Development and Validation of a Nomogram to Predict the 180-Day Readmission Risk for Chronic Heart Failure: A Multicenter Prospective Study

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.

Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients

“…In Martin’s study, BUN was discovered to have a significant association with 28-day mortality ( 47 ), and Jamshid’s study identified BUN as one of the factors with the highest predictive values to predict the risk of mortality from patients with severe COVID-19 ( 48 ), which also provides support for our results. BUN was also identified as a significant variable for prolonged LOS and readmission prediction, and the same results can also be found in homogeneous studies ( 49 , 50 ). The increased level of BUN is associated with kidney damage, which is supported by multiple mechanisms ( 51 ).…”

Prevalence and impact of malnutrition on readmission among hospitalized patients with heart failure in China

“…Readmission and death are the two most common adverse prognoses in patients with HF, and death is the most common outcome indicator in the current study; however, readmission has been shown in numerous studies to significantly increase the risk of death. [33][34][35][36] Therefore, we believe that the use of readmission as an outcome indicator complements previous studies by better confirming and building on findings from other HF cohort studies that malnutrition is a significant predictor of poor outcomes, readmission or death.…”

“…The findings of our study differ from those of most previous studies in that we included readmission as a primary outcome indicator, with the aim of exploring the impact of malnutrition on readmission in hospitalized patients with HF and screening for tools that could better predict readmission. Readmission and death are the two most common adverse prognoses in patients with HF, and death is the most common outcome indicator in the current study; however, readmission has been shown in numerous studies to significantly increase the risk of death 33–36 . Therefore, we believe that the use of readmission as an outcome indicator complements previous studies by better confirming and building on findings from other HF cohort studies that malnutrition is a significant predictor of poor outcomes, either readmission or death.…”

A Novel Nomogram Integrated with Systemic Inflammation Markers and Traditional Prognostic Factors for Adverse Events’ Prediction in Patients with Chronic Heart Failure in the Southwest of China

“…The continuous variables such as age (I: <60, II: 60–69, III: 70–79, IV: >79) and BMI (I:<18.5, II:18.5–24.9, III: 25–29.9, IV: >29.9) were transformed into categorical variables according to the criteria of studied documents 16 , 17 ; SBP, DBP, HR, BNP, LEVF, platelet count, neutrophil count, lymphocyte count, monocyte count, BUN, TG, TC, creatinine, HDL-C, LDL-C, Apolipoprotein-A, Apolipoprotein-B, and Lipoprotein(α) were transformed into categorical variables on basis of their corresponding best cut-off values. The properties of other parameters were unchanged.…”