AUTHOR=Qu Hongling , Wu Cuiyun , Ye Peiji , Lv Weibiao 

TITLE=Development of Prediction Model to Estimate the Risk of Heart Failure in Diabetes Mellitus

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.900267

DOI=10.3389/fcvm.2022.900267

ISSN=2297-055X

ABSTRACT=Background
Heart failure is one leading cause of mortality and disability in diabetes mellitus. The aim of the study is to predict the risk of heart failure incidence in diabetes mellitus patients by developing a risk prediction model.
Methods 
We constructed a regression model based on 270 inpatients with diabetes mellitus between February, 2018 and January, 2019. Binary logistic regression was applied to develop the final model incorporating the predictors selected by least absolute shrinkage and selection operator regression. The nomogram was estimated by an area under the receiver operator characteristics curve and calibration diagram and validated by bootstrap method.
Results 
Risk factors including age, coronary heart disease, high-density lipoprotein and low-density lipoprotein was incorporated in the final model as predictors. Age ≥ 61 years old, low-density lipoprotein and coronary heart disease were risk factors for diabetes mellitus with heart failure with odds ratios (OR) of 32.84 (95% CI: 6.74, 253.99), 1.33 (95% CI: 1.06, 1.72) and 3.94 (95% CI: 1.43, 13.43), respectively. High-density lipoprotein was protective factor with OR of 0.11(95% CI: 0.04, 0.28). The area under curve of the model was 0.863 (95% confidence interval, 0.812~0.913). The plot of the calibration showed that there was a good consistency between predicted probability and actual probability. The Harrell's C-indexes of the nomogram was 0.845 and the model showed satisfactory calibration in the internal validation cohort. 
Conclusion 
The prediction nomogram we developed can estimate the possibility of heart failure in diabetes mellitus patients according the predictor items.