Predictive Mortality Index for Community-Dwelling Elderly Koreans

Evaluation of the added value of Brain Natriuretic Peptide to a validated mortality risk-prediction model in older people using a standardised international clinical assessment tool

The ability to accurately predict the one-year survival of older adults is challenging for clinicians as they endeavour to provide the most appropriate care. Standardised clinical needs assessments are routine in many countries and some enable application of mortality prediction models. The added value of blood biomarkers to these models is largely unknown. We undertook a proof of concept study to assess if adding biomarkers to needs assessments is of value. Assessment of the incremental value of a blood biomarker, Brain Naturetic Peptide (BNP), to a one year mortality risk prediction model, RiskOP, previously developed from data from the international interRAI-HomeCare (interRAI-HC) needs assessment. Participants were aged ≥65 years and had completed an interRAI-HC assessment between 1 January 2013 and 21 August 2021 in Canterbury, New Zealand. Inclusion criteria was a BNP test within 90 days of the date of interRAI-HC assessment. The primary outcome was one-year mortality. Incremental value was assessed by change in Area Under the Receiver Operating Characteristic Curve (AUC) and Brier Skill, and the calibration of the final model. Of 14,713 individuals with an interRAI-HC assessment 1,537 had a BNP within 90 days preceding the assessment and all data necessary for RiskOP. 553 (36.0%) died within 1-year. The mean age was 82.6 years. Adding BNP improved the overall AUC by 0.015 (95% CI:0.004 to 0.028) and improved predictability by 1.9% (0.26% to 3.4%). In those with no Congestive Heart Failure the improvements were 0.029 (0.004 to 0.057) and 4.0% (0.68% to 7.6%). Adding a biomarker to a risk model based on standardised needs assessment of older people improved prediction of 1-year mortality. BNP added value to a risk prediction model based on the interRAI-HC assessment in those patients without a diagnosis of congestive heart failure.

Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults

As global demographics change, ageing is a global phenomenon which is increasingly of interest in our modern and rapidly changing society. Thus, the application of proper prognostic indices in clinical decisions regarding mortality prediction has assumed a significant importance for personalized risk management (i.e., identifying patients who are at high or low risk of death) and to help ensure effective healthcare services to patients. Consequently, prognostic modelling expressed as all-cause mortality prediction is an important step for effective patient management. Machine learning has the potential to transform prognostic modelling. In this paper, results on the development of machine learning models for all-cause mortality prediction in a cohort of healthy older adults are reported. The models are based on features covering anthropometric variables, physical and lab examinations, questionnaires, and lifestyles, as well as wearable data collected in free-living settings, obtained for the "Healthy Ageing Initiative" study conducted on 2291 recruited participants. Several machine learning techniques including feature engineering, feature selection, data augmentation and resampling were investigated for this purpose. A detailed empirical comparison of the impact of the different techniques is presented and discussed. The achieved performances were also compared with a standard epidemiological model. This investigation showed that, for the dataset under consideration, the best results were achieved with Random UnderSampling in conjunction with Random Forest (either with or without probability calibration). However, while including probability calibration slightly reduced the average performance, it increased the model robustness, as indicated by the lower 95% confidence intervals. The analysis showed that machine learning models could provide comparable results to standard epidemiological models while being completely data-driven and disease-agnostic, thus demonstrating the opportunity for building machine learning models on health records data for research and clinical practice. However, further testing is required to significantly improve the model performance and its robustness.

Investigation of the analysis of wearable data for cancer-specific mortality prediction in older adults

Cancer is an aggressive disease which imparts a tremendous socio-economic burden on the international community. Early detection is an important aspect in improving survival rates for cancer sufferers; however, very few studies have investigated the possibility of predicting which people have the highest risk to develop this disease, even years before the traditional symptoms first occur. In this paper, a dataset from a longitudinal study which was collected among 2291 70-year olds in Sweden has been analyzed to investigate the possibility for predicting 2-7 year cancer-specific mortality. A tailored ensemble model has been developed to tackle this highly imbalanced dataset. The performance with different feature subsets has been investigated to evaluate the impact that heterogeneous data sources may have on the overall model. While a full-features model shows an Area Under the ROC Curve (AUC-ROC) of 0.882, a feature subset which only includes demographics, self-report health and lifestyle data, and wearable dataset collected in free-living environments presents similar performance (AUC-ROC: 0.857). This analysis confirms the importance of wearable technology for providing unbiased health markers and suggests its possible use in the accurate prediction of 2-7 year cancer-related mortality in older adults.

Social relationships and survival in the older adult cohort

Objective:

to verify the influence of social relations on the survival of older adults living in southern Brazil.

Method:

a cohort study (2008 and 2016/17), conducted with 1,593 individuals aged 60 years old or over, in individual interviews. The outcomes of social relations and survival were verified by Multiple Correspondence Analysis, which guided the proposal of an explanatory matrix for social relations, the analysis of survival by Kaplan-Meier, and the multivariate analysis by Cox regression to verify the association between the independent variables.

Results:

follow-up was carried out with 82.5% (n=1,314), with 46.1% being followed up in 2016/17 (n=735) and 579 deaths (36.4%). The older adults who went out of their homes daily had a 39% reduction in mortality, and going to parties kept the protective effect of 17% for survival. The lower risk of death for women is modified when the older adults live in households with two or more people, in this case women have an 89% higher risk of death than men.

Conclusion:

strengthened social relationships play a mediating role in survival. The findings made it possible to verify the importance of going out of the house as a marker of protection for survival.

The predictive relationship between factors related to fear of falling and mortality among community-dwelling older adults in Korea: analysis of the Korean longitudinal study of aging from 2006 to 2014

OBJECTIVES

This study was conducted to identify the predictive relationship between factors related to fear of falling (FOF) and mortality among community-dwelling older adults in Korea.

METHOD

Data were obtained from the Korean Longitudinal Study of Aging (KLoSA). Hierarchical Cox proportional hazards regression analyses were conducted to identify factors related to FOF and correlations of these factors with mortality.

RESULTS

During the eight-year follow-up period, 964 participants (23.5%) died. Death was more likely to occur in males (hazard ratio [HR], 2.55; 95% confidence interval [95% CI], 2.17-3.00), those 75 years old or older (HR, 2.76; 95% CI, 2.40-3.17), those without education (HR, 1.27; 95% CI, 1.05-1.52), and those living without a spouse (HR, 1.30; 95% CI, 1.11-1.51). Those afraid of falling (HR, 1.41; 95% CI, 1.17-1.70), limiting their activities due to FOF (HR, 1.40; 95% CI, 1.21-1.62), showing symptoms of depression (HR, 1.34; 95% CI, 1.16-1.54), and having low life satisfaction (HR, 1.34; 95% CI, 1.13-1.59) were also more likely to experience decreased lifespans.

CONCLUSION

These results suggest that early management and prevention of factors related to FOF should be an effective approach to reducing mortality in older adults.