Do changes in frailty, physical functioning, and cognitive functioning predict mortality in old age? Results from the Longitudinal Aging Study Amsterdam

Toward a prognostic model for all-cause mortality among old people with disability in long-term care in China

BACKGROUND

Current prognostic model of all-cause mortality may not be applicable for old people with disability in long-term care due to the absence of injury- and care-related predictors. We aimed to develop a prognostic model specifically tailored to this population, based on comprehensive predictors.

METHOD

We conducted a prospective study involving 41,004 participants aged ≥60 with disability in long-term care across 16 study sites in Southwest China from 2017 to 2021. Participants' demographics, clinical characteristics, disability status, and injury- and care-related information at baseline were used as candidate predictors. We employed a LASSO Cox regression model to develop the prognostic model using the training set (70 % of participants), and the predictive performance was validated in the validation set (30 % of participants). The prognostic index (PI) scores of the prognostic model were used to quantify mortality risk.

RESULTS

At the end of the 4-year follow-up, 17,797 deaths (43.4 %) were observed. The prognostic model revealed several powerful and robust predictors of mortality across the total sample and subgroups, including higher age, living with comorbidities, physical and perceptual disability, and living with pressure sores. Non-professional care was an additional predictor in older participants. The risk of death for participants in the highest quartile of PI scores was approximately four-fold higher compared to those in the lowest quartile.

CONCLUSIONS

We developed and validated a prognostic model that can be practically utilized to identify individuals and populations at risk of death among old people with disability in long-term care.

The prognostic value of the NECPAL instrument, Palliative Prognostic Index, and PROFUND index in elderly residents of nursing homes with advanced chronic condition

BACKGROUND

It is essential to assess the need for palliative care and the life prognosis of elderly nursing home residents with an advanced chronic condition, and the NECPAL ICO-CCOMS©4.0 prognostic instrument may be adequate for both purposes. The objective of this study was to examine the predictive capacity of NECPAL, the Palliative Prognosis Index, and the PROFUND index in elderly residents with advanced chronic condition with and without dementia, comparing their results at different time points.

METHODS

This prospective observational study was undertaken in eight nursing homes, following the survival of 146 residents with advanced chronic condition (46.6% with dementia) at 3, 6, 12, and 24 months. The capacity of the three instruments to predict mortality was evaluated by calculating the area under the receiver operating characteristic curve (AUC), with 95% confidence interval, for the global population and separately for residents with and without dementia.

RESULTS

The mean age of residents was 84.63 years (± 8.989 yrs); 67.8% were female. The highest predictive capacity was found for PROFUND at 3 months (95%CI: 0.526-0.756; p = 0.016), for PROFUND and NECPAL at 12 months (non-significant; AUC > 0.5), and NECPAL at 24 months (close-to-significant (AUC = 0.624; 95% CI: 0.499-0.750; p = 0.053). The highest capacity at 12 months was obtained using PROFUND in residents with dementia (AUC = 0.698; 95%CI: 0.566-0.829; p = 0.003) and NECPAL in residents without dementia (non-significant; AUC = 0.649; 95%CI: 0.432-0.867; p = 0.178). Significant differences in AUC values were observed between PROFUND at 12 (p = 0.017) and 24 (p = 0.028) months.

CONCLUSIONS

PROFUND offers the most accurate prediction of survival in elderly care home residents with advanced chronic condition overall and in those with dementia, especially over the short term, whereas NECPAL ICO-CCOMS©4.0 appears to be the most useful to predict the long-term survival of residents without dementia. These results support early evaluation of the need for palliative care in elderly care home residents with advanced chronic condition.

Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals

BACKGROUND

The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using health care usage for predicting mortality remains unexplored. The aim of this study was to investigate if temporal patterns of healthcare expenditures, can improve the predictive performance for mortality, in spousal bereaved older adults, next to other widely used sociodemographic variables.

METHODS

This is a population-based cohort study of 48,944 Danish citizens 65 years of age and older suffering bereavement within 2013-2016. Individuals were followed from date of spousal loss until death from all causes or 31st of December 2016, whichever came first. Healthcare expenditures were available on weekly basis for each person during the follow-up and used as predictors for mortality risk in Extreme Gradient Boosting models. The extent to which medical spending trajectories improved mortality predictions compared to models with sociodemographics, was assessed with respect to discrimination (AUC), overall prediction error (Brier score), calibration, and clinical benefit (decision curve analysis).

RESULTS

The AUC of age and sex for mortality the year after spousal loss was 70.8% [95% CI 68.8, 72.8]. The addition of sociodemographic variables led to an increase of AUC ranging from 0.9% to 3.1% but did not significantly reduce the overall prediction error. The AUC of the model combining the variables above plus medical spending usage was 80.8% [79.3, 82.4] also exhibiting smaller Brier score and better calibration. Overall, patterns of healthcare expenditures improved mortality predictions the most, also exhibiting the highest clinical benefit among the rest of the models.

CONCLUSION

Temporal patterns of medical spending have the potential to significantly improve our assessment on who is at high risk of dying after suffering spousal loss. The proposed methodology can assist in a more efficient risk profiling and prognosis of bereaved individuals.

Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study

Machine learning (ML) could have advantages over traditional statistical models in identifying risk factors. Using ML algorithms, our objective was to identify the most important variables associated with mortality after dementia diagnosis in the Swedish Registry for Cognitive/Dementia Disorders (SveDem). From SveDem, a longitudinal cohort of 28,023 dementia-diagnosed patients was selected for this study. Sixty variables were considered as potential predictors of mortality risk, such as age at dementia diagnosis, dementia type, sex, body mass index (BMI), mini-mental state examination (MMSE) score, time from referral to initiation of work-up, time from initiation of work-up to diagnosis, dementia medications, comorbidities, and some specific medications for chronic comorbidities (e.g., cardiovascular disease). We applied sparsity-inducing penalties for three ML algorithms and identified twenty important variables for the binary classification task in mortality risk prediction and fifteen variables to predict time to death. Area-under-ROC curve (AUC) measure was used to evaluate the classification algorithms. Then, an unsupervised clustering algorithm was applied on the set of twenty-selected variables to find two main clusters which accurately matched surviving and dead patient clusters. A support-vector-machines with an appropriate sparsity penalty provided the classification of mortality risk with accuracy = 0.7077, AUROC = 0.7375, sensitivity = 0.6436, and specificity = 0.740. Across three ML algorithms, the majority of the identified twenty variables were compatible with literature and with our previous studies on SveDem. We also found new variables which were not previously reported in literature as associated with mortality in dementia. Performance of basic dementia diagnostic work-up, time from referral to initiation of work-up, and time from initiation of work-up to diagnosis were found to be elements of the diagnostic process identified by the ML algorithms. The median follow-up time was 1053 (IQR = 516-1771) days in surviving and 1125 (IQR = 605-1770) days in dead patients. For prediction of time to death, the CoxBoost model identified 15 variables and classified them in order of importance. These highly important variables were age at diagnosis, MMSE score, sex, BMI, and Charlson Comorbidity Index with selection scores of 23%, 15%, 14%, 12% and 10%, respectively. This study demonstrates the potential of sparsity-inducing ML algorithms in improving our understanding of mortality risk factors in dementia patients and their application in clinical settings. Moreover, ML methods can be used as a complement to traditional statistical methods.

Higher frailty levels are associated with lower cognitive test scores in a multi-country study: evidence from the study on global ageing and adult health

Background

Frailty has been recognized as a growing issue in older adults, with recent evidence showing that this condition heralds several health-related problems, including cognitive decline. The objective of this work is to determine if frailty is associated with cognitive decline among older adults from different countries.

Methods

We analyzed the baseline the Study on Global Ageing and Adult Health (SAGE), that includes six countries (Ghana, South Africa, Mexico, China, Russia, and India). A cross-section analysis was used to assess how Frailty was related with the Clinical Frailty Scale decision tree, while cognitive decline was evaluated using standardized scores of tests used in SAGE.

Results

A total of 30,674 participants aged 50 years or older were included. There was an association between frailty levels and cognitive performance. For example, women had an inverse relationship between frailty levels and cognitive scores, even when comparing robust category with frailty level 2 (RRR = 0.85; p = 0.41), although the relative risks decrease significantly at level 3 (RRR = 0.66; p = 0.03). When controlling for age, the relative risks between frailty levels 4 to 7 significantly decreased as cognitive performance increased (RRR = 0.46, RRR = 0.52, RRR = 0.44, RRR = 0.32; p < 0.001).

Conclusion

Our results show an association between frailty levels measured in a novel way, and cognitive decline across different cultural settings.

Association of longitudinal repeated measurements of frailty index with mortality: Cohort study among community-dwelling older adults

BACKGROUND

Frailty indices (FIs), defined by accumulation of health deficits, have been shown to be strongly related to mortality in older adults. However, previous studies mostly relied on FI measurement at a single point of time. We aimed to investigate the association of frailty with mortality according to longitudinal repeated measurements of FI in a large population-based cohort study in Germany.

METHODS

Among 9912 men and women aged 50-75 years living in Saarland, Germany and recruited in the ESTHER study in 2000-2002, a FI based on 30 deficits was determined at baseline, 2-, 5-, 8-, and 11-year follow-up. Hazard ratios (HRs) were calculated to assess the associations of FI with all-cause mortality and cause-specific mortality during 14 years of follow-up using Cox proportional hazards models that included FI as a time-varying covariate.

FINDINGS

During the 14-year follow-up, a total of 2483 deaths were observed, of which 859 and 863 were due to cancer and cardiovascular diseases (CVD), respectively. The time-varying FI showed consistently strong associations with mortality throughout 14 years of follow-up, with HRs (95% confidence intervals) for frail (FI≥ 0·35) versus non-frail (FI≤ 0·11) participants of 4·72 (4.05-5.51), 2·55 (1·95-3·34) and 7·52 (5·69-9·94) for all-cause, cancer, and CVD mortality, respectively. Gradually decreasing associations with increasing length of follow-up would have been obtained by using baseline FI only.

INTERPRETATION

Longitudinal repeated measures of FI show strong, consistent associations with mortality, especially CVD mortality, throughout extended periods of follow-up among community-dwelling older adults.

FUNDING

The ESTHER study was funded by grants from the Baden-Württemberg state Ministry of Science, Research and Arts (Stuttgart, Germany), the Federal Ministry of Education and Research (Berlin, Germany), the Federal Ministry of Family Affairs, Senior Citizens, Women and Youth (Berlin, Germany), and the Saarland State Ministry of Health, Social Affairs, Women and the Family (Saarbrücken, Germany).