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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 this study is to evaluate the relative merits of various heart failure models of care with regard to a variety of outcomes.
Systematic review.
Five databases including PubMed, Web of Science, Medline, Embase and Science Direct were searched from the inception date of databases to August 20, 2022.
This review used the Cochrane Collaboration's ‘Risk of Bias’ tool to assess quality. Only randomised controlled trails were included in this review that assessed all care models in the management of adults with heart failure. A categorical summary of the pattern of the papers was found, followed by extraction of outcome indicators.
Twenty articles (19 studies) were included. Seven examined nurse-led care, two examined multidisciplinary specialist care, nine (10 articles) examined patient self-management, and one examined nurse and physiotherapist co-led care. Regarding outcomes, this review examined how well the four models performed with regard to quality of life, health services use, HF self-care, and anxiety and depression for heart failure patients. The model of patient self-management showed more beneficial results than nurse-led care, multidisciplinary specialist care, and nurse and physiotherapist co-led care in reducing hospital days, improving symptoms, promoting self-care behaviours of HF patients, enhancing the quality of life, and strengthening self-care ability.
This systematic review synthesises the different care models and their relative effectiveness. Four different models of care were summarised. Of these models, the self-management model demonstrated better outcomes.
The self-management model is more effective in increasing self-management behaviours and self-management abilities, lowering the risk of hospitalisation and death, improving quality of life, and relieving anxiety and depression than other models.
There was no funding to remunerate a patient/member of the public for this review.
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