PMID- 32008161
OWN - NLM
STAT- MEDLINE
DCOM- 20200820
LR  - 20220601
IS  - 1432-5233 (Electronic)
IS  - 0940-5429 (Print)
IS  - 0940-5429 (Linking)
VI  - 57
IP  - 6
DP  - 2020 Jun
TI  - A potent risk model for predicting new-onset acute coronary syndrome in patients 
      with type 2 diabetes mellitus in Northwest China.
PG  - 705-713
LID - 10.1007/s00592-020-01484-x [doi]
AB  - 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.
FAU - Lyu, Jun
AU  - Lyu J
AD  - Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong 
      University, Xi'an, 710061, Shaanxi, China.
FAU - Li, Zhiying
AU  - Li Z
AD  - Department of Geratology, The First Affiliated Hospital of Xi'an Jiaotong 
      University, Xi'an, 710061, Shaanxi, China.
FAU - Wei, Huiyi
AU  - Wei H
AD  - The Second Affiliated Middle School of Xi'an Jiaotong University, Xi'an, 710061, 
      Shaanxi, China.
FAU - Liu, Dandan
AU  - Liu D
AD  - Department of Geratology, The First Affiliated Hospital of Xi'an Jiaotong 
      University, Xi'an, 710061, Shaanxi, China.
FAU - Chi, Xiaoxian
AU  - Chi X
AD  - Department of Geratology, The First Affiliated Hospital of Xi'an Jiaotong 
      University, Xi'an, 710061, Shaanxi, China.
FAU - Gong, Da-Wei
AU  - Gong DW
AD  - Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, 
      University of Maryland School of Medicine, Baltimore, 21201, USA.
FAU - Zhao, Qingbin
AU  - Zhao Q
AUID- ORCID: 0000-0002-9736-2440
AD  - Department of Geratology, The First Affiliated Hospital of Xi'an Jiaotong 
      University, Xi'an, 710061, Shaanxi, China. zhaoqingbin05@163.com.
LA  - eng
GR  - 2019KW- 079/Shaanxi Provincial Science and Technology Department/
GR  - 81970329/National Natural Science Foundation of China/
PT  - Journal Article
DEP - 20200201
PL  - Germany
TA  - Acta Diabetol
JT  - Acta diabetologica
JID - 9200299
RN  - 0 (Biomarkers)
RN  - 0 (Cholesterol, LDL)
RN  - 268B43MJ25 (Uric Acid)
SB  - IM
MH  - Acute Coronary Syndrome/blood/*diagnosis/epidemiology/*etiology
MH  - Aged
MH  - Biomarkers/blood
MH  - Blood Pressure/physiology
MH  - Body Mass Index
MH  - China/epidemiology
MH  - Cholesterol, LDL/blood
MH  - Diabetes Mellitus, Type 2/blood/*complications/diagnosis/epidemiology
MH  - Diabetic Angiopathies/blood/*diagnosis/epidemiology
MH  - Female
MH  - Humans
MH  - Incidence
MH  - Male
MH  - Middle Aged
MH  - *Models, Statistical
MH  - Predictive Value of Tests
MH  - Prevalence
MH  - Prognosis
MH  - Risk Factors
MH  - Uric Acid/blood
PMC - PMC7220880
OTO - NOTNLM
OT  - Cardiovascular disease
OT  - Northwest China
OT  - Risk predictive model
OT  - Type 2 diabetes mellitus
COIS- The authors declare that they have no competing interests.
EDAT- 2020/02/03 06:00
MHDA- 2020/08/21 06:00
PMCR- 2020/02/01
CRDT- 2020/02/03 06:00
PHST- 2019/10/27 00:00 [received]
PHST- 2020/01/14 00:00 [accepted]
PHST- 2020/02/03 06:00 [pubmed]
PHST- 2020/08/21 06:00 [medline]
PHST- 2020/02/03 06:00 [entrez]
PHST- 2020/02/01 00:00 [pmc-release]
AID - 10.1007/s00592-020-01484-x [pii]
AID - 1484 [pii]
AID - 10.1007/s00592-020-01484-x [doi]
PST - ppublish
SO  - Acta Diabetol. 2020 Jun;57(6):705-713. doi: 10.1007/s00592-020-01484-x. Epub 2020 
      Feb 1.