Conectar
Early identification and intervention of the frailty of the elderly will help lighten the burden of social medical care and improve the quality of life of the elderly. Therefore, we used machine learning (ML) algorithm to develop models to predict frailty risk in the elderly.
A prospective cohort study.
We collected data on 6997 elderly people from Chinese Longitudinal Healthy Longevity Study wave 6–7 surveys (2011–2012, 2014). After the baseline survey in 1998 (wave 1), the project conducted follow-up surveys (wave 2–8) in 2000–2018. The osteoporotic fractures index was used to assess frailty. Four ML algorithms (random forest [RF], support vector machine, XGBoost and logistic regression [LR]) were used to develop models to identify the risk factors of frailty and predict the risk of frailty. Different ML models were used for the prediction of frailty risk in the elderly and frailty risk was trained on a cohort of 4385 elderly people with frailty (split into a training cohort [75%] and internal validation cohort [25%]). The best-performing model for each study outcome was tested in an external validation cohort of 6997 elderly people with frailty pooled from the surveys (wave 6–7). Model performance was assessed by receiver operating curve and F2-score.
Among the four ML models, the F2-score values were similar (0.91 vs. 0.91 vs. 0.88 vs. 0.90), and the area under the curve (AUC) values of RF model was the highest (0.75), followed by LR model (0.74). In the final two models, the AUC values of RF and LR model were similar (0.77 vs. 0.76) and their accuracy was identical (87.4% vs. 87.4%).
Our study developed a preliminary prediction model based on two different ML approaches to help predict frailty risk in the elderly.
The presented models from this study can be used to inform healthcare providers to predict the frailty probability among older adults and maybe help guide the development of effective frailty risk management interventions.
Detecting frailty at an early stage and implementing timely targeted interventions may help to improve the allocation of health care resources and to reduce frailty-related burden. Identifying risk factors for frailty could be beneficial to provide tailored and personalized care intervention for older adults to more accurately prevent or improve their frail conditions so as to improve their quality of life.
The study has adhered to STROBE guidelines.
No patient or public contribution.
The aim of the study was to identify and synthesize the contents and the psychometric properties of the existing instruments measuring home-based care (HBC) nurses' competencies.
A hybrid systematic narrative review was performed.
The eligible studies were reviewed to identify the competencies measured by the instruments for HBC nurses. The psychometric properties of instruments in development and psychometric testing design studies were also examined. The methodological quality of the studies was evaluated using the Medical Education Research Study Quality Instrument and COSMIN checklist accordingly.
Relevant studies were searched on CINAHL, MEDLINE (via PubMed), EMBASE, PsychINFO and Scopus from 2000 to 2022. The search was limited to full-text items in the English language.
A total of 23 studies reporting 24 instruments were included. 12 instruments were adopted or modified by the studies while the other 12 were developed and psychometrically tested by the studies. None of the instruments encompassed all of the 10 home-based nursing care competencies identified in an earlier study. The two most frequently measured competencies were the management of health conditions, and critical thinking and problem-solving skills, while the two least measured competencies were quality and safety, and technological literacy. The content and structural validity of most instruments were inadequate since the adopted instruments were not initially designed or tested among HBC nurses.
This review provides a consolidation of existing instruments that were used to assess HBC nurses' competencies. The instruments were generally not comprehensive, and the content and structural validity were limited. Nonetheless, the domains, items and approaches to instrument development could be adopted to develop and test a comprehensive competency instrument for home-based nursing care practice in the future.
This review consolidated instruments used to measure home-based care nurses' competency. The instruments were often designed for ward-based care nurses hence a comprehensive and validated home-based nursing care competency instrument is needed. Nurses, researchers and nursing leaders could consider the competency instruments identified in this review to measure nurses' competencies, while a home-based nursing care competency scale is being developed.
No patient or public contribution was required in this review.
FreshRSS 