Incident disability in older adults: prediction models based on two British prospective cohort studies

Development and External Validation of Models to Predict Need for Nursing Home Level of Care in Community-Dwelling Older Adults With Dementia

Importance

Most older adults living with dementia ultimately need nursing home level of care (NHLOC).

Objective

To develop models to predict need for NHLOC among older adults with probable dementia using self-report and proxy reports to aid patients and family with planning and care management.

Design, Setting, and Participants

This prognostic study included data from 1998 to 2016 from the Health and Retirement Study (development cohort) and from 2011 to 2019 from the National Health and Aging Trends Study (validation cohort). Participants were community-dwelling adults 65 years and older with probable dementia. Data analysis was conducted between January 2022 and October 2023.

Exposures

Candidate predictors included demographics, behavioral/health factors, functional measures, and chronic conditions.

Main Outcomes and Measures

The primary outcome was need for NHLOC defined as (1) 3 or more activities of daily living (ADL) dependencies, (2) 2 or more ADL dependencies and presence of wandering/need for supervision, or (3) needing help with eating. A Weibull survival model incorporating interval censoring and competing risk of death was used. Imputation-stable variable selection was used to develop 2 models: one using proxy responses and another using self-responses. Model performance was assessed by discrimination (integrated area under the receiver operating characteristic curve [iAUC]) and calibration (calibration plots).

Results

Of 3327 participants with probable dementia in the Health and Retirement Study, the mean (SD) age was 82.4 (7.4) years and 2301 (survey-weighted 70%) were female. At the end of follow-up, 2107 participants (63.3%) were classified as needing NHLOC. Predictors for both final models included age, baseline ADL and instrumental ADL dependencies, and driving status. The proxy model added body mass index and falls history. The self-respondent model added female sex, incontinence, and date recall. Optimism-corrected iAUC after bootstrap internal validation was 0.72 (95% CI, 0.70-0.75) in the proxy model and 0.64 (95% CI, 0.62-0.66) in the self-respondent model. On external validation in the National Health and Aging Trends Study (n = 1712), iAUC in the proxy and self-respondent models was 0.66 (95% CI, 0.61-0.70) and 0.64 (95% CI, 0.62-0.67), respectively. There was excellent calibration across the range of predicted risk.

Conclusions and Relevance

This prognostic study showed that relatively simple models using self-report or proxy responses can predict need for NHLOC in community-dwelling older adults with probable dementia with moderate discrimination and excellent calibration. These estimates may help guide discussions with patients and families in future care planning.

Prediction models for functional status in community dwelling older adults: a systematic review

Background

Disability poses a burden for older persons, and is associated with poor outcomes and high societal costs. Prediction models could potentially identify persons who are at risk for disability. An up to date review of such models is missing.

Objective

To identify models developed for the prediction of functional status in community dwelling older persons.

Methods

A systematic review was performed including studies of older persons that developed and/or validated prediction models for the outcome functional status. Medline and EMBASE were searched, and reference lists and prospective citations were screened for additional references. Risk of bias was assessed using the PROBAST-tool. The performance of models was described and summarized, and the use of predictors was collated using the bag-of-words text mining procedure.

Results

Forty-three studies were included and reported 167 evaluations of prediction models. The median c-statistic values for the multivariable development models ranged between 0.65 and 0.76 (minimum = 0.58, maximum = 0.90), and were consistently higher than the values of the validation models for which median c-statistic values ranged between 0.6 and 0.68 (minimum = 0.50, maximum = 0.81). A total of 559 predictors were used in the models. The five predictors most frequently used were gait speed (n = 47), age (n = 38), cognition (n = 27), frailty (n = 24), and gender (n = 22).

Conclusions

No model can be recommended for implementation in practice. However, frailty models appear to be the most promising, because frailty components (e.g. gait speed) and frailty indexes demonstrated good to excellent predictive performance. However, the risk of study bias was high. Substantial improvements can be made in the methodology.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03156-7.

Predicting Future Mobility Limitation in Older Adults: A Machine Learning Analysis of Health ABC Study Data

BACKGROUND

Mobility limitation in older adults is common and associated with poor health outcomes and loss of independence. Identification of at-risk individuals remains challenging because of time-consuming clinical assessments and limitations of statistical models for dynamic outcomes over time. Therefore, we aimed to develop machine learning models for predicting future mobility limitation in older adults using repeated measures data.

METHODS

We used annual assessments over 9 years of follow-up from the Health, Aging, and Body Composition study to model mobility limitation, defined as self-report of any difficulty walking a quarter mile or climbing 10 steps. We considered 46 predictors, including demographics, lifestyle, chronic conditions, and physical function. With a split sample approach, we developed mixed models (generalized linear and Binary Mixed Model forest) using (a) all 46 predictors, (b) a variable selection algorithm, and (c) the top 5 most important predictors. Age was included in all models. Performance was evaluated using area under the receiver operating curve in 2 internal validation data sets.

RESULTS

Area under the receiver operating curve ranged from 0.80 to 0.84 for the models. The most important predictors of mobility limitation were ease of getting up from a chair, gait speed, self-reported health status, body mass index, and depression.

CONCLUSIONS

Machine learning models using repeated measures had good performance for identifying older adults at risk of developing mobility limitation. Future studies should evaluate the utility and efficiency of the prediction models as a tool in clinical settings for identifying at-risk older adults who may benefit from interventions aimed to prevent or delay mobility limitation.

Potential utility of physical function measures to improve the risk prediction of functional disability in community-dwelling older Japanese adults: a prospective study

Background

While gait speed, one-leg standing balance, and handgrip strength have been shown to be independent predictors for functional disability, it is unclear whether such simple measures of physical function contribute to improved risk prediction of functional disability in older adults.

Methods

A total of 1,591 adults aged ≥ 65 years and without functional disability at baseline were followed up for up to 7.9 years. Functional disability was identified using the database of Japan’s Long-term Care Insurance System. Maximum gait speed, one-leg standing time, and handgrip strength were measured at baseline. Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95 % confidence intervals (CIs) for the association of physical function and functional disability incidence. The incremental predictive value of each physical function measure for risk prediction was quantified using the difference in overall C-statistic, category-free net reclassification improvement (NRI), and integrated discrimination improvement (IDI) index.

Results

During follow-up (median: 7.8 years), functional disability was identified in 384 participants. All of the physical function measures were inversely associated with the risk of functional disability, independent of potential confounding factors. The multivariable adjusted HRs (95 % CIs) for functional disability per one standard deviation increment of maximum gait speed, one-leg-standing time, and hand grip strength were 0.73 (0.65–0.83), 0.68 (0.59–0.79), and 0.72 (0.59–0.86), respectively. Incorporation of each of maximum gait speed, one-leg-stand time, and hand grip strength into a basic model with other risk factors significantly improved C-statistic from 0.770 (95 % CIs, 0.751–0.794) to 0.778 (0.759–0.803), 0.782 (0.760–0.805), and 0.775 (0.756–0.800), respectively (all p < 0.05). A model including all three measures had the highest C-statistic of 0.787 (0.765–0.810). The improvements in risk prediction were also confirmed by category-free NRI and IDI index.

Conclusions

Adding any of the three measures to a basic model with other known risk factors significantly improved the prediction of functional disability and addition of all three measures provided further improvement of the prediction in older Japanese adults. These data provide robust evidence to support the practical utility of incorporating these simple physical function measures into functional disability risk prediction tools.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02415-3.

The combined effect of behavioural risk factors on disability in aging adults from the Canadian Longitudinal Study on Aging (CLSA)

The objective of this study was to explore how behavioural risk factors (smoking, physical activity, and nutrition) cluster together and assess how clusters of behavioural risk factors are associated with functional disability by age and sex at the individual and population level. We used currently available baseline cross-sectional data from the Canadian Longitudinal Study on Aging (CLSA). The CLSA is a national, population-based longitudinal study established to understand and examine health of an aging population. This study included 51,338 Canadian men and women aged 45 to 85 years residing in the community in 10 Canadian provinces. Behavioural risk factors included smoking, physical activity, and nutrition. The main outcome used in the study was functional disability, which was assessed using a questionnaire adapted from the Older Americans Resources and Services Multidimensional Assessment Questionnaire. In this analyses of unique combinations of the risk factors of smoking, physical activity, and nutritional risk, the magnitude of the association of the behavioural risk factors with functional disability was dependent on which risk factors were included and differed by age and sex strata. Of the risk factors, physical activity accounted for between 70% to 90% of the total population level risk in individuals with all three risk factors, suggesting it is a key driver of the population burden of disability. Together, these results show that considering unique clusters of risk factors, as well as age and sex, is essential for tailoring public health strategies to reduce the burden of disability among aging populations.

Development of a clinical prediction model for the onset of functional decline in people aged 65-75 years: pooled analysis of four European cohort studies

BACKGROUND

Identifying those people at increased risk of early functional decline in activities of daily living (ADL) is essential for initiating preventive interventions. The aim of this study is to develop and validate a clinical prediction model for onset of functional decline in ADL in three years of follow-up in older people of 65-75 years old.

METHODS

Four population-based cohort studies were pooled for the analysis: ActiFE-ULM (Germany), ELSA (United Kingdom), InCHIANTI (Italy), LASA (Netherlands). Included participants were 65-75 years old at baseline and reported no limitations in functional ability in ADL at baseline. Functional decline was assessed with two items on basic ADL and three items on instrumental ADL. Participants who reported at least some limitations at three-year follow-up on any of the five items were classified as experiencing functional decline. Multiple logistic regression analysis was used to develop a prediction model, with subsequent bootstrapping for optimism-correction. We applied internal-external cross-validation by alternating the data from the four cohort studies to assess the discrimination and calibration across the cohorts.

RESULTS

Two thousand five hundred sixty community-dwelling people were included in the analyses (mean age 69.7 ± 3.0 years old, 47.4% female) of whom 572 (22.3%) reported functional decline at three-year follow-up. The final prediction model included 10 out of 22 predictors: age, handgrip strength, gait speed, five-repeated chair stands time (non-linear association), body mass index, cardiovascular disease, diabetes, chronic obstructive pulmonary disease, arthritis, and depressive symptoms. The optimism-corrected model showed good discrimination with a C statistic of 0.72. The calibration intercept was 0.06 and the calibration slope was 1.05. Internal-external cross-validation showed consistent performance of the model across the four cohorts.

CONCLUSIONS

Based on pooled cohort data analyses we were able to show that the onset of functional decline in ADL in three years in older people aged 65-75 years can be predicted by specific physical performance measures, age, body mass index, presence of depressive symptoms, and chronic conditions. The prediction model showed good discrimination and calibration, which remained stable across the four cohorts, supporting external validity of our findings.

Development of a prediction model for 30-day acute readmissions among older medical patients: the influence of social factors along with other patient-specific and organisational factors

Background

Readmission rate is one way to measure quality of care for older patients. Knowledge is sparse on how different social factors can contribute to predict readmission. We aimed to develop and internally validate a comprehensive model for prediction of acute 30-day readmission among older medical patients using various social factors along with demographic, organisational and health-related factors.

Methods

We performed an observational prospective study based on a group of 770 medical patients aged 65 years or older, who were consecutively screened for readmission risk factors at an acute care university hospital during the period from February to September 2012. Data on outcome and candidate predictors were obtained from clinical screening and administrative registers. We used multiple logistic regression analyses with backward selection of predictors. Measures of model performance and performed internal validation were calculated.

Results

Twenty percent of patients were readmitted within 30 days from index discharge. The final model showed that low educational level, along with male gender, contact with emergency doctor, specific diagnosis, higher Charlson Comorbidity Index score, longer hospital stay, cognitive problems, and medical treatment for thyroid disease, acid-related disorders, and glaucoma, predicted acute 30-day readmission. Area under the receiver operating characteristic curve (0.70) indicated acceptable discriminative ability of the model. Calibration slope was 0.98 and calibration intercept was 0.01. In internal validation analysis, both discrimination and calibration measures were stable.

Conclusions

We developed a model for prediction of readmission among older medical patients. The model showed that social factors in the form of educational level along with demographic, organisational and health-related factors contributed to prediction of acute 30-day readmissions among older medical patients.

Unilateral Exoskeleton Imposes Significantly Different Hemispherical Effect in Parietooccipital Region, but Not in Other Regions

In modern society, increasing people suffering from locomotor disabilities need an assistive exoskeleton to help them improve or restore ambulation. When walking is assisted by an exoskeleton, brain activities are altered as the closed-loop between brain and lower limbs is affected by the exoskeleton. Intuitively, a unilateral exoskeleton imposes differential effect on brain hemispheres (i.e., hemispherical effect) according to contralateral control mechanism. However, it is unclear whether hemispherical effect appears in whole hemisphere or particular region. To this end, we explored hemispherical effect on different brain regions using EEG data collected from 30 healthy participants during overground walking. The results showed that hemispherical effect was significantly different between regions when a unilateral exoskeleton was employed for walking assistance and no significance was observed for walking without the exoskeleton. Post-hoc t-test analysis revealed that hemispherical effect in the parietooccipital region significantly differed from other regions. In the parietooccipital region, a greater hemispherical effect was observed in beta band for exoskeleton-assisted walking compared to walking without exoskeleton, which was also found in the source analysis. These findings deepen the understanding of hemispherical effect of unilateral exoskeleton on brain and could aid the development of more efficient and suitable exoskeleton for walking assistance.

Can measures of physical performance in mid-life improve the clinical prediction of disability in early old age? Findings from a British birth cohort study

BACKGROUND

Poor performance in physical tests such as grip strength and walking speed is a risk factor for disability in old age, although whether such measures improve the discrimination of clinical prediction models when traditional clinical risk factors are already known is not clear. The prevalence of disability in mid-life is relatively low and hence screening in this age group may present an opportunity for early identification of those at increased future risk who may benefit most from preventative interventions.

METHODS

Data were drawn from two waves of the Medical Research Council National Survey of Health and Development. We examined whether several chronic conditions, poor health behaviours and lower scores on three measures of physical performance (grip strength, chair rise speed and standing balance time) at age 53 were associated with self-reported mobility and/or personal care disability at age 69. We used the area under the curve statistic (AUC) to assess model discrimination.

RESULTS

At age 69, 44% (826/1855) of participants reported mobility and/or personal care disability. Our final clinical prediction model included sex, knee osteoarthritis, taking 2+ medications, smoking, increased BMI and poor performance in all three physical tests, with an AUC of 0.740 compared with 0.708 for a model which did not include the performance measures.

CONCLUSION

Measures of physical performance in midlife improve discrimination in clinical prediction models for disability over 16 years. Importantly, these and similar measures are also potential targets of future diet, exercise and pharmacological intervention in mid-life.

Effectiveness of Story-Centred Care Intervention Program in older persons living in long-term care facilities: A randomized, longitudinal study

Depression is a common issue in institutionalized elderly people. The “Attentively Embracing Story” theory is applied to help individuals transform negative thoughts into positive, and reflect on spiritual healing. This study aimed to examine the effectiveness of a “Story-Centred Care Intervention Program” based on the “Attentively Embracing Story” theory in improving depressive symptoms, cognitive function, and heart rate variability in institutionalized elderly people. Seventy long-term care residents were recruited from two long-term care facilities and randomized into the story-centred care intervention (n = 35) and control groups (n = 35). We excluded five long-term care residents who did not complete the post-test measures and five long-term care residents who had interference events on the outcome measures. Finally, sixty long-term care residents (40 women and 20 men; age 84.3±5.98 years) were included in the final analysis. Data were collected at four times (pre-intervention and post-intervention, 1 and 3-month follow-up) and analyzed with the generalized estimating equation approach.Instruments, including Geriatric Depression Scale, Short Portable Mind Status Questionnaire, and a CheckMyHeart device to measure heart rate variability, were used in study. The degree of improvement in depressive symptoms was significantly higher in the story-centred care intervention group than in the control group after providing the story-centred care intervention program (p < .001) and at 1 and 3-month follow-up (p = .001, p = .006, respectively; GDS-15 score reduced 1.816 at the 3-month follow-up). Participants receiving the story-centred care intervention program showed significantly greater improvement than those in the control group in the cognitive function at 1and 3-month follow-up (p = .009, p = .024, respectively; SPMSQ score reduced 0.345 at the 3-month follow-up). The heart rate variability parameters (SDNN, RMSSD) did not show a statistically significant increase. However an increasing trend in the parameters was observed in the intervention group (SDNN increased 16.235ms at the 3-month follow-up; RMSSD increased 16.424 ms at the 3-month follow-up). In conclusions, the story-centred care intervention program was effective on the improvement of depressive symptoms and cognitive status in institutionalized elderly people.

Predicting Trajectories of Functional Decline in 60- to 70-Year-Old People

BACKGROUND

Early identification of people at risk of functional decline is essential for delivering targeted preventive interventions.

OBJECTIVE

The aim of this study is to identify and predict trajectories of functional decline over 9 years in males and females aged 60-70 years.

METHODS

We included 403 community-dwelling participants from the InCHIANTI study and 395 from the LASA study aged 60-70 years at baseline, of whom the majority reported no functional decline at baseline (median 0, interquartile range 0-1). Participants were included if they reported data on ≥2 measurements of functional ability during a 9-year follow-up. Functional ability was scored with 6 self-reported items on activities of daily living. We performed latent class growth analysis to identify trajectories of functional decline and applied multinomial regression models to develop prediction models of identified trajectories. Analyses were stratified for sex.

RESULTS

Three distinct trajectories were identified: no/little decline (219 males, 241 females), intermediate decline (114 males, 158 females), and severe decline (36 males, 30 females). Higher gait speed showed decreased risk of functional limitations in males (intermediate limitations, odds ratio [OR] 0.74, 95% CI 0.57-0.97; severe limitations, OR 0.42, 95% CI 0.26-0.66). The final model in males further included the predictors fear of falling and alcohol intake (no/little decline, area under the receiver operating curve [AUC] 0.68, 95% CI 0.62-0.73; intermediate decline, AUC 0.63, 95% CI 0.56-0.69; severe decline, AUC 0.79, 95% CI 0.71-0.87). In females, higher gait speed showed a decreased risk of intermediate limitations (OR 0.51, 95% CI 0.38-0.68) and severe limitations (OR 0.18, 95% CI 0.07-0.44). Other predictors in females were age, living alone, economic satisfaction, balance, physical activity, BMI, and cardiovascular disease (no/little decline, AUC 0.80, 95% CI 0.75-0.85; intermediate decline, AUC 0.74, 95% CI 0.69-0.79; severe decline, AUC 0.95, 95% CI 0.91-0.99).

CONCLUSION

Already in people aged 60-70 years, 3 distinct trajectories of functional decline were identified in these cohorts over a 9-year follow-up. Predictors of trajectories differed between males and females, except for gait speed. Identification of people at risk is the basis for targeting interventions.