PMID- 35418766
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220421
IS  - 1178-7007 (Print)
IS  - 1178-7007 (Electronic)
IS  - 1178-7007 (Linking)
VI  - 15
DP  - 2022
TI  - Nomogram for Prediction of Diabetic Retinopathy Among Type 2 Diabetes Population 
      in Xinjiang, China.
PG  - 1077-1089
LID - 10.2147/DMSO.S354611 [doi]
AB  - PURPOSE: To establish an accurate risk prediction model of diabetic retinopathy 
      (DR) using cost effective and easily available patients' characteristics and 
      clinical biomarkers. PATIENTS AND METHODS: Totally 18,904 cases diagnosed type 2 
      diabetes mellitus (T2DM) were collected, among which 13,980 cases were selected 
      after quality screening. The least absolute shrinkage and selection operator 
      (LASSO) regression models were used for univariate analysis and factors 
      selection, and the multi-factor logistic regression analysis was used to 
      establish the prediction model. Discrimination, calibration, and clinical 
      usefulness of the prediction model were assessed using AUC/ Harrell's C 
      statistic, calibration plot, and decision curve analysis. Both the development 
      group and validation group were assessed. RESULTS: Candidate variables were 
      selected by Lasso regression and multivariate logistic regression analysis. 
      Finally, the candidate predictive variables were included diabetic peripheral 
      neuropathy (DPN), age, neutrophilic granulocyte (NE), high-density lipoprotein 
      (HDL), hemoglobin A1c (HbA1C), duration of T2DM, and glycosylated serum protein 
      (GSP) were used to establish a nomogram model for predicting the risk of DR. In 
      the development group, the area under the receiver operating characteristic curve 
      (AUC) was 0.882 (95% CI, 0.875-0.888). In the validation group, the AUC was 0.870 
      (95% CI, 0.856-0.881). Meanwhile, the optimism-corrected Harrell's C statistic 
      were 0.878 and 0.867 in the development group and the validation group, 
      respectively. Decision curve analysis demonstrated that the nomogram was 
      clinically useful. CONCLUSION: We constructed and verified nomograms that could 
      accurately predict the risk of DR in T2DM patients, which could be used to 
      predict the personalized risk of DR patients in Xinjiang, China.
CI  - (c) 2022 Li et al.
FAU - Li, Yongsheng
AU  - Li Y
AD  - College of Public Health, Xinjiang Medical University, Urumqi, 830011, People's 
      Republic of China.
FAU - Li, Cheng
AU  - Li C
AD  - Center for Data Statistics and Analysis, First Affiliated Hospital of Xinjiang 
      Medical University, Urumqi, 830054, People's Republic of China.
FAU - Zhao, Shi
AU  - Zhao S
AUID- ORCID: 0000-0001-8722-6149
AD  - JC School of Public Health and Primary Care, Chinese University of Hong Kong, 
      Hong Kong, 999077, People's Republic of China.
FAU - Yin, Yi
AU  - Yin Y
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Nanjing 
      Medical University, Nanjing, Jiangsu, 211166, People's Republic of China.
FAU - Zhang, Xueliang
AU  - Zhang X
AD  - Department of Medical Engineering and Technology, Xinjiang Medical University, 
      Urumqi, 830011, People's Republic of China.
FAU - Wang, Kai
AU  - Wang K
AD  - Department of Medical Engineering and Technology, Xinjiang Medical University, 
      Urumqi, 830011, People's Republic of China.
LA  - eng
PT  - Journal Article
DEP - 20220407
PL  - New Zealand
TA  - Diabetes Metab Syndr Obes
JT  - Diabetes, metabolic syndrome and obesity : targets and therapy
JID - 101515585
PMC - PMC8999722
OTO - NOTNLM
OT  - diabetic peripheral neuropathy
OT  - nomogram
OT  - prediction model
OT  - risk factors
COIS- The authors report no conflicts of interest in this work.
EDAT- 2022/04/15 06:00
MHDA- 2022/04/15 06:01
PMCR- 2022/04/07
CRDT- 2022/04/14 05:20
PHST- 2021/12/17 00:00 [received]
PHST- 2022/03/23 00:00 [accepted]
PHST- 2022/04/14 05:20 [entrez]
PHST- 2022/04/15 06:00 [pubmed]
PHST- 2022/04/15 06:01 [medline]
PHST- 2022/04/07 00:00 [pmc-release]
AID - 354611 [pii]
AID - 10.2147/DMSO.S354611 [doi]
PST - epublish
SO  - Diabetes Metab Syndr Obes. 2022 Apr 7;15:1077-1089. doi: 10.2147/DMSO.S354611. 
      eCollection 2022.