Effect of Angiotensin System Inhibitors on Physical Performance in Older People - A Systematic Review and Meta-Analysis

Objective: Preclinical and observational data suggest that angiotensin converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARBs) may be able to improve physical performance in older people via direct and indirect effects on skeletal muscle. We aimed to summarize current evidence from randomised controlled trials in this area. Design: Systematic review and meta-analysis. Setting and Participants: Randomized controlled trials enrolling older people, comparing ACEi or ARB to placebo, usual care or another antihypertensive agent, with outcome data on measures of physical performance. Methods: We searched multiple electronic databases without language restriction between inception and the end of February 2020. Trials were excluded if the mean age of participants was <65 years or treatment was targeting specific diseases known to affect muscle function (for example heart failure). Data were sought on measures of endurance and strength. Standardized mean difference (SMD) treatment effects were calculated using random-effects models with RevMan software. Results: Eight trials (952 participants) were included. Six trials tested ACEi, 2 trials tested ARBs. The mean age of participants ranged from 66 to 79 years, and the duration of treatment ranged from 2 months to 1 year. Trials recruited healthy older people and people with functional impairment; no trials specifically targeted older people with sarcopenia. Risk of bias for all trials was low to moderate. No significant effect was seen on endurance outcomes [6 trials, SMD 0.04 (95% CI –0.22 to 0.29); P =.77; I2 = 53%], strength outcomes [6 trials, SMD –0.02 (95% CI –0.18 to 0.14), P =.83, I2 = 21%] or the short physical performance battery [3 trials, SMD –0.04 (95% CI –0.19 to 0.11), P =.60, I2 = 0%]. No evidence of publication bias was evident on inspection of funnel plots. Conclusions and Implications: Existing evidence does not support the use of ACE inhibitors or angiotensin receptor blockers as a single intervention to improve physical performance in older people

New horizons in the role of digital data in the healthcare of older people

There are national and global moves to improve effective digital data design and application in healthcare. This New Horizons commentary describes the role of digital data in healthcare of the ageing population. We outline how health and social care professionals can engage in the proactive design of digital systems that appropriately serve people as they age, carers and the workforce that supports them.// Key Points: //Healthcare improvements have resulted in increased population longevity and hence multimorbidity.// //Shared care records to improve communication and information continuity across care settings hold potential for older people.// Data structure and coding are key considerations.// A workforce with expertise in caring for older people with relevant knowledge and skills in digital healthcare is important./

Predicting discharge to institutional long-term care after stroke: a systematic review &amp; meta-analysis

Background/Objectives: Stroke is a leading cause of disability worldwide, and a significant proportion of stroke survivors require long-term institutional care. Understanding who cannot be discharged home is important for health and social care planning. Our aim was to establish predictive factors for discharge to institutional care after hospitalization for stroke. Design: We registered and conducted a systematic review and meta-analysis (PROSPERO: CRD42015023497) of observational studies. We searched MEDLINE, EMBASE, and CINAHL Plus to February 2017. Quantitative synthesis was performed where data allowed. Setting: Acute and rehabilitation hospitals. Participants: Adults hospitalized for stroke who were newly admitted directly to long-term institutional care at the time of hospital discharge. Measurements: Factors associated with new institutionalization. Results: From 10,420 records, we included 18 studies (n = 32,139 participants). The studies were heterogeneous and conducted in Europe, North America, and East Asia. Eight studies were at high risk of selection bias. The proportion of those surviving to discharge who were newly discharged to long-term care varied from 7% to 39% (median 17%, interquartile range 12%), and the model of care received in the long-term care setting was not defined. Older age and greater stroke severity had a consistently positive association with the need for long-term care admission. Individuals who had a severe stroke were 26 times as likely to be admitted to long-term care than those who had a minor stroke. Individuals aged 65 and older had a risk of stroke that was three times as great as that of younger individuals. Potentially modifiable factors were rarely examined. Conclusion: Age and stroke severity are important predictors of institutional long-term care admission directly from the hospital after an acute stroke. Potentially modifiable factors should be the target of future research. Stroke outcome studies should report discharge destination, defining the model of care provided in the long-term care setting

New horizons in the role of digital data in the healthcare of older people

There are national and global moves to improve effective digital data design and application in healthcare. This New Horizons commentary describes the role of digital data in healthcare of the ageing population. We outline how health and social care professionals can engage in the proactive design of digital systems that appropriately serve people as they age, carers and the workforce that supports them

New Horizons in the use of routine data for ageing research

The past three decades have seen a steady increase in the availability of routinely collected health and social care data and the processing power to analyse it. These developments represent a major opportunity for ageing research, especially with the integration of different datasets across traditional boundaries of health and social care, for prognostic research and novel evaluations of interventions with representative populations of older people. However, there are considerable challenges in using routine data at the level of coding, data analysis and in the application of findings to everyday care. New Horizons in applying routine data to investigate novel questions in ageing research require a collaborative approach between clinicians, data scientists, biostatisticians, epidemiologists and trial methodologists. This requires building capacity for the next generation of research leaders in this important area. There is a need to develop consensus code lists and standardised, validated algorithms for common conditions and outcomes that are relevant for older people to maximise the potential of routine data research in this group. Lastly, we must help drive the application of routine data to improve the care of older people, through the development of novel methods for evaluation of interventions using routine data infrastructure. We believe that harnessing routine data can help address knowledge gaps for older people living with multiple conditions and frailty, and design interventions and pathways of care to address the complex health issues we face in caring for older people

Predicting discharge to institutional long-term care following acute hospitalisation: a systematic review and meta-analysis

Background: moving into long-term institutional care is a significant life event for any individual. Predictors of institutional care admission from community-dwellers and people with dementia have been described, but those from the acute hospital setting have not been systematically reviewed. Our aim was to establish predictive factors for discharge to institutional care following acute hospitalisation. Methods: we registered and conducted a systematic review (PROSPERO: CRD42015023497). We searched MEDLINE; EMBASE and CINAHL Plus in September 2015. We included observational studies of patients admitted directly to long-term institutional care following acute hospitalisation where factors associated with institutionalisation were reported. Results: from 9,176 records, we included 23 studies (n = 354,985 participants). Studies were heterogeneous, with the proportions discharged to a care home 3–77% (median 15%). Eleven studies (n = 12,642), of moderate to low quality, were included in the quantitative synthesis. The need for institutional long-term care was associated with age (pooled odds ratio (OR) 1.02, 95% confidence intervals (CI): 1.00–1.04), female sex (pooled OR 1.41, 95% CI: 1.03–1.92), dementia (pooled OR 2.14, 95% CI: 1.24–3.70) and functional dependency (pooled OR 2.06, 95% CI: 1.58–2.69). Conclusions: discharge to long-term institutional care following acute hospitalisation is common, but current data do not allow prediction of who will make this transition. Potentially important predictors evaluated in community cohorts have not been examined in hospitalised cohorts. Understanding these predictors could help identify individuals at risk early in their admission, and support them in this transition or potentially intervene to reduce their risk

New Horizons in the role of digital data in the healthcare of older people

There are national and global moves to improve effective digital data design and application in healthcare. This New Horizons commentary describes the role of digital data in healthcare of the ageing population. We outline how health and social care professionals can engage in the proactive design of digital systems that appropriately serve people as they age, carers and the workforce that supports them.JM is funded by the National Institute for Health and Care Research (NIHR), (NIHR302270 Advanced Fellowship). OT is funded as an academic clinical lecturer funded by the National Institute for Health Research (NIHR). JKB is a funded co-applicant on the DACHA study which is funded by the NIHR Health and Social Care Delivery Research programme (NIHR 127234). KEW is supported by clinical research fellowships from Alzheimer Scotland and The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology, part of the cross council Lifelong Health and Wellbeing Initiative (MR/L501530/1). Funding from the Biotechnology and Biological Sciences Research Council (BBSRC) and Medical Research Council (MRC) is gratefully acknowledged. JvO received funding from National Institute for Health and Care Research (NIHR) Doctoral Research Fellowship 300901. VLK is funded by an MRC/NIHR Clinical Academic Research Partnership Grant (CARP; grant code: MR/T023902/1). EV has time funded by the National Institute for Health (NIHR) Applied Research Collaboration (ARC) Greater Manchester and the NIHR Clinical Research network Greater Manchester hosted through Manchester University hospitals NHS Foundation Trust. She also holds an Honorary Clinical Chair through the Manchester Academic Health Sciences Centre, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester. The Ageing Data Research Collaboration (@geridata) is supported by the British Geriatrics Society