Machine Learning Approaches for the Frailty Screening: A Narrative Review

The effects of the generative adversarial network and personalized virtual reality platform in improving frailty among the elderly

As society ages, improving the health of the elderly through effective training programs has become a pressing issue. Virtual reality (VR) technology, with its immersive experience, is increasingly being utilized as a vital tool in rehabilitation training for the elderly. To further enhance training outcomes and improve health conditions among the elderly, this work proposes an integrated model that combines the Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and Long Short-Term Memory (LSTM) network. The GAN generates realistic, personalized virtual environments, the VAE builds training models closely related to health data, and the LSTM network provides precise motion monitoring and feedback. They collectively improve training effectiveness and assist the elderly in enhancing their health. First, the work optimizes the GAN through alternating training of the generator and discriminator to create personalized virtual environments. Next, the VAE is trained by maximizing the marginal log-likelihood of observed and generated data, and the personalized training model is constructed. Finally, the optimized LSTM network is used to implement a motion monitoring and feedback system. Experimental evaluations reveal that the optimized GAN outperforms the non-optimized version in both image quality scores and diversity indices. The optimized VAE shows improvements in reconstruction error and personalized fitness scores, with a slight reduction in image generation time. Additionally, the training time for the VAE is reduced. After training, the elderly participants exhibit a significant increase in their daily step count and weekly exercise frequency, with p-values less than 0.01, indicating a substantial improvement in their physical activity. Assessments of psychological health show a notable decrease in anxiety and depression scores among the elderly participants.

Frailty Modeling Using Machine Learning Methodologies: A Systematic Review With Discussions on Outstanding Questions

Studying frailty is crucial for enhancing the health and quality of life among older adults, refining healthcare delivery methods, and tackling the obstacles linked to an aging demographic. Approaches to frailty modeling often utilise simple analytic techniques rather than available advanced machine learning methods, which may be sub-optimal. There is no large-scale systematic review on applications of machine learning methods on frailty modeling. In this study we explore the use of machine learning methods to predict or classify frailty in older persons in routinely collected data. We reviewed 181 research articles, and categorised analytic methods into three categories: generalised linear models, survival models, and non-linear models. These methods have a moderate agreement with existing frailty scores and predictive validity for adverse outcomes. Limited evidence suggests that non-linear methods outperform generalised linear methods. The top-three predictor/input variables are specific diagnosis or groups of diagnoses, functional performance (e.g., ADLs), and impaired cognition. Mortality, hospital admissions and prolonged hospital stay are the mainly predicted outcomes. Most studies utilise classical machine learning methods with cross-sectional data. Longitudinal data collected by wearable sensors have been used for frailty modeling. We also discuss the opportunities to use more advanced machine learning methods with high dimensional longitudinal data for more personalised and accessible frailty tools.

Advances of artificial intelligence in predicting frailty using real-world data: A scoping review

BACKGROUND

Frailty assessment is imperative for tailoring healthcare interventions for older adults, but its implementation remains challenging due to the effort and time needed. The advances of artificial intelligence (AI) and natural language processing (NLP) present a novel opportunity to harness real-world data (RWD) including electronic health records, administrative claims, and other routinely collected medical records for frailty assessments.

METHODS

We followed the PRISMA-ScR guideline and searched Embase, Web of Science, and PubMed databases for articles that predict frailty using AI through RWD from inception until October 2023. We synthesized and analyzed the selected publications according to their field of application, methodologies employed, validation processes, outcomes achieved, and their respective limitations and strengths.

RESULTS

A total of 23 publications were selected from the initial search (N=2067) and bibliography. The approaches to frailty prediction using RWD and AI were categorized into two groups based on the type of data utilized: 1) AI models using structured data and 2) NLP techniques applied to unstructured clinical notes. We found that AI models achieved moderate to high predictive performance in predicting frailty. However, to demonstrate their clinical utility, these models require further validation using external data and a comprehensive assessment of their impact on patients' health outcomes. Additionally, the application of NLP in frailty prediction is still in its early stages. Great potential exists to enhance frailty prediction by integrating structured data and clinical notes.

CONCLUSION

The combination of AI and RWD presents significant opportunities for advancing frailty assessment. To maximize the advantages of these technological advances, future research is needed to rigorously address the challenges associated with the validation of AI models and innovative data integration.

Not All Frailty Assessments Are Created Equal: Comparability of Electronic Health Data-Based Frailty Assessments in Assessing Older People in Residential Care

Objectives: To evaluate the comparability of frailty assessment tools - the electronic frailty index (eFI), retrospective electronic frailty index (reFI), and clinical frailty scale (CFS) - in older residents of care facilities. Methods: Data from 813 individuals aged 65 or older, with frailty and co-morbidities, collected between 2022 and 2023, were analysed using various statistical methods. Results: The results showed significant differences in frailty classification among the tools: 78.3% were identified as moderately to severely frail by eFI, 59.6% by reFI, and 92.1% by CFS. Statistical tests confirmed significant differences (p < .05) in their assessments, indicating variability in measurement methods. Discussion: This study advances the understanding of frailty assessment within aged-care settings, highlighting the differences in the efficacy of these assessment tools. It underscores the challenges in frailty assessments and emphasizes the need for continuous refinement of assessment methods to address the diverse facets of frailty in aged care.

Frailty in the cardiac intensive care unit: assessment and impact

Frailty, a clinical syndrome of increased vulnerability, due to diminished cognitive, physical, and physiological reserves is a growing concern in the cardiac intensive care unit (CICU). It contributes to morbidity, mortality, and complications and often exerts a bidirectional association with cardiovascular disease. Although it predominately affects older adults, frailty can also be observed in younger patients <65 years of age, with approximately 30% of those admitted in CICU are frail. Acute cardiovascular illness can also impair physical and cognitive functioning among survivors and these survivors often suffer from frailty and functional declines post-CICU discharge. Patients with frailty in the CICU often have higher comorbidity burden, and they are less likely to receive optimal therapy for their acute cardiovascular conditions. Given the significance of this geriatric syndrome, this review will focus on assessment, clinical outcomes, and interventions, in an attempt to establish appropriate assessment, management, and resource utilization in frail patients during and after CICU admission.

Assessment of the Impact of Home-Based Hospitalization on Health Outcomes: An Observational Study

INTRODUCTION

In Portugal, evidence of clinical outcomes within home-based hospitalization programs remains limited. Despite the adoption of homebased hospitalization services, it is still unclear whether these services represent an effective way to manage patients compared with inpatient hospital care. Therefore, the aim of this study was to evaluate the outcomes of home-based hospitalization compared with conventional hospitalization in a group of patients with a primary diagnosis of infectious, cardiovascular, oncological, or 'other' diseases.

METHODS

An observational retrospective study using anonymized administrative data to investigate the outcomes of home-based hospitalization (n = 209) and conventional hospitalization (n = 192) for 401 Portuguese patients admitted to CUF hospitals (Tejo, Cascais, Sintra, Descobertas, and the Unidade de Hospitalização Domiciliária CUF Lisboa). Data on demographics and clinical outcomes, including Barthel index, Braden scale, Morse scale, mortality, and length of hospital stay, were collected. The statistical analysis included comparison tests and logistic regression.

RESULTS

The study found no statistically significant differences between patients' admission and discharge for the Barthel index, Braden scale, and Morse scale scores, for both conventional and home-based hospitalizations. In addition, no statistically significant differences were found in the length of stay between conventional and home-based hospitalization, although patients diagnosed with infectious diseases had a longer stay than patients with other conditions. Although the mortality rate was higher in home-based hospitalization compared to conventional hospitalization, the mortality risk index (higher in home-based hospitalization) assessed at admission was a more important predictor of death than the type of hospitalization.

CONCLUSION

The study found that there were no significant differences in outcomes between conventional and home-based hospitalization. Home-based hospitalization was found to be a valuable aspect of patient- and family-centered care. However, it is noteworthy that patients with infectious diseases experienced longer hospital stays.

Use of Artificial Intelligence in the Identification and Management of Frailty: A Scoping Review Protocol

INTRODUCTION

Rapid population ageing and associated health issues such as frailty are a growing public health concern. While early identification and management of frailty may limit adverse health outcomes, the complex presentations of frailty pose challenges for clinicians. Artificial intelligence (AI) has emerged as a potential solution to support the early identification and management of frailty. In order to provide a comprehensive overview of current evidence regarding the development and use of AI technologies including machine learning and deep learning for the identification and management of frailty, this protocol outlines a scoping review aiming to identify and present available information in this area. Specifically, this protocol describes a review that will focus on the clinical tools and frameworks used to assess frailty, the outcomes that have been evaluated and the involvement of knowledge users in the development, implementation and evaluation of AI methods and tools for frailty care in clinical settings.

METHODS AND ANALYSIS

This scoping review protocol details a systematic search of eight major academic databases, including Medline, Embase, PsycInfo, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Ageline, Web of Science, Scopus and Institute of Electrical and Electronics Engineers (IEEE) Xplore using the framework developed by Arksey and O'Malley and enhanced by Levac et al and the Joanna Briggs Institute. The search strategy has been designed in consultation with a librarian. Two independent reviewers will screen titles and abstracts, followed by full texts, for eligibility and then chart the data using a piloted data charting form. Results will be collated and presented through a narrative summary, tables and figures.

ETHICS AND DISSEMINATION

Since this study is based on publicly available information, ethics approval is not required. Findings will be communicated with healthcare providers, caregivers, patients and research and health programme funders through peer-reviewed publications, presentations and an infographic.

REGISTRATION DETAILS

OSF Registries (https://doi.org/10.17605/OSF.IO/T54G8).

Biomarkers of aging in frailty and age-associated disorders: State of the art and future perspective

According to the Geroscience concept that organismal aging and age-associated diseases share the same basic molecular mechanisms, the identification of biomarkers of age that can efficiently classify people as biologically older (or younger) than their chronological (i.e. calendar) age is becoming of paramount importance. These people will be in fact at higher (or lower) risk for many different age-associated diseases, including cardiovascular diseases, neurodegeneration, cancer, etc. In turn, patients suffering from these diseases are biologically older than healthy age-matched individuals. Many biomarkers that correlate with age have been described so far. The aim of the present review is to discuss the usefulness of some of these biomarkers (especially soluble, circulating ones) in order to identify frail patients, possibly before the appearance of clinical symptoms, as well as patients at risk for age-associated diseases. An overview of selected biomarkers will be discussed in this regard, in particular we will focus on biomarkers related to metabolic stress response, inflammation, and cell death (in particular in neurodegeneration), all phenomena connected to inflammaging (chronic, low-grade, age-associated inflammation). In the second part of the review, next-generation markers such as extracellular vesicles and their cargos, epigenetic markers and gut microbiota composition, will be discussed. Since recent progresses in omics techniques have allowed an exponential increase in the production of laboratory data also in the field of biomarkers of age, making it difficult to extract biological meaning from the huge mass of available data, Artificial Intelligence (AI) approaches will be discussed as an increasingly important strategy for extracting knowledge from raw data and providing practitioners with actionable information to treat patients.

The use of natural language processing in detecting and predicting falls within the healthcare setting: a systematic review

Falls are a common problem associated with significant morbidity, mortality, and economic costs. Current fall prevention policies in local healthcare settings are often guided by information provided by fall risk assessment tools, incident reporting, and coding data. This review was conducted with the aim of identifying studies which utilized natural language processing (NLP) for the automated detection and prediction of falls in the healthcare setting. The databases Ovid Medline, Ovid Embase, Ovid Emcare, PubMed, CINAHL, IEEE Xplore, and Ei Compendex were searched from 2012 until April 2023. Retrospective derivation, validation, and implementation studies wherein patients experienced falls within a healthcare setting were identified for inclusion. The initial search yielded 2611 publications for title and abstract screening. Full-text screening was conducted on 105 publications, resulting in 26 unique studies that underwent qualitative analyses. Studies applied NLP towards falls risk factor identification, known falls detection, future falls prediction, and falls severity stratification with reasonable success. The NLP pipeline was reviewed in detail between studies and models utilizing rule-based, machine learning (ML), deep learning (DL), and hybrid approaches were examined. With a growing literature surrounding falls prediction in both inpatient and outpatient environments, the absence of studies examining the impact of these models on patient and system outcomes highlights the need for further implementation studies. Through an exploration of the application of NLP techniques, it may be possible to develop models with higher performance in automated falls prediction and detection.

The prognostic impacts of frailty on clinical and patient-reported outcomes in patients undergoing coronary artery or valvular surgeries/procedures: A systematic review and meta-analysis

BACKGROUND

Frailty is emerging as an important prognostic indicator for patients undergoing cardiac surgeries/procedures. We sought to evaluate the prognostic and differential impacts of frailty on patients undergoing coronary artery or valvular surgical procedures of different levels of invasiveness, and to explore the differential predictability of various frailty measurement models.

METHODS

Eight databases were searched for prospective cohort studies that have adopted validated measure(s) of frailty and reported clinical, healthcare service utilization, or patient-reported outcomes in patients undergoing coronary artery or valvular surgeries/procedures.

RESULTS

Sixty-two articles were included (N = 16,679). Frailty significantly predicted mortality (short-term [≤ 30 days]: odds ratio [OR]: 2.33, 95% confidence interval [CI]: 1.28-4.26; midterm [6 months to 1 year]: OR: 3.93, 95%CI: 2.65-5.83; long-term [>1 year]: HR: 2.23, 95%CI: 1.60-3.11), postoperative complications (ORs: 2.54-3.57), discharge to care facilities (OR: 5.52, 95%CI: 3.84-7.94), hospital readmission (OR: 2.00, 95%CI: 1.15-3.50), and reduced health-related quality of life (HRQoL; standardized mean difference: -0.74, 95%CI: -1.30 to -0.18). Subgroup analyses showed that frailty exerted a greater impact on short-term mortality in patients undergoing open-heart surgeries than those receiving transcatheter procedures. Multidimensional and physical-aspect-focused frailty measurements performed equally in predicting mortality, but multidimensional measurements were more predictive of hospital readmission than physical-aspect-focused measurements.

CONCLUSION

Frailty was predictive of postoperative mortality, complications, increased healthcare service utilization, and reduced HRQoL. The impact of frailty on short-term mortality was more prominent in patients undergoing open-heart surgeries than those receiving transcatheter procedures. Multidimensional measures of frailty enhanced prognostic risk estimation, especially for hospital readmission.

The Effectiveness of a Hybrid Exercise Program on the Physical Fitness of Frail Elderly

BACKGROUND

Frailty is a serious physical disorder affecting the elderly all over the world. However, the frail elderly have low physical fitness, which limits the effectiveness of current exercise programs. Inspired by this, we attempted to integrate Baduanjin and strength and endurance exercises into an exercise program to improve the physical fitness and alleviate frailty among the elderly. Additionally, to achieve the goals of personalized medicine, machine learning simulations were performed to predict post-intervention frailty.

METHODS

A total of 171 frail elderly individuals completed the experiment, including a Baduanjin group (BDJ), a strength and endurance training group (SE), and a combination of Baduanjin and strength and endurance training group (BDJSE), which lasted for 24 weeks. Physical fitness was evaluated by 10-meter maximum walk speed (10 m MWS), grip strength, the timed up-and-go test (TUGT), and the 6 min walk test (6 min WT). A one-way analysis of variance (ANOVA), chi-square test, and two-way repeated-measures ANOVA were carried out to analyze the experimental data. In addition, nine machine learning models were utilized to predict the frailty status after the intervention.

RESULTS

In 10 m MWS and TUGT, there was a significant interactive influence between group and time. When comparing the BDJ group and the SE group, participants in the BDJSE group demonstrated the maximum gains in 10 m MWS and TUGT after 24 weeks of intervention. The stacking model surpassed other algorithms in performance. The accuracy and precision rates were 75.5% and 77.1%, respectively.

CONCLUSION

The hybrid exercise program that combined Baduanjin with strength and endurance training proved more effective at improving fitness and reversing frailty in elderly individuals. Based on the stacking model, it is possible to predict whether an elderly person will exhibit reversed frailty following an exercise program.