Predictive value of risk factors for bladder dysfunction in Chinese patients with type 2 diabetes mellitus: A case-control study.

Abstract

To analyze risk factors associated with bladder dysfunction in patients with type 2 diabetes mellitus (T2DM) and to construct a prediction model for early prediction of diabetic bladder dysfunction (DBD). We included hospitalized patients with T2DM from the endocrinology department of Shenzhen Hospital, Southern Medical University, Shenzhen, China, from January 2019 to 2022. Factors associated with DBD in bivariate analysis with a p < 0.05 were included in a multivariate logistic regression analysis. Multivariate logistic regression analysis was used to determine independent risk factors and to construct a prediction model. The prediction model was presented as the model formula. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of the above risk factors and the prediction model for DBD. The model was internally verified by Boostrap resampling 1000 times. Two hundred and eleven patients were included in this study, and they were divided into the DBD group (n = 101) and the non-DBD group (n = 110). Eight variables showed significant significance in the bivariate analysis, including age, diabetic peripheral neuropathy (DPN), glycated hemoglobin (HbA1c), urinary microalbumin (mALB), red blood cell count (RBC), white blood cell count (WBC),absolute neutrophil count (ANC), percentage of monocyte (Mono%). Furthermore, multivariate logistic regression analysis revealed that age (OR [95%CI]: 1.077 [1.042-1.112]), p < 0.001; DPN (OR [95%CI]: 2.373 [1.013-5.561]), p = 0.047; HbA1c (OR [95%CI]: 1.170 [1.029-1.330]), p = 0.017 and ANC (OR [95%CI]: 1.234 [1.059-1.438]), p = 0.007 were independent risk factors for the DBD. The prediction model formula was Logit (p) = -6.611 + 0.074 age + 0.864 DPN + 0.157 HbA 1 c + 0.078 ANC. The area under the ROC curve (AUC) for the four risk factors were 0.676, 0.582, 0.618, and 0.674, respectively. The prediction model predicted DBD with higher accuracy than the individual risk factors, AUC = 0.817 (95%CI: 0.757-0.877), and the sensitivity and specificity were 88.1% and 50.0%, respectively. The model internal validation results showed that the AUC = 0.804 (95%CI: 0.707-0.901), and the calibration curve is close to the ideal diagonal line. Age, DPN, HbA1c, and ANC were risk factors for DBD. The prediction model constructed based on the four risk factors had a good predictive value for predicting the occurrence of DBD.