Development and validation of dynamic nomogram of frailty risk for older patients hospitalized with heart failure

Abstract

Objective

This study aimed to establish and validate a dynamic online nomograph for predicting the risk of frailty in older patients hospitalized with heart failure in China.

Methods

A total of 451 older adults with heart failure hospitalized were selected between December 2021 and November 2022 at the Department of Cardiovascular Medicine in a Class A tertiary hospital in Shandong, China. The data of patients were obtained by using Barthel Index, instrumental activity of daily living scale, mini nutrition assessment-short form, Pittsburgh sleep quality index scale, Morse fall risk assessment scale and general information scale. The brain natriuretic peptide and echocardiographic indexes of patients were collected by electronic medical records. All participants were randomly divided into the training set (n = 319) and the validation set (n = 132) at the ratio of 7:3. The training set is used for model construction, and the validation set is used for internal validation. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression method to filter modeling variables, while the multivariable logistic regression was used to establish the nomogram based on the screened optimal variables. The performance of the model was evaluated by the area under the curve (AUC) of the receiver operator characteristic (ROC) curve, Hosmer-Lemeshow test, calibration plot, and decision curve analysis (DCA).

Results

The prevalence of frailty in 451 patients was 50.6%, 51.4%, and 48.5% in the training and validation sets, respectively. Drinking, grip strength, New York Heart Association (NYHA) class, multimorbidity, hospitalization history of heart failure, Barthel Index, the instrumental activities of daily living, nutritional status, sleep, fall, and left atrial end-diastolic diameter were used for LASSO regression analysis as the significant predictors of frailty. According to internal validation, the AUC of the ROC curve for the nomogram was 0.920, with a sensitivity of 86.8% and specificity of 84.4%. Moreover, in the validation set, the P-values of the H-L test were 0.742, and the calibration curve had good concordance between the estimated frailty risk and actual observation, indicating the model was well-calibrated. The DCA results confirmed that the nomogram had a well-performance in clinical suitability.

Conclusions

An online dynamic nomogram predicting frailty for older patients hospitalized for heart failure in China was well-established and identified in this study. This model benefits medical professionals in identifying high-risk frailty in older hospitalized patients with heart failure, which could reduce the medical and disease burden of heart failure to a certain extent. However, further verification is needed in the future.