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A potent risk model for predicting new-onset acute coronary syndrome in patients with type 2 diabetes mellitus in Northwest China

Authors
  • Lyu, Jun1
  • Li, Zhiying1
  • Wei, Huiyi2
  • Liu, Dandan1
  • Chi, Xiaoxian1
  • Gong, Da-Wei3
  • Zhao, Qingbin1
  • 1 The First Affiliated Hospital of Xi’an Jiaotong University,
  • 2 The Second Affiliated Middle School of Xi’an Jiaotong University,
  • 3 University of Maryland School of Medicine,
Type
Published Article
Journal
Acta Diabetologica
Publisher
Springer Milan
Publication Date
Feb 01, 2020
Volume
57
Issue
6
Pages
705–713
Identifiers
DOI: 10.1007/s00592-020-01484-x
PMID: 32008161
PMCID: PMC7220880
Source
PubMed Central
Keywords
License
Unknown

Abstract

Aims Type 2 diabetes mellitus (T2DM) is now very prevalent in China. Due to the lower rate of controlled diabetes in China compared to that in developed countries, there is a higher incidence of serious cardiovascular complications, especially acute coronary syndrome (ACS). The aim of this study was to establish a potent risk predictive model in the economically disadvantaged northwest region of China, which could predict the probability of new-onset ACS in patients with T2DM. Methods Of 456 patients with T2DM admitted to the First Affiliated Hospital of Xi’an Jiaotong University from January 2018 to January 2019 and included in this study, 270 had no ACS, while 186 had newly diagnosed ACS. Overall, 32 demographic characteristics and serum biomarkers of the study patients were analysed. The least absolute shrinkage and selection operator regression was used to select variables, while the multivariate logistic regression was used to establish the predictive model that was presented using a nomogram. The area under the receiver operating characteristics curve (AUC) was used to evaluate the discriminatory capacity of the model. A calibration plot and Hosmer–Lemeshow test were used for the calibration of the predictive model, while the decision curve analysis (DCA) was used to evaluate its clinical validity. Results After random sampling, 319 and 137 T2DM patients were included in the training and validation sets, respectively. The predictive model included age, body mass index, diabetes duration, systolic blood pressure (SBP), diastolic blood pressure (DBP), low-density lipoprotein cholesterol, serum uric acid, lipoprotein(a), hypertension history and alcohol drinking status as predictors. The AUC of the predictive model and that of the internal validation set was 0.830 [95% confidence interval (CI) 0.786–0.874] and 0.827 (95% CI 0.756–0.899), respectively. The predictive model showed very good fitting degree, and DCA demonstrated a clinically effective predictive model. Conclusions A potent risk predictive model was established, which is of great value for the secondary prevention of diabetes. Weight loss, lowering of SBP and blood uric acid levels and appropriate control for DBP may significantly reduce the risk of new-onset ACS in T2DM patients in Northwest China.

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