training set and 0.755 for the validation set. The calibration test indicated that the S:P of the prediction model was 0.982 in the training set and 0.499 in the validation set. The decision curve analysis showed that the threshold probability of the model was 16-69% in the training set and 16-73% in the validation set.

Conclusion: Based on community surveys and data analysis, a prediction model of CHD in T2DM patients was established.

Keywords: coronary heart disease; prediction model; type 2 diabetes mellitus.

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Conflict of interest statement

The authors declare that they have no conflicts of interest for this work.

Figures

Figure 1
Figure 1
The flow chart presents the entire process of patient follow-up, data collection and statistical analysis in this study.
Figure 2
Figure 2
Demographic and clinical feature selection using the LASSO binary logistic regression model in T2DM patients with CHD based on the training set.
Figure 3
Figure 3
Developed nomogram for CHD.
Figure 4
Figure 4
The pooled AUC of the ROC curve.
Figure 5
Figure 5
The calibration curves of the CHD incidence risk prediction in the array.
Figure 6
Figure 6
Decision curve analysis for the incidence risk nomogram of CHD.
See this image and copyright information in PMC

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References

    1. IDF. Available from: https://diabetesatlas.org/en/. Accessed December 10, 2020.
    1. International Diabetes Federation IDF Diabetes Atlas. Epidemiology and morbidity. Available from: https://diabetesatlas.org/en/.Accessed December 10, 2020.
    1. Ramachandran A, Ma RCW, Snehalatha C. Diabetes in asia. Lancet. 2010;375(9712):408–418. doi:10.1016/S0140-6736(09)60937-5 - DOI - PubMed
    1. Hu H, Sawhney M, Shi L. A systematic review of the direct economic burden of type 2 diabetes in China. Diabetes Ther. 2015;6(1):7–16. doi:10.1007/s13300-015-0096-0 - DOI - PMC - PubMed
    1. Dal Canto E, Ceriello A, Ryden L, et al. Diabetes as a cardiovascular risk factor: an overview of global trends of macro and micro vascular complications. Eur J Prev Cardiol. 2019;26(2_SUPPL):25–32. doi:10.1177/2047487319878371 - DOI - PubMed