T1DMicro: A Clinical Risk Calculator for Type 1 Diabetes Related Microvascular Complications

Clinical scoring systems for the risk of cardiovascular autonomic neuropathy in type 1 and type 2 diabetes: a simple tool

BACKGROUND AND AIMS

This study was aimed at developing a clinical risk score for cardiovascular autonomic neuropathy (CAN) for type 1 and type 2 diabetes.

METHODS

In a retrospective cross-sectional one-centre study in an unselected population, 115 participants with type 1 diabetes (age 41.1±12.2 years), and 161 with type 2 diabetes (age 63.1±8.9 years), well-characterised for clinical variables, underwent standard cardiovascular reflex tests (CARTs). Strength of associations of confirmed CAN (based on 2 abnormal CARTs) with clinical variables was used to build a CAN risk score.

RESULTS

CAN risk score was based on resting heart rate, HbA1c, retinopathy, nephropathy, cardiovascular disease in both type 1 and type 2 diabetes, and on HDL cholesterol, systolic blood pressure, and smoking in type 1 diabetes or insulin treatment and physical activity in type 2 diabetes (range 0-10). In type 1 diabetes, CAN risk score showed an area under the ROC curve (AUC) of 0.890±0.034, and at cut-off of 4 sensitivity of 88%, specificity of 74.4%, and negative predictive value (NPV) of 95.7% for confirmed CAN. In type 2 diabetes, CAN risk score showed an AUC of 0.830±0.051 and at the cut-off of 4 sensitivity and specificity of 78.6% and 73.5%, respectively, and NPV of 97.3% for confirmed CAN.

INTERPRETATION

These newly developed CAN risk scores are accessible in clinical practice and, if confirmed in a validation study, they might identify asymptomatic individuals with diabetes at greater risk of CAN to be referred to CARTs, thus limiting the burden of a universal screening. This article is protected by copyright. All rights reserved.

Nomogram for Prediction of Diabetic Retinopathy Among Type 2 Diabetes Population in Xinjiang, China

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.