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.
Highlights
It is well-known that chronic heart failure (CHF) is a systemic clinical syndrome as well as an endpoint stage of various cardiovascular diseases with typical symptoms of systolic and/or diastolic dysfunction
The existing readmission models of heart failure (HF) have been established and widely used in different regions of China, including the standard model developed by Tan et al [8] and two models restricted to the northern cities of China [9, 10]
We found that acute heart failure (AHF), emergency, estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), and NT-proBNP were independently associated with the 180-day readmission of patients with CHF
Summary
It is well-known that chronic heart failure (CHF) is a systemic clinical syndrome as well as an endpoint stage of various cardiovascular diseases with typical symptoms of systolic and/or diastolic dysfunction. In recent years, existing treatments are effective in reversing the progression to end-stage disease among patients diagnosed with CHF; the mortality and readmission rates are still high [1] To better address these issues, many experts and clinical physicians have committed to identifying a prognostic model of CHF in its early stages [2]. Given the delay in the treatment of patients, the 180-day readmission time seems more suitable for patients in developing countries [11, 12] Based on these studies, we can conclude that readmission rates vary from 34.9 to 56.4%, and most of the state-of-the-art models are derived from singlecenter studies consisting of a large sample of patients admitted in the hospital, which usually lack general applicability in realworld clinical diagnoses. We aimed to develop and validate a widely applicable nomogram for the prediction of 180-day readmission to the patients
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