Machine learning models for identifying pre-frailty in community dwelling older adults.

BACKGROUND: There is increasing evidence that pre-frailty manifests as early as middle age. Understanding the factors contributing to an early trajectory from good health to pre-frailty in middle aged and older adults is needed to inform timely preventive primary care interventions to mitigate early decline and future frailty. METHODS: A cohort of 656 independent community dwelling adults, aged 40-75 years, living in South Australia, undertook a comprehensive health assessment as part of the Inspiring Health cross-sectional observational study. Secondary analysis was completed using machine learning models to identify factors common amongst participants identified as not frail or pre-frail using the Clinical Frailty Scale (CFS) and Fried Frailty Phenotype (FFP). A correlation-based feature selection was used to identify factors associated with pre-frailty classification. Four machine learning models were used to derive the prediction models for classification of not frail and pre-frail. The class discrimination capability of the machine learning algorithms was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, F1-score and accuracy. RESULTS: Two stages of feature selection were performed. The first stage included 78 physiologic, anthropometric, environmental, social and lifestyle variables. A follow-up analysis with a narrower set of 63 variables was then conducted with physiologic factors associated with the FFP associated features removed, to uncover indirect indicators connected with pre-frailty. In addition to the expected physiologic measures, a range of anthropometric, environmental, social and lifestyle variables were found to be associated with pre-frailty outcomes for the cohort. With FFP variables removed, machine learning (ML) models found higher BMI and lower muscle mass, poorer grip strength and balance, higher levels of distress, poor quality sleep, shortness of breath and incontinence were associated with being classified as pre-frail. The machine learning models achieved an AUC score up to 0.817 and 0.722 for FFP and CFS respectively for predicting pre-frailty. With feature selection, the performance of ML models improved by up to + 7.4% for FFP and up to + 7.9% for CFS. CONCLUSIONS: The results of this study indicate that machine learning methods are well suited for predicting pre-frailty and indicate a range of factors that may be useful to include in targeted health assessments to identify pre-frailty in middle aged and older adults.

Effectiveness of cardiac rehabilitation programs on medication adherence in patients with cardiovascular disease: A systematic review and meta-analysis.

Background: Education to improve medication adherence is one of the core components of cardiac rehabilitation (CR) programs. However, the evidence on the effectiveness of CR programs on medication adherence is conflicting. Therefore, we aimed to summarize the effectiveness of CR programs versus standard care on medication adherence in patients with cardiovascular disease. Methods: A systematic review and meta-analysis was conducted. Seven databases and clinical trial registries were searched for published and unpublished articles from database inception to 09 Feb 2022. Only randomised controlled trials and quasi-experimental studies were included. Two independent reviewers conducted the screening, extraction, and appraisal. The JBI methodology for effectiveness reviews and PRISMA 2020 guidelines were followed. A statistical meta-analysis of included studies was pooled using RevMan version 5.4.1. Results: In total 33 studies were included with 16,677 participants. CR programs increased medication adherence by 14 % (RR = 1.14; 95 % CI: 1.07 to 1.22; p = 0.0002) with low degree of evidence certainty. CR also lowered the risk of dying by 17 % (RR = 0.83; 95 % CI: 0.69 to 1.00; p = 0.05); primary care and emergency department visit by mean difference of 0.19 (SMD = -0.19; 95 % CI: -0.30 to -0.08; p = 0.0008); and improved quality of life by 0.93 (SMD = 0.93; 95 % CI: 0.38 to 1.49; p = 0.0010). But no significant difference was observed in lipid profiles, except with total cholesterol (SMD = -0.26; 95 % CI: -0.44 to -0.07; p = 0.006) and blood pressure levels. Conclusions: CR improves medication adherence with a low degree of evidence certainty and non-significant changes in lipid and blood pressure levels. This result requires further investigation.

Predicting Australian Adults at High Risk of Cardiovascular Disease Mortality Using Standard Risk Factors and Machine Learning.

Effective cardiovascular disease (CVD) prevention relies on timely identification and intervention for individuals at risk. Conventional formula-based techniques have been demonstrated to over- or under-predict the risk of CVD in the Australian population. This study assessed the ability of machine learning models to predict CVD mortality risk in the Australian population and compare performance with the well-established Framingham model. Data is drawn from three Australian cohort studies: the North West Adelaide Health Study (NWAHS), the Australian Diabetes, Obesity, and Lifestyle study, and the Melbourne Collaborative Cohort Study (MCCS). Four machine learning models for predicting 15-year CVD mortality risk were developed and compared to the 2008 Framingham model. Machine learning models performed significantly better compared to the Framingham model when applied to the three Australian cohorts. Machine learning based models improved prediction by 2.7% to 5.2% across three Australian cohorts. In an aggregated cohort, machine learning models improved prediction by up to 5.1% (area-under-curve (AUC) 0.852, 95% CI 0.837-0.867). Net reclassification improvement (NRI) was up to 26% with machine learning models. Machine learning based models also showed improved performance when stratified by sex and diabetes status. Results suggest a potential for improving CVD risk prediction in the Australian population using machine learning models.