RESUMEN
Objective:To study the effect of related laboratory indexes such as glycosylated hemoglobin on the occurrence of complications in patients with type 2 diabetes mellitus, and to construct a nomogram model.Methods:The clinical data of 203 patients with 2 diabetes mellitus from May 2020 to April 2022 in Quzhou Hospital, Zhejiang Medical and Health Group were retrospectively analyzed. Among them, 64 patients had no diabetic complications (control group), and 139 patients had diabetic complications (complication group). The clinical data of the two groups were recorded, and the related influencing factors of complications in patients with type 2 diabetes were analyzed; receiver operating characteristic (ROC) curve was used to analyze the predicting value of significant indexes for the complications in patients with type 2 diabetes; multivariate Logistic regression analysis was used to analyze the independent risk factors of complications in patients with type 2 diabetes; R language software 4.0 "rms" package was used to construct the nomogram model for predicting the complications in patients with type 2 diabetes, the calibration curve was internally validated, and the decision curve was used to evaluate the predictive efficacy of the nomogram model.Results:The hypertension rate, hyperlipemia rate, course of disease, fasting blood glucose, postprandial 2 h blood glucose and glycosylated hemoglobin in complication group were significantly higher in those in control group: 44.60% (62/139) vs. 20.31% (13/64), 48.92% (68/139) vs. 25.00% (16/64), (5.42 ± 0.68) years vs. (4.84 ± 0.51) years, (12.60 ± 2.80) mmol/L vs. (10.20 ± 1.90) mmol/L, (16.50 ± 3.10) mmol/L vs. (12.50 ± 2.90) mmol/L and (9.62 ± 1.33)% vs. (7.96 ± 0.85)%, and there were statistical differences ( P<0.01); there were no statistical differences in gender composition, age, body mass index, smoking rate, drinking rate, albumin and creatinine between the two groups ( P>0.05). ROC curve analysis result showed that the area under the curve of the course of disease, fasting blood glucose, postprandial 2 h blood glucose and glycosylated hemoglobin for predicting the complications in patients with type 2 diabetes were 0.725, 0.752, 0.830 and 0.861, respectively; the optimal cut-off values were 5 year, 11.8 mmol/L, 15.1 mmol/L and 9.23%. Multivariate Logistic regression analysis result showed that hypertension, hyperlipemia, course of disease, fasting blood glucose, postprandial 2 h blood glucose and glycosylated hemoglobin were independent risk factors of complications in patients with type 2 diabetes ( OR = 1.563, 1.692, 1.451, 1.703, 1.506 and 1.805; 95% CI 1.268 to 1.689, 1.483 to 1.824, 1.215 to 1.620, 1.402 to 1.903, 1.303 to 1.801 and 1.697 to 1.926; P<0.05). The hypertension, hyperlipemia, course of disease, fasting blood glucose, postprandial 2 h blood glucose and glycosylated hemoglobin were used as predictors to construct a nomogram model for predicting the complications in patients with type 2 diabetes. Internal validation result showed that the nomogram model predicted the complications with good concordance in patients with type 2 diabetes (C-index = 0.815, 95% CI 0.796 to 0.843); the nomogram model predicted the complications in patients with type 2 diabetes at a threshold >0.18, provided a net clinical benefit, and all had higher clinical net benefits than hypertension, hyperlipemia, course of disease, fasting blood glucose, postprandial 2 h blood glucose and glycosylated hemoglobin. Conclusions:The nomogram model constructed based on hypertension, hyperlipemia, course of disease, fasting blood glucose, postprandial 2 h blood glucose and glycosylated hemoglobin has better clinical value in predicting the complications in patients with type 2 diabetes.