Qian Li , Yanping Chen , Dechun Qin , Shumei Li , Shiyu Zhang , Liu Fang , Jiafeng Zhu , Yingchao Wang , Yanan Mao , Lane Zhang
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引用次数: 0
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.
期刊介绍:
This journal aims to promote excellence in nursing and health care through the dissemination of the latest, evidence-based, peer-reviewed clinical information and original research, providing an international platform for exchanging knowledge, research findings and nursing practice experience. This journal covers a wide range of nursing topics such as advanced nursing practice, bio-psychosocial issues related to health, cultural perspectives, lifestyle change as a component of health promotion, chronic disease, including end-of-life care, family care giving. IJNSS publishes four issues per year in Jan/Apr/Jul/Oct. IJNSS intended readership includes practicing nurses in all spheres and at all levels who are committed to advancing practice and professional development on the basis of new knowledge and evidence; managers and senior members of the nursing; nurse educators and nursing students etc. IJNSS seeks to enrich insight into clinical need and the implications for nursing intervention and models of service delivery. Contributions are welcomed from other health professions on issues that have a direct impact on nursing practice.