Bayesian Techniques in Predicting Frailty among Community-Dwelling Older Adults in the Netherlands

Predicting frailty in older patients with chronic pain using explainable machine learning: A cross-sectional study

Frailty is common among older adults with chronic pain, and early identification is crucial in preventing adverse outcomes like falls, disability, and dementia. However, effective tools for identifying frailty in this population remain limited. This study aimed to explore frailty risk factors in older adults with chronic pain and to develop 9 machine learning models for frailty identification. The Shapley Additive Explanations (SHAP) method was used to explain the models. The Random Forest (RF) model performed best with 0.822 accuracy, 0.797 precision, and an AUC of 0.881. The variables in the RF model included: age, BMI, education level, pain duration, number of pain sites, pain level, depression, and Activity of Daily Living (ADL). Pain level, depression, and ADL were the 3 most important variables in the RF model. This model helps healthcare providers to identify frailty early, enabling timely interventions to improve patient outcomes and promote healthy aging.

Cognitive status and low sun exposure as frailty major risk factor among older people in a rural area of East Java, Indonesia: A cross-sectional study

AIM

To reveal the prevalence of frailty and factors that strongly affected the frailty condition among older adults in East Java, Indonesia.

METHOD

We conducted a cross-sectional study carried out among 400 older adults aged ≥60 years without any acute illness. Data were collected from rural area in two locations in Malang and Pasuruan, East Java, Indonesia, in 2019-2020. For data collection, we used the sociodemographic profile assessment, Fried frailty phenotype, Geriatric Depression Scale, Mini Mental State assessment, sun exposure, handgrip strength, International Physical Activity Questionnaire, walk score, and body mass index. We used logistic regression statistics for data analysis.

RESULTS

The result showed that 2.5% were robust, 83% were prefrail, and 14.5% were frail. A higher proportion of subjects were aged 60-74 years (83.3%), women (70.3%), with lower educational status (84.5%). Multivariate analysis showed that the intrinsic factors low cognitive status (odds ratio [OR], 3.052 [95% confidence interval (CI), 1.691-5.508]) and older age (OR, 3.073 [95% CI, 1.637-5.767]) were associated with frailty among the older adults in a rural area. Depression was also associated with frailty (OR, 2.458 [95% CI, 0.465-12.985]). From extrinsic factors, we also found that low sun exposure (OR, 2.931 [95% CI, 1.650-5.204]) and unemployment (OR, 1.997 [95% CI, 1.112-3.588]) were associated with frailty.

CONCLUSION

For the Indonesian elderly in this study, low cognitive status, older age, depression, low sun exposure, and unemployment were associated with frailty. Understanding the modifiable risk factors of frailty can provide a valuable reference for future prevention and intervention. Geriatr Gerontol Int 2024; 24: 170-175.

Disability transitions in Dutch community-dwelling older people aged 75 years or older

BACKGROUND

Recent world population predictions show that the world population aged >=65 years will increase from 10% in 2022 to 16% in 2050. Population aging is accompanied by an increase in people with disability. It is important to pay special attention to people with disability, as these people are at high risk of adverse outcomes. Our study aimed to investigate the transitions of disability among Dutch community-dwelling older people aged 75 years or older, using a follow-up of nine years. We used socio-demographic factors gender, age, marital status, education, and income, but also lifestyle, diseases, and life events to predict the disability transitions over time.

METHODS

We used a sample of 484 people that was randomly drawn from the municipality of Roosendaal (the Netherlands), a municipality with 78,000 inhabitants. A subset of people who completed part A of the Tilburg Frailty Indicator (TFI) at baseline and the Groningen Activity Restriction Scale (GARS) questionnaires was used with a nine-year follow-up. Paired Wilcoxon tests were used to compare the consecutive measurements. Socio-demographic factors gender, age, marital status, education, and income, but also lifestyle, diseases, and life events were included to predict the disability transitions over time. For the univariable and multivariable analysis of the measurements over time with the predictor variables, we used generalized estimation equations (GEE). A p-value <0.05 was considered significant. R version 3.4.4 was used for all analyses.

RESULTS

Of the participants, 65% were younger than 80 years, 50% were married or cohabiting, 87% reported a healthy lifestyle, and 63% had no diseases or chronic disorders. Each year, more participants changed from status not disabled to disabled than vice versa. The GEE analyses showed that lifestyle ('not healthy') and diseases or chronic disorders ('two or more') were significant in the multivariable analysis for the disability score and only diseases or chronic disorders ('two or more') was significant in the multivariable analysis for the dichotomous disability score.

CONCLUSIONS

The transition of the disability score is strongly influenced by lifestyle and diseases or disorders. This applies to a lesser extent to the dichotomous disability score. There, only diseases or disorders are an important predictor. For health care professionals our study provides starting points for interventions focused on the prevention of worsening disability and for community-dwelling older people >= 75, the most important recommendation is: live healthy!

External Validation of Models for Predicting Disability in Community-Dwelling Older People in the Netherlands: A Comparative Study

Background

Advanced statistical modeling techniques may help predict health outcomes. However, it is not the case that these modeling techniques always outperform traditional techniques such as regression techniques. In this study, external validation was carried out for five modeling strategies for the prediction of the disability of community-dwelling older people in the Netherlands.

Methods

We analyzed data from five studies consisting of community-dwelling older people in the Netherlands. For the prediction of the total disability score as measured with the Groningen Activity Restriction Scale (GARS), we used fourteen predictors as measured with the Tilburg Frailty Indicator (TFI). Both the TFI and the GARS are self-report questionnaires. For the modeling, five statistical modeling techniques were evaluated: general linear model (GLM), support vector machine (SVM), neural net (NN), recursive partitioning (RP), and random forest (RF). Each model was developed on one of the five data sets and then applied to each of the four remaining data sets. We assessed the performance of the models with calibration characteristics, the correlation coefficient, and the root of the mean squared error.

Results

The models GLM, SVM, RP, and RF showed satisfactory performance characteristics when validated on the validation data sets. All models showed poor performance characteristics for the deviating data set both for development and validation due to the deviating baseline characteristics compared to those of the other data sets.

Conclusion

The performance of four models (GLM, SVM, RP, RF) on the development data sets was satisfactory. This was also the case for the validation data sets, except when these models were developed on the deviating data set. The NN models showed a much worse performance on the validation data sets than on the development data sets.