Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis

Drug Burden Index Is a Modifiable Predictor of 30-Day Hospitalization in Community-Dwelling Older Adults With Complex Care Needs: Machine Learning Analysis of InterRAI Data

BACKGROUND

Older adults (≥65 years) account for a disproportionately high proportion of hospitalization and in-hospital mortality, some of which may be avoidable. Although machine learning (ML) models have already been built and validated for predicting hospitalization and mortality, there remains a significant need to optimize ML models further. Accurately predicting hospitalization may tremendously affect the clinical care of older adults as preventative measures can be implemented to improve clinical outcomes for the patient.

METHODS

In this retrospective cohort study, a data set of 14 198 community-dwelling older adults (≥65 years) with complex care needs from the International Resident Assessment Instrument-Home Care database was used to develop and optimize 3 ML models to predict 30-day hospitalization. The models developed and optimized were Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR). Variable importance plots were generated for all 3 models to identify key predictors of 30-day hospitalization.

RESULTS

The area under the receiver-operating characteristics curve for the RF, XGB, and LR models were 0.97, 0.90, and 0.72, respectively. Variable importance plots identified the Drug Burden Index and alcohol consumption as important, immediately potentially modifiable variables in predicting 30-day hospitalization.

CONCLUSIONS

Identifying immediately potentially modifiable risk factors such as the Drug Burden Index and alcohol consumption is of high clinical relevance. If clinicians can influence these variables, they could proactively lower the risk of 30-day hospitalization. ML holds promise to improve the clinical care of older adults. It is crucial that these models undergo extensive validation through large-scale clinical studies before being utilized in the clinical setting.

Predicting Outcomes in Frail Older Community-Dwellers in Western Australia: Results from the Community Assessment of Risk Screening and Treatment Strategies (CARTS) Programme

Understanding risk factors for frailty, functional decline and incidence of adverse healthcare outcomes amongst community-dwelling older adults is important to plan population-level health and social care services. We examined variables associated with one-year risk of institutionalisation, hospitalisation and death among patients assessed in their own home by a community-based Aged Care Assessment Team (ACAT) in Western Australia. Frailty and risk were measured using the Clinical Frailty Scale (CFS) and Risk Instrument for Screening in the Community (RISC), respectively. Predictive accuracy was measured from the area under the curve (AUC). Data from 417 patients, median 82 ± 10 years, were included. At 12-month follow-up, 22.5% (n = 94) were institutionalised, 44.6% (n = 186) were hospitalised at least once and 9.8% (n = 41) had died. Frailty was common, median CFS score 6/9 ± 1, and significantly associated with institutionalisation (p = 0.001), hospitalisation (p = 0.007) and death (p < 0.001). Impaired activities of daily living (ADL) measured on the RISC had moderate correlation with admission to long-term care (r = 0.51) and significantly predicted institutionalisation (p < 0.001) and death (p = 0.01). The RISC had the highest accuracy for institutionalisation (AUC 0.76). The CFS and RISC had fair to good accuracy for mortality (AUC of 0.69 and 0.74, respectively), but neither accurately predicted hospitalisation. Home assessment of community-dwelling older patients by an ACAT in Western Australia revealed high levels of frailty, ADL impairment and incident adverse outcomes, suggesting that anticipatory care planning is imperative for these patients.

Application of machine learning in measurement of ageing and geriatric diseases: a systematic review

BACKGROUND

As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and the application of machine learning methods in this area.

METHODS

This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. The study mainly focused on three areas, that are machine learning, the geriatric population, and diseases. Peer-reviewed articles were searched in the PubMed and Scopus databases with inclusion criteria of population above 45 years, must have used machine learning methods, and availability of full text. To assess the quality of the studies, Joanna Briggs Institute's (JBI) critical appraisal tool was used.

RESULTS

A total of 70 papers were selected from the 120 identified papers after going through title screening, abstract screening, and reference search. Limited research is available on predicting biological or brain age using deep learning and different supervised machine learning methods. Neurodegenerative disorders were found to be the most researched disease, in which Alzheimer's disease was focused the most. Among non-communicable diseases, diabetes mellitus, hypertension, cancer, kidney diseases, and cardiovascular diseases were included, and other rare diseases like oral health-related diseases and bone diseases were also explored in some papers. In terms of the application of machine learning, risk prediction was the most common approach. Half of the studies have used supervised machine learning algorithms, among which logistic regression, random forest, XG Boost were frequently used methods. These machine learning methods were applied to a variety of datasets including population-based surveys, hospital records, and digitally traced data.

CONCLUSION

The review identified a wide range of studies that employed machine learning algorithms to analyse various diseases and datasets. While the application of machine learning in geriatrics and care has been well-explored, there is still room for future development, particularly in validating models across diverse populations and utilizing personalized digital datasets for customized patient-centric care in older populations. Further, we suggest a scope of Machine Learning in generating comparable ageing indices such as successful ageing index.