AUTHOR=Gao Shanshan , Yin Gang , Xia Qing , Wu Guihai , Zhu Jinxiu , Lu Nan , Yan Jingyi , Tan Xuerui
TITLE=Development and Validation of a Nomogram to Predict the 180-Day Readmission Risk for Chronic Heart Failure: A Multicenter Prospective Study
JOURNAL=Frontiers in Cardiovascular Medicine
VOLUME=8
YEAR=2021
URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2021.731730
DOI=10.3389/fcvm.2021.731730
ISSN=2297-055X
ABSTRACT=Background: The existing prediction models lack the generalized applicability for chronic heart failure (CHF) readmission. We aimed to develop and validate a widely applicable nomogram for the prediction of 180-day readmission to the patients.
Methods: We prospectively enrolled 2,980 consecutive patients with CHF from two hospitals. A nomogram was created to predict 180-day readmission based on the selected variables. The patients were divided into three datasets for development, internal validation, and external validation (mean age: 74.2 ± 14.1, 73.8 ± 14.2, and 71.0 ± 11.7 years, respectively; sex: 50.2, 48.8, and 55.2% male, respectively). At baseline, 102 variables were submitted to the least absolute shrinkage and selection operator (Lasso) regression algorithm for variable selection. The selected variables were processed by the multivariable Cox proportional hazards regression modeling combined with univariate analysis and stepwise regression. The model was evaluated by the concordance index (C-index) and calibration plot. Finally, the nomogram was provided to visualize the results. The improvement in the regression model was calculated by the net reclassification index (NRI) (with tenfold cross-validation and 200 bootstraps).
Results: Among the selected 2,980 patients, 1,696 (56.9%) were readmitted within 180 days, and 1,502 (50.4%) were men. A nomogram was established by the results of Lasso regression, univariate analysis, stepwise regression and multivariate Cox regression, as well as variables with clinical significance. The values of the C-index were 0.75 [95% confidence interval (CI): 0.72–0.79], 0.75 [95% CI: 0.69–0.81], and 0.73 [95% CI: 0.64–0.83] for the development, internal validation, and external validation datasets, respectively. Calibration plots were provided for both the internal and external validation sets. Five variables including history of acute heart failure, emergency department visit, age, blood urea nitrogen level, and beta blocker usage were considered in the final prediction model. When adding variables involving hospital discharge way, alcohol taken and left bundle branch block, the calculated values of NRI demonstrated no significant improvements.
Conclusions: A nomogram for the prediction of 180-day readmission of patients with CHF was developed and validated based on five variables. The proposed methodology can improve the accurate prediction of patient readmission and have the wide applications for CHF.