Unknown

Dataset Information

0

An early predictive model of frailty for older inpatients according to nutritional risk: protocol for a cohort study in China.


ABSTRACT:

Background

Previous reports suggest that the attributes of frailty are multidimensional and include nutrition, cognition, mentality, and other aspects. We aim to develop an early warning model of frailty based on nutritional risk screening and apply the frailty early warning model in the clinic to screen high-risk patients and provide corresponding intervention target information.

Methods

The proposed study includes two stages. In the first stage, we aim to develop a prediction model of frailty among older inpatients with nutritional risk. Study data were collected from a population-based aging cohort study in China. A prospective cohort study design will be used in the second stage of the study. We will recruit 266 older inpatients (age 65 years or older) with nutritional risk, and we will apply the frailty model in the clinic to explore the predictive ability of the model in participants, assess patients' health outcomes with implementation of the frailty model, and compare the model with existing frailty assessment tools. Patients' health outcomes will be measured at admission and at 30-day follow-up.

Discussion

This project is the first to develop an early prediction model of frailty for older inpatients according to nutritional risk in a nationally representative sample of Chinese older inpatients of tertiary hospitals. The results will hopefully help to promote the development of more detailed frailty assessment tools according to nutritional risk, which may ultimately lead to reduced health care costs and improvement in independence and quality of life among geriatric patients.

Trial registration

Chinese Clinical Trial Registry, ChiCTR1800017682 , registered August 9, 2018; and ChiCTR2100044148 , registered March 11, 2021.

SUBMITTER: Liu H 

PROVIDER: S-EPMC8371757 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC6314758 | biostudies-literature
| S-EPMC10074647 | biostudies-literature
| S-EPMC9307996 | biostudies-literature
| S-EPMC8379589 | biostudies-literature
| S-EPMC5433026 | biostudies-literature
| S-EPMC10149728 | biostudies-literature
| S-EPMC7368949 | biostudies-literature
| S-EPMC4621238 | biostudies-literature
| S-EPMC10131187 | biostudies-literature
| S-EPMC6559705 | biostudies-literature