1
|
Kusumastuti S, Hoogendijk EO, Gerds TA, Lund R, Mortensen EL, Huisman M, Westendorp RGJ. Do changes in frailty, physical functioning, and cognitive functioning predict mortality in old age? Results from the Longitudinal Aging Study Amsterdam. BMC Geriatr 2022; 22:193. [PMID: 35279092 PMCID: PMC8917670 DOI: 10.1186/s12877-022-02876-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 02/25/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
The ability to accurately predict survival in older adults is crucial as it guides clinical decision making. The added value of using various health indicators as well as changes in these indicators for predicting mortality remains unclear. The aim of this study was to investigate whether changes in health indicators such as frailty and physical performance improve mortality predictions in old age.
Methods
This is a population based prospective cohort study on 995 community-dwelling people aged 68–92 years from the Longitudinal Aging Study Amsterdam. Two measurements at a three-year interval (1995/1996 and 1998/1999) were available for the frailty index, frailty phenotype, grip strength, walking speed, and Mini-Mental State Examination (MMSE). Cox regression was used to analyze mortality risks associated with the current health status and changes in health, with mortality data up to 2017. The extent to which these health indicators improved mortality predictions compared to models with age and sex only was assessed by the area under the receiver operating characteristic curve (AUC).
Results
The AUC of age and sex for five-year mortality was 72.8% (95% CI 69.0 – 76.5) and was the lowest in the oldest old (age > 80.5 years). The added AUC of the current status of health indicators ranged from 0.7 to 3.3%. The added AUC of the three-year change was lower, ranging from -0.0 to 1.1%, whereas the added AUC of three-year change and current status combined was similar to current status alone, ranging from 0.6 to 3.2%. Across age, the added AUC of current status was highest in the oldest old, however there was no such pattern using three-year change. Overall, the frailty index appeared to improve mortality predictions the most, followed by the frailty phenotype, MMSE, grip strength, and walking speed.
Conclusions
Current health status improved mortality predictions better than changes in health. Its contribution was highest in the oldest old, but the added value to models with age and sex only was limited.
Collapse
|
2
|
The hanging chin sign as a mortality predictor in geriatric patients at the emergency department: a retrospective cohort study. BMC Geriatr 2022; 22:95. [PMID: 35114953 PMCID: PMC8815262 DOI: 10.1186/s12877-022-02780-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 01/19/2022] [Indexed: 11/30/2022] Open
Abstract
Background At the emergency department, there is a need for an instrument which is quick and easy to use to identify geriatric patients with the highest risk of mortality. The so- called ‘hanging chin sign’, meaning that the mandibula projects over one or more ribs on the chest X-ray, could be such an instrument. This study aims to investigate if the hanging chin sign is a predictor of mortality in geriatric patients admitted through the emergency department. Methods We performed an observational retrospective cohort study in a Dutch teaching hospital. Patients of ≥65 years who were admitted to the geriatric ward following an emergency department visit were included. The primary outcome of this study was mortality. Secondary outcomes included the length of admission, discharge destination and the reliability compared to patient-related variables and the APOP screener. Results Three hundred ninety-six patients were included in the analysis. Mean follow up was 300 days; 207 patients (52%) died during follow up. The hanging chin sign was present in 85 patients (21%). Patients with the hanging chin sign have a significantly higher mortality risk during admission (OR 2.94 (1.61 to 5.39), p < 0.001), within 30 days (OR 2.49 (1.44 to 4.31), p = 0.001), within 90 days (OR 2.16 (1.31 to 3.56), p = 0.002) and within end of follow up (OR 2.87 (1.70 to 4.84),p < 0.001). A chest X-ray without a PA view or lateral view was also associated with mortality. This technical detail of the chest x-ray and the hanging chin sign both showed a stronger association with mortality than patient-related variables or the APOP screener. Conclusions The hanging chin sign and other details of the chest x-ray were strong predictors of mortality in geriatric patients presenting at the emergency department and admitted to the geriatric ward. Compared to other known predictors, they seem to do even better in predicting mortality. Supplementary Information The online version contains supplementary material available at 10.1186/s12877-022-02780-7.
Collapse
|
3
|
Hall A, Boulton E, Kunonga P, Spiers G, Beyer F, Bower P, Craig D, Todd C, Hanratty B. Identifying older adults with frailty approaching end-of-life: A systematic review. Palliat Med 2021; 35:1832-1843. [PMID: 34519246 PMCID: PMC8637378 DOI: 10.1177/02692163211045917] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
BACKGROUND People with frailty may have specific needs for end-of-life care, but there is no consensus on how to identify these people in a timely way, or whether they will benefit from intervention. AIM To synthesise evidence on identification of older people with frailty approaching end-of-life, and whether associated intervention improves outcomes. DESIGN Systematic review (PROSPERO: CRD42020462624). DATA SOURCES Six databases were searched, with no date restrictions, for articles reporting prognostic or intervention studies. Key inclusion criteria were adults aged 65 and over, identified as frail via an established measure. End-of-life was defined as the final 12 months. Key exclusion criteria were proxy definitions of frailty, or studies involving people with cancer, even if also frail. RESULTS Three articles met the inclusion criteria. Strongest evidence came from one study in English primary care, which showed distinct trajectories in electronic Frailty Index scores in the last 12 months of life, associated with increased risk of death. We found no studies evaluating established clinical tools (e.g. Gold Standards Framework) with existing frail populations. We found no intervention studies; the literature on advance care planning with people with frailty has relied on proxy definitions of frailty. CONCLUSION Clear implications for policy and practice are hindered by the lack of studies using an established approach to assessing frailty. Future end-of-life research needs to use explicit approaches to the measurement and reporting of frailty, and address the evidence gap on interventions. A focus on models of care that incorporate a palliative approach is essential.
Collapse
Affiliation(s)
- Alex Hall
- National Institute for Health Research (NIHR) Older People and Frailty Policy Research Unit, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Elisabeth Boulton
- National Institute for Health Research (NIHR) Older People and Frailty Policy Research Unit, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Patience Kunonga
- National Institute for Health Research (NIHR) Older People and Frailty Policy Research Unit, Population Health Sciences Institute, Newcastle University, Newcastle-upon-Tyne, UK
| | - Gemma Spiers
- National Institute for Health Research (NIHR) Older People and Frailty Policy Research Unit, Population Health Sciences Institute, Newcastle University, Newcastle-upon-Tyne, UK
| | - Fiona Beyer
- National Institute for Health Research (NIHR) Older People and Frailty Policy Research Unit, Population Health Sciences Institute, Newcastle University, Newcastle-upon-Tyne, UK
| | - Peter Bower
- National Institute for Health Research (NIHR) Older People and Frailty Policy Research Unit, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Dawn Craig
- National Institute for Health Research (NIHR) Older People and Frailty Policy Research Unit, Population Health Sciences Institute, Newcastle University, Newcastle-upon-Tyne, UK
| | - Chris Todd
- National Institute for Health Research (NIHR) Older People and Frailty Policy Research Unit, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Barbara Hanratty
- National Institute for Health Research (NIHR) Older People and Frailty Policy Research Unit, Population Health Sciences Institute, Newcastle University, Newcastle-upon-Tyne, UK
| |
Collapse
|
4
|
Steinmeyer Z, Piau A, Thomazeau J, Kai SHY, Nourhashemi F. Mortality in hospitalised older patients: the WHALES short-term predictive score. BMJ Support Palliat Care 2021:bmjspcare-2021-003258. [PMID: 34824134 DOI: 10.1136/bmjspcare-2021-003258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 09/11/2021] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To develop and validate the WHALES screening tool predicting short-term mortality (3 months) in older patients hospitalised in an acute geriatric unit. METHODS Older patients transferred to an acute geriatric ward from June 2017 to December 2018 were included. The cohort was divided into two groups: derivation (n=664) and validation (n=332) cohorts. Cause for admission in emergency room, hospitalisation history within the previous year, ongoing medical conditions, cognitive impairment, frailty status, living conditions, presence of proteinuria on a urine strip or urine albumin-to-creatinine ratio and abnormalities on an ECG were collected at baseline. Multiple logistic regressions were performed to identify independent variables associated with mortality at 3 months in the derivation cohort. The prediction score was then validated in the validation cohort. RESULTS Five independent variables available from medical history and clinical data were strongly predictive of short-term mortality in older adults including age, sex, living in a nursing home, unintentional weight loss and self-reported exhaustion. The screening tool was discriminative (C-statistic=0.74 (95% CI: 0.67 to 0.82)) and had a good fit (Hosmer-Lemeshow goodness-of-fit test (X2 (3)=0.55, p=0.908)). The area under the curve value for the final model was 0.74 (95% CI: 0.67 to 0.82). CONCLUSIONS AND IMPLICATIONS The WHALES screening tool is a short and rapid tool predicting 3-month mortality among hospitalised older patients. Early identification of end of life may help appropriate timing and implementation of palliative care.
Collapse
Affiliation(s)
- Zara Steinmeyer
- Geriatrics, CHU, Toulouse, France
- UMR 1295, Paul Sabatier University Toulouse III, INSERM, Toulouse, France
| | - Antoine Piau
- Geriatrics, CHU, Toulouse, France
- UMR 1295, Paul Sabatier University Toulouse III, INSERM, Toulouse, France
| | | | - Samantha Huo Yung Kai
- UMR 1295, Paul Sabatier University Toulouse III, INSERM, Toulouse, France
- Methodological Research Support Unit, CHU Toulouse, Toulouse, France
| | - Fati Nourhashemi
- Geriatrics, CHU, Toulouse, France
- UMR 1295, Paul Sabatier University Toulouse III, INSERM, Toulouse, France
| |
Collapse
|
5
|
Kusumastuti S, Rozing MP, Lund R, Mortensen EL, Westendorp RGJ. The added value of health indicators to mortality predictions in old age: A systematic review. Eur J Intern Med 2018; 57:7-18. [PMID: 30017559 DOI: 10.1016/j.ejim.2018.06.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 06/18/2018] [Accepted: 06/21/2018] [Indexed: 12/23/2022]
Abstract
BACKGROUND Numerous risk prediction models use indicators of health to predict mortality in old age. The added value to mortality predictions based on demographic variables is unknown. OBJECTIVE To evaluate the accuracy of health indicators in predicting all-cause mortality among individuals aged 50+ using area under receiver operating characteristic curve (AUC). Specifically, to assess the added value of health indicators relative to demographic variables. METHODS We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. There were no restrictions on study designs, follow-up duration, language, or publication dates. We also examined the quality of studies using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies. RESULTS Out of 804 studies investigating all-cause mortality in older persons, 16 studies were eligible. In community-dwelling populations, the accuracy of demographic variables and health indicators combined ranged from AUC 0.71 to 0.82, indicating modest ability to predict mortality. Age contributed the most to mortality prediction (AUC 0.65 to 0.78) and compared to age and sex, the added values of genetics, physiology, functioning, mood, cognition, nutritional status, subjective health, disease, frailty, and lifestyle ranged from AUC 0.01 to 0.10. The lack of validation samples made it difficult to assess their true added value. Findings were similar in institutionalized populations. Heterogeneity of the studies prevented us from performing a meta-analysis. CONCLUSION Age and sex contributed the most to mortality predictions in old age while the added value of health indicators is likely to be limited.
Collapse
Affiliation(s)
- Sasmita Kusumastuti
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark; Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark.
| | - Maarten Pieter Rozing
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark; Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark
| | - Rikke Lund
- Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark; Section of Social Medicine, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Erik Lykke Mortensen
- Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark; Section of Environmental Health, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Rudi G J Westendorp
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark; Center for Healthy Aging, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|