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Comprehensive geriatric assessment for older adults admitted to hospital

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Background

Comprehensive geriatric assessment (CGA) is a multi‐dimensional, multi‐disciplinary diagnostic and therapeutic process conducted to determine the medical, mental, and functional problems of older people with frailty so that a co‐ordinated and integrated plan for treatment and follow‐up can be developed. This is an update of a previously published Cochrane review.

Objectives

We sought to critically appraise and summarise current evidence on the effectiveness and resource use of CGA for older adults admitted to hospital, and to use these data to estimate its cost‐effectiveness.

Search methods

We searched CENTRAL, MEDLINE, Embase, three other databases, and two trials registers on 5 October 2016; we also checked reference lists and contacted study authors.

Selection criteria

We included randomised trials that compared inpatient CGA (delivered on geriatric wards or by mobile teams) versus usual care on a general medical ward or on a ward for older people, usually admitted to hospital for acute care or for inpatient rehabilitation after an acute admission.

Data collection and analysis

We followed standard methodological procedures expected by Cochrane and Effective Practice and Organisation of Care (EPOC). We used the GRADE approach to assess the certainty of evidence for the most important outcomes. For this update, we requested individual patient data (IPD) from trialists, and we conducted a survey of trialists to obtain details of delivery of CGA. We calculated risk ratios (RRs), mean differences (MDs), or standardised mean differences (SMDs), and combined data using fixed‐effect meta‐analysis. We estimated cost‐effectiveness by comparing inpatient CGA versus hospital admission without CGA in terms of cost per quality‐adjusted life year (QALY) gained, cost per life year (LY) gained, and cost per life year living at home (LYLAH) gained.

Main results

We included 29 trials recruiting 13,766 participants across nine, mostly high‐income countries. CGA increases the likelihood that patients will be alive and in their own homes at 3 to 12 months' follow‐up (risk ratio (RR) 1.06, 95% confidence interval (CI) 1.01 to 1.10; 16 trials, 6799 participants; high‐certainty evidence), results in little or no difference in mortality at 3 to 12 months' follow‐up (RR 1.00, 95% CI 0.93 to 1.07; 21 trials, 10,023 participants; high‐certainty evidence), decreases the likelihood that patients will be admitted to a nursing home at 3 to 12 months follow‐up (RR 0.80, 95% CI 0.72 to 0.89; 14 trials, 6285 participants; high‐certainty evidence) and results in little or no difference in dependence (RR 0.97, 95% CI 0.89 to 1.04; 14 trials, 6551 participants; high‐certainty evidence). CGA may make little or no difference to cognitive function (SMD ranged from ‐0.22 to 0.35 (5 trials, 3534 participants; low‐certainty evidence)). Mean length of stay ranged from 1.63 days to 40.7 days in the intervention group, and ranged from 1.8 days to 42.8 days in the comparison group. Healthcare costs per participant in the CGA group were on average GBP 234 (95% CI GBP ‐144 to GBP 605) higher than in the usual care group (17 trials, 5303 participants; low‐certainty evidence). CGA may lead to a slight increase in QALYs of 0.012 (95% CI ‐0.024 to 0.048) at GBP 19,802 per QALY gained (3 trials; low‐certainty evidence), a slight increase in LYs of 0.037 (95% CI 0.001 to 0.073), at GBP 6305 per LY gained (4 trials; low‐certainty evidence), and a slight increase in LYLAH of 0.019 (95% CI ‐0.019 to 0.155) at GBP 12,568 per LYLAH gained (2 trials; low‐certainty evidence). The probability that CGA would be cost‐effective at a GBP 20,000 ceiling ratio for QALY, LY, and LYLAH was 0.50, 0.89, and 0.47, respectively (17 trials, 5303 participants; low‐certainty evidence).

Authors' conclusions

Older patients are more likely to be alive and in their own homes at follow‐up if they received CGA on admission to hospital. We are uncertain whether data show a difference in effect between wards and teams, as this analysis was underpowered. CGA may lead to a small increase in costs, and evidence for cost‐effectiveness is of low‐certainty due to imprecision and inconsistency among studies. Further research that reports cost estimates that are setting‐specific across different sectors of care are required.

PICOs

Population
Intervention
Comparison
Outcome

The PICO model is widely used and taught in evidence-based health care as a strategy for formulating questions and search strategies and for characterizing clinical studies or meta-analyses. PICO stands for four different potential components of a clinical question: Patient, Population or Problem; Intervention; Comparison; Outcome.

See more on using PICO in the Cochrane Handbook.

Plain language summary

Comprehensive geriatric assessment for older adults admitted to hospital

What is the aim of this review?

The aim of this Cochrane Review was to find out if organised and co‐ordinated specialist care (known as comprehensive geriatric assessment, or CGA) can improve care provided to older people admitted to hospital. Researchers at Cochrane collected and analysed all relevant studies to answer this question and included 29 trials in the review.

Key messages

Giving older people who are admitted to hospital access to specialist co‐ordinated geriatric assessment (CGA) services on admission to hospital increases the chances that they will be alive in their own homes at follow‐up.

What was studied in the review?

Older people admitted to hospital may have multiple, complex, and overlapping problems. They are more prone to rapid loss of independence during an acute illness, leading to potential admission to a nursing home. Some of this decline might be avoided if care needs are identified appropriately and if treatment is co‐ordinated and managed. Specialist co‐ordinated care (known as comprehensive geriatric assessment, or CGA) was developed to address medical, social, mental health, and physical needs with the help of a skilled multi‐disciplinary team. The aims are to maximise recovery and to return patients to previous levels of function when possible. In hospital, CGA is carried out on a geriatric ward, or on a general ward that is visited by a specialist geriatric team.

What are the main results of the review?

Review authors found 29 relevant trials from nine countries that recruited 13,766 people. These studies compared CGA with routine care for patients over 65 who were admitted to hospital. Most trials evaluated CGA that was provided on a specialised hospital ward or across several wards by a mobile team. The review shows that older people who receive CGA rather than routine medical care after admission to hospital are more likely to be living at home and are less likely to be admitted to a nursing home at up to a year after hospital admission.

We found no evidence that CGA reduces risk of death during follow‐up at up to a year after admission, and we noted that CGA appeared to make little or no difference in dependence (whether patients need help for everyday activities such as feeding and walking).

We found too much variation in cognitive function and length of hospital stay to draw a conclusion. Uncertainty regarding the cost‐effectiveness analysis suggests that further research is needed.

How up‐to‐date is this review?

Review authors searched for studies that had been published up to 5 October 2016.

Authors' conclusions

Implications for practice

Comprehensive geriatric assessment offers benefit for the increasing numbers of older people with frailty admitted to inpatient care. Most trials have evaluated CGA on a discrete ward. Although it has been suggested that having control over implementing recommendations of the multi‐disciplinary team in a ward setting is likely to increase success, the number of trials in the subgroup of trials evaluating CGA teams were insufficient to confirm a difference of effect. We conducted a survey of trialists to attempt to identify the elements of CGA that are considered important; 13 of the 29 trialists completed the survey, and 10 to 13 of these agreed that critical elements of CGA include tailored treatment plans, clinical leadership, knowledge and experience, multi‐disciplinary team meetings, and involvement of patients and carers in goal setting. We found that CGA may be slightly more costly to the health service than usual care without CGA. However, our analysis did not include the cost of home or social care. CGA may be cost‐effective, although with low certainty of evidence, and further research that reports cost estimates that are setting‐specific across different sectors of care are required.

Implications for research

Questions remain over effects of delays to specialist geriatric care, benefits of targeting CGA to older adults with frailty, effects of CGA wards versus teams, and cost‐effectiveness. Future trials should provide a clear description of the elements of the geriatric intervention, and should make individual participant data available for subsequent meta‐analysis. We recommend standardised outcome assessments for these trials. We developed the outcome measurement 'life year living at home' as an indicator of independence and well‐being. This outcome aligns with the primary outcome used in this review. Further research conducted to test the robustness of the LYLAH and use of alternative methods for valuing outcomes of interventions in older people would be beneficial.

Summary of findings

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Summary of findings for the main comparison. Comprehensive geriatric assessment (CGA) versus admission to hospital without CGA

Comprehensive geriatric assessment (CGA) versus admission to hospital without CGA

Patient or population: older adults admitted to hospital
Setting: unplanned hospital admissions in 9 largely high‐income countries
Intervention: CGA
Comparison: usual care

Outcomes

Anticipated absolute effects* (95% CI)

Relative effect
(95% CI)

No. of participants
(studies)

Certainty of the evidence
(GRADE)

Risk with usual care

Risk with CGA

Living at home (end of follow‐up 3 to 12 months)

Study population

RR 1.06
(1.01 to 1.10)

6799
(16 RTs)

⊕⊕⊕⊕
HIGH

561 per 1000

595 per 1000
(567 to 617)

Mortality (end of follow‐up 3 to 12 months)

Study population

RR 1.00
(0.93 to 1.07)

10,023
(21 RTs)

⊕⊕⊕⊕
HIGH

230 per 1000

230 per 1000
(214 to 247)

Admission to a nursing home (end of follow‐up
3 to 12 months)

Study population

RR 0.80
(0.72 to 0.89)

6285
(14 RTs)

⊕⊕⊕⊕
HIGH

186 per 1000

151 per 1000
(136 to 169)

Dependence

Study population

RR 0.97
(0.89 to 1.04)

6551
(14 RTs)

⊕⊕⊕⊕
HIGH

291 per 1000

282 per 1000
(259 to 302)

Cognitive function

Standardised mean difference ranged from ‐0.22 to 0.35.

3534
(5 RTs)

⊕⊕⊝⊝
LOWa,

Length of stay

Not estimable

Mean length of stay in the control group ranged from 1.8 days to 42.8 days.

Mean length of stay in the intervention group ranged from 1.63 days to 40.7 days.

5303
(17 RTs)

⊕⊕⊝⊝
LOWa,

Cost and cost‐effectiveness

Healthcare costs per participant in the CGA group were on average GBP 234 (95% CI GBP ‐144 to GBP 605) higher than in the usual care group (17 trials); CGA led to 0.012 (95% CI ‐0.024 to 0.048) more QALYs (3 trials), 0.037 (95% CI 0.001 to 0.073) more LYs (4 trials), and 0.019 (95% CI ‐0.019 to 0.155) more LYLAH (2 trials) per participant. Costs per QALYs gained was GBP 19,802, per LY gained was GBP 6305, and per LYLAH gained was GBP 12,568. CGA was more costly in 89% of 10,000 generated ICERs and led to QALY gains in 66% of cases, LY gains in 87% of cases, and LYLAH gains in 74% of cases. The probability that CGA would be cost‐effective at a GBP 20,000 ceiling ratio for QALY, LY, and LYLAH was 0.50, 0.89, and 0.47, respectively.

5303 (17 RTs)

⊕⊕⊝⊝

LOWa,

*The risk in the intervention group (and its 95% confidence interval) is based on assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).

CGA: comprehensive geriatric assessment: CI: confidence interval; ICER: incremental cost‐effectiveness ratio; LY: life year; LYLAH: life year living at home; OR: odds ratio; QALY: quality‐adjusted life year; RR: risk ratio; RT: randomised trial.

GRADE Working Group grades of evidence
High certainty: We are very confident that the true effect lies close to the estimate of effect.
Moderate certainty: We are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of effect but may be substantially different.
Low certainty: Our confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of effect.
Very low certainty: We have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect.

aThe evidence was downgraded due to imprecision and substantial heterogeneity

Background

The number of adults surviving into old age is on the rise in populations around the world (The Lancet 2014; WHO 2016). This changing demographic has contributed to an increase in emergency hospital admissions that is having an impact on delivery of healthcare services. In England, emergency admissions increased by 47% between 1997‐1998 and 2012‐2013 (National Audit Office 2013), and in the USA by 16.7% between 2003 and 2009 (Morganti 2013). Older adults (over age 65) now represent the largest users of hospital care (National Audit Office 2013). The concern of practitioners is that this increase in admissions, against the backdrop of reduction in hospital beds, places provision of safe sustainable health care for older adults at risk (Francis 2013; Royal College of Physicians 2012; The Lancet 2014).

Description of the condition

The combination of multi‐morbidity (Barnett 2012), age‐related frailty, and acute illness places older people at increased risk for adverse outcomes. These include longer‐term dependence, admission to a nursing home, and death (Clegg 2013). Frailty ("defined as the inability to withstand illness or insult without loss of function") is characterised by typical frailty syndromes (falls, reduced mobility, increased confusion, etc.) (Clegg 2013).

Description of the intervention

Comprehensive geriatric assessment (CGA) was developed in response to concern that problems experienced by older people who require hospital‐level care are not recognised and acted on. CGA is a multi‐dimensional diagnostic and therapeutic process that is focused on determining a frail older person's medical, functional, mental, and social capabilities and limitations with the goal of ensuring that problems are identified, quantified, and managed appropriately. CGA has the potential to improve health outcomes while reducing the costs of health care and social care (Rubenstein 1991).

How the intervention might work

Models of CGA have evolved in different healthcare settings to meet differing needs. Common to these interventions are the following key features, which are believed to account for their effectiveness.

  • Specialty expertise.

  • Multi‐dimensional assessment and identification of medical, functional, mental, social, and environmental problems.

  • Co‐ordinated multi‐disciplinary meetings.

  • Formulation of a plan of care around patient‐centred goals.

  • Delivery of the care plan, including rehabilitation.

  • Iterative review of progress and care planning.

Key components that have been reported to be associated with improved CGA outcomes include ability to implement treatment recommendations provided by the multi‐disciplinary team and to target the intervention to patients who present with frailty syndromes (Ellis 2011; Stuck 1993), as described above (Bachmann 2010).

Why it is important to do this review

Searches for the previous version of this review were completed in 2010 (Ellis 2011). Access to individual patient data (IPD) from a subgroup of trials, along with additional details of delivery of the intervention provided by trialists, has allowed us to estimate the cost‐effectiveness of delivering CGA.

Objectives

We sought to critically appraise and summarise current evidence on the effectiveness and resource use of CGA in hospital for older adults admitted to hospital, and to use these data to estimate its cost‐effectiveness.

Methods

Criteria for considering studies for this review

Types of studies

Individual participant and cluster‐randomised trials.

Types of participants

Participants 65 years of age or older who were admitted to hospital for acute care or inpatient rehabilitation after an acute admission with medical, psychological, functional, or social problems.

Types of interventions

Comprehensive geriatric assessment (CGA) can be delivered on a specialist CGA ward or across several wards by a mobile team. On a CGA ward, care is provided by a specialist team that conducts a tailored assessment across a variety of domains, while possibly using standardised assessment tools to gather information. Assessment findings are discussed in a multi‐disciplinary meeting, and a plan of treatment is developed. Members of the multi‐disciplinary team are responsible for delivering the recommended treatment or rehabilitation plan (such as physiotherapy, occupational therapy, or medical treatment). CGA delivered by a mobile team also includes a multi‐disciplinary assessment of a patient that is performed on one or more general medical wards. This is followed by a multi‐disciplinary team meeting that results in a recommended plan for treatment with recommendations passed on to the ward team (medical and nursing staff). Multi‐disciplinary team members may or may not be involved in delivering direct care (e.g. physiotherapy input).

We searched for trials that compared CGA for older people (over 65) admitted to hospital (conducted on CGA wards or by mobile team) versus general medical care.

We excluded studies of condition‐specific interventions (e.g. stroke units, geriatric orthopaedic rehabilitation) that require specialist skills for assessment, acute management, and rehabilitation (Handoll 2009; SUTC 2013).

Types of outcome measures

Primary outcomes

  • Living at home (the inverse of death or institutionalisation combined; used to describe someone who is alive and in own home at follow‐up)

Secondary outcomes

  • Mortality (death)

  • Admission to a nursing home

  • Dependence

  • Activities of daily living (as measured and reported by trialists)

  • Cognitive function

  • Length of stay

  • Re‐admission

  • Cost and cost‐effectiveness

Search methods for identification of studies

Electronic searches

We searched the following databases with no restrictions (language or date) on 5 October 2016.

  • Cochrane Central Register of Controlled Trials (CENTRAL; 2016, Issue 9) in the Cochrane Library.

  • MEDLINE (including Epub Ahead of Print, In‐Process & Other Non‐Indexed Citations) via OvidSP (from 1946).

  • Embase via OvidSP (from 1974).

  • CINAHL EbscoHOST (Cumulative Index to Nursing and Allied Health Literature; from 1982).

  • DARE (Database of Abstracts of Reviews of Effects; 2015, Issue 2) in the Cochrane Library.

  • HTA (Health Technology Assessment Database; 2016, Issue 3) in the Cochrane Library.

We also searched these clinical trials registers on 5 October 2016.

We reported the search strategies in Appendix 1,

Searching other resources

We checked the reference lists of included trials and the following related systematic reviews and meta‐analyses: Bachmann 2010; Baztan 2009; Baztan 2011; Conroy 2011; Extermann 2007; Van Craen 2010.

Data collection and analysis

Selection of studies

One review author (MG) read all abstracts and retrieved full‐text papers for publications that appeared relevant. Two review authors (MG, GE) independently assessed their eligibility, selected studies for inclusion in the review according to prespecified inclusion criteria, and resolved disagreements by discussion with a third review author (SS).

Data extraction and management

Two review authors (MG, GE) independently extracted data, using a modified version of the Cochrane good practice extraction form (EPOC 2017a). We resolved disagreements and areas of uncertainty by discussion with SS.

We contacted investigators of included trials by email or by telephone to invite them to contribute trial data and to complete a questionnaire to provide details about delivery of CGA. This survey included questions on the population using the service; intervention characteristics (including details of core team members); key components of the CGA intervention; and control group characteristics. We sent each trialist a minimum of three reminders to provide this information.

Survey of trialists

We contacted investigators of included trials by email or by telephone to invite them to contribute trial data and to complete a questionnaire to provide details about delivery of CGA. We sent a minimum of three reminders to each trialist. We sent a survey to trialists of the 29 trials included in the review to obtain a detailed description of the CGA models evaluated in these trials. The survey included questions on the population using the service; intervention characteristics (including details of core team members); key components of the CGA intervention; and control group characteristics.

Assessment of risk of bias in included studies

Three review authors (MG, GE, SS) independently assessed risk of bias of included trials by using the suggested risk of bias criteria and guidance for EPOC reviews (EPOC 2017b). We resolved areas of uncertainty or disagreement by discussion.

Measures of treatment effect

We calculated risk ratios (RRs) with 95% confidence intervals (CIs) using fixed‐effect inverse variance meta‐analysis for living at home, mortality, admission to a nursing home, and dependence as measured by an activities of daily living scale (Deeks 2001; see Analysis 1.7 for details).

For the continuous outcomes 'activities of daily living' (ADLs) and 'cognitive function', we calculated standardised mean differences (SMDs) and for the continuous outcome 'length of stay', we calculated mean differences (MDs). We calculated odds ratios (ORs) with 95% CIs for data from studies that provided individual patient data (IPD) using logistic regression meta‐analysis.

Unit of analysis issues

All included trials were individual participant randomised trials. We noted no unit of analysis issues.

Dealing with missing data

We contacted the authors of included trials to request missing data.

Assessment of heterogeneity

We carried out tests of heterogeneity using Cochran's Q and the I² statistic (Cochran 1954; Higgins 2003). We did not retain a pooled analysis if values of I² were greater than 70%. We also considered trial characteristics such as CGA delivered by a team or on a geriatric ward, and we grouped studies on the basis of these characteristics.

Assessment of reporting biases

We assessed reporting bias by creating a funnel plot for the main outcome (living at home) at 3 to 12 months' follow‐up, recognising that when a small number of trials are included, these plots are not necessarily indicative of publication bias.

Data synthesis

We combined published data using fixed‐effect meta‐analysis for living at home, death, admitted to a nursing home, dependence, ADLs, cognitive function, re‐admissions, and length of stay. We grouped trials by ward or by team for all outcomes, as suggested by previous reviews (Ellis 2011; Stuck 1993). When multi‐arm trials are included (Cohen 2002 GEMC; Nikolaus 1999), we analysed each intervention group separately and described this analysis in the table of included trials. We calculated standardised mean differences for the different scales used to measure ADLs and cognitive function. We analysed dependence by combining a binary definition of dependence (as defined by trials) with deterioration in ADLs.

We conducted a meta‐regression analysis by using a fixed‐effect model to assess effects of trial covariates on living at home at the end of follow‐up (3 to 12 months) (Thompson 1999). Trial covariates consisted of team or ward intervention; age or frailty as a criterion for targeting delivery of CGA (frailty typically included criteria such as geriatric syndromes, risk of nursing home admission, and functional or cognitive impairment); timing of admission from emergency department directly or after 72 hours (stepdown); and outpatient follow‐up. We used post‐estimation Wald tests to derive F ratios and P values.

We used STATA version 13 and Review Manager 5 when performing all analyses (Review Manager 2014; STATA 13) .

In the survey, we asked trialists to report elements of CGA that were most critical to success; processes of care followed; and staff profiles of the control group. We counted these elements of CGA, and reported them in the results as a fraction of the total number of trialists (N = 13).

Cost‐effectiveness

We used length of inpatient stay (measured in days) from 17 trials as the main driver of resource use (Analysis 1.10), and we derived the costs of providing CGA from IPD provided by one trial (Primary AMIGOS Trial, Edmans 2013; cost‐effectiveness study, Tanajewski 2015); this trial evaluated a version of CGA that included an attending geriatrician in a medical assessment unit and outpatient follow‐up. We valued relative costs using English unit cost prices for 2013/2014, taking a National Health Service (NHS) perspective (NICE 2013), and we compared incremental health outcomes of CGA versus usual care.

For trials that reported the cost of CGA, we used the following measure of cost‐effectiveness.

  • We calculated quality‐adjusted life‐years (QALYs) by converting Barthel Index IPD provided by Edmans 2013, Kircher 2007, and Saltvedt 2002 to EQ‐5D‐3L (EuroQoL Group Quality of Life Questionnaire based on a three‐level scale) UK scores according to Kaambwa 2013. We used data from trials with mean Barthel scores at baseline ranging from 14.0 to 15.2, on a scale of 0 to 20, as these were similar to scores reported in the mapping study of Kaambwa 2013 (from 14.8 to 16.5, on a scale of 0 to 20). Edmans 2013 provided IPD for the EQ‐5D; this allowed us to compare calculated QALYs based on the Barthel index versus QALYs based on EQ‐5D (Edmans 2013).

  • We estimated life‐years (LYs) using IPD from four trials by calculating time to death from recruitment, expressed as a fraction of a year (Edmans 2013; Goldberg 2013; Kircher 2007; Saltvedt 2002).

  • We created a variable called 'life years living at home' (LYLAHs) after discharge from hospital to use as a measure of independence and well‐being in an older population; this was based on IPD from Edmans 2013 and Goldberg 2013.

We used a decision model to estimate an incremental cost‐effectiveness ratio (ICER) of inpatient care with or without CGA. The ICER is expressed as cost per QALY gained, cost per LY gained, and cost per LYLAH gained from a health service perspective. We multiplied the RR of living at home at the end of follow‐up by the incremental LYLAH, to adjust LYLAH with the probability of living at home (Analysis 1.2). We presented in Table 1 the input parameters used in these models. We addressed uncertainty by performing 10,000 draws of all incremental cost and incremental health outcome parameters using prespecified distributions, and by recording incremental costs, incremental QALYs, incremental LYs, and incremental LYLAHs from each draw. We plotted these results on a cost‐effectiveness plane (i.e. a scatterplot graph with incremental costs on the y‐axis and incremental effects on the x‐axis) and on a cost‐effectiveness acceptability curve (i.e. a graph that displays the probability that an intervention will be cost‐effective at different values of a QALY) to display uncertainty in the estimated ICERs.

Open in table viewer
Table 1. Parameters used in the decision model for the economic evaluation

Value

Standard error

Distribution

Alpha

Beta

Source

Probabilities

Risk ratio: living at home (end of follow‐up on ward)

1.070

0.92

Gamma

1.34

0.80

Main meta‐analysis

Risk ratio: living at home (end of follow‐up on ward and by team)

1.060

1.20

Gamma

0.78

1.36

Main meta‐analysis

Risk ratio: admitted to a nursing home (end of follow‐up on ward)

0.780

0.06

Gamma

173.99

0.00

Main meta‐analysis

Risk ratio: admitted to a nursing home (end of follow‐up on ward and by team)

0.810

0.06

Gamma

207.55

0.00

Main meta‐analysis

Resource utilisation

Mean difference in length of stay in hospital

0.029

0.22

Normal

Main meta‐analysis

Mean length of stay in a nursing home after discharge ‐ CGA

49.91

8.12

Gamma

38

1

Saltvedt

Mean length of stay in a nursing home after discharge ‐ UC

40.87

8.44

Gamma

23

2

Saltvedt

Health outcomes

Mean difference in LYLAH

0.009

0.022

Normal

Meta‐analysis based on IPD (Edmans, Saltvedt)

Mean difference in QALY

0.012

0.019

Normal

Meta‐analysis based on IPD (Edmans, Kircher, Saltvedt)

Mean difference in QALY (severe patients)

0.018

0.024

Normal

Meta‐analysis based on IPD (Goldberg, Somme)

Mean difference in time to death

13.061

6.664

Normal

Meta‐analysis based on IPD (Edmans, Goldberg, Kircher, Saltvedt)

Unit costs

Cost of bed day in hospital

874

Weighted average of elective and non‐elective hospitalisation based on national reference costs 2013/2014

Cost of nursing home day

77

Personal social services: Expenditure and unit costs, England ‐ 2013‐14, final release: Unit costs by CASSR

Cost of CGA per patient

208

8.929

Gamma

543

0

Tanajewski et al. 2015, AMIGOS trial

Mean difference in QALY was based on mapping the IPD for the Barthel from three trials (Edmans 2013; Kircher 2007; Saltvedt 2002).

In Saltvedt 2002, baseline Barthel Index was assessed 1.7 days after inclusion in the control group and at 3.5 days in the intervention group.

In Saltvedt 2002, baseline Barthel Index was self‐reported in the control group and was performance‐based in the intervention group.

Certainty of evidence

We graded our confidence in the evidence by creating a 'Summary of findings' table, using the approach recommended by the GRADE Working Group and guidance developed by EPOC (EPOC 2017c; Guyatt 2008). We included the most important outcomes of living at home, mortality, admission to a nursing home, dependence, cognitive function, hospital length of stay, and cost‐effectiveness. We used methods and recommendations described in Section 8.5 and Chapter 12 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011), along with GRADE worksheets, to assess the certainty of evidence (GRADEpro GDT 2015). Three review authors (MG, SS, GE) independently assessed the certainty of evidence.

Subgroup analysis and investigation of heterogeneity

We conducted fixed‐effect logistic regression meta‐analyses on IPD from five studies (N = 1767 participants) for two outcomes: living at home and mortality (Edmans 2013; Goldberg 2013; Kircher 2007; Somme 2010; Saltvedt 2002). We analysed a third outcome (time to death) by performing fixed‐effect time‐to‐event meta‐analysis (Edmans 2013; Goldberg 2013; Kircher 2007; Somme 2010). We adjusted all three meta‐analyses for participant age and sex and baseline Barthel Index by applying a threshold of ≤ 15/20 for moderate to severe disability (Rudd 1997).

We created a model for each trial, and we pooled parameters using a weighted average. We combined log odds ratios for living at home and death, using fixed‐effect inverse variance meta‐analysis (Deeks 2001). We used Cox regression models to calculate the log hazard ratio and its standard error for living at home and death separately for each trial data set. We expressed the pooled effect as the hazard ratio for inpatient CGA compared with general medical care.

Sensitivity analysis

We ran a random‐effects meta‐analysis in a sensitivity analysis for primary outcomes and compared these results with results of fixed‐effect meta‐analysis (Deeks 2001; DerSimonian 1986); we also assessed the impact of excluding three trials that included participants who were admitted from a nursing home for the outcomes living at home and admitted to a nursing home (Asplund 2000; McVey 1989; Rubenstein 1984). We assessed the impact of using data at 6 months' follow‐up, rather than at 12 months' follow‐up, for three trials (Applegate 1990; Counsell 2000; Saltvedt 2002), for the primary outcome living at home (end of follow‐up 3 to 12 months).

We performed two univariate sensitivity analyses for the cost‐effectiveness analysis. In the first (van Exel 2004), we mapped EQ‐5D utilities to the Barthel Index using data from two trials that recruited a more dependent population (mean Barthel Index of 9 at baseline) (Goldberg 2013; Somme 2010); in the second, we used the RR for delivering CGA on a dedicated ward and excluded studies evaluating CGA delivered by mobile teams for the outcomes living at home (Analysis 1.2); and being admitted to a nursing home at the end of follow‐up (Analysis 1.6).

Results

Description of studies

Results of the search

We retrieved 7147 unique records and excluded 7131 records on the basis of title and abstract screening. We retrieved the full text of 16 potentially relevant records and excluded eight with reasons. We identified seven new trials (from eight publications) for inclusion in this review (Barnes 2012; Boustani 2012; Edmans 2013; Goldberg 2013; Li 2015; Somme 2010; Wald 2011). This review now includes 29 randomised trials. Figure 1 outlines the study selection process.


PRISMA flow diagram.

PRISMA flow diagram.

Included studies

See Characteristics of included studies.

We included 29 randomised trials involving 13,766 participants that examined the effectiveness of CGA provided for older adults admitted to hospital.

Most included trials were conducted in North America: 16 in the USA and four in Canada. Two trials were conducted in Germany and in the UK, and single trials were reported from Australia, China, Norway, France, and Sweden. Eleven trials targeted CGA to the frailest or most at‐risk participants (Applegate 1990; Cohen 2002 GEMC; Edmans 2013; Goldberg 2013; Kay 1992; Kircher 2007; Nikolaus 1999; Rubenstein 1984; Saltvedt 2002; White 1994; Winograd 1993), and 11 targeted CGA on the basis of age alone (Asplund 2000; Barnes 2012; Collard 1985; Counsell 2000; Fretwell 1990; Harris 1991; Landefeld 1995; McVey 1989; Naughton 1994; Somme 2010; Wald 2011).

Trialists delivered CGA in a dedicated geriatric ward environment in 20 trials (Applegate 1990; Asplund 2000; Barnes 2012; Boustani 2012; Cohen 2002 GEMC; Collard 1985; Counsell 2000; Fretwell 1990; Goldberg 2013; Harris 1991; Kay 1992; Landefeld 1995; Nikolaus 1999; Powell 1990; Rubenstein 1984; Saltvedt 2002; Shamian 1984; Somme 2010; Wald 2011; White 1994), and by using a mobile team on a general medical ward in eight trials (Edmans 2013; Hogan 1987; Kircher 2007; McVey 1989; Naughton 1994; Reuben 1995; Thomas 1993; Winograd 1993).

We have presented intervention components for all studies in Figure 2.


Components of in‐hospital CGA and staff profiles.∙ Present or carried out∘ Recommendation made or staff accessed from general poolWhen it was unclear or was not explicitly stated in the paper, it has been left blank.Two trials (Li 2015; Powell 1990) are excluded from , as full details of the intervention components were not available.

Components of in‐hospital CGA and staff profiles.

∙ Present or carried out

∘ Recommendation made or staff accessed from general pool

When it was unclear or was not explicitly stated in the paper, it has been left blank.

Two trials (Li 2015; Powell 1990) are excluded from Figure 2, as full details of the intervention components were not available.

In the AMIGOS trial (Edmans 2013; Tanajewski 2015), the intervention was case management by a geriatrician at the point of discharge from an acute medical unit, and no other staff served as core team members. In one trial (Goldberg 2013), the CGA intervention consisted of care in a specialist medical and mental health unit that admitted people with delirium or dementia. We counted Cohen 2002 GEMC as two trials, as the investigators used a 2 × 2 factorial design that compared care received in an inpatient geriatric evaluation and management unit versus usual care, followed by outpatient care in a geriatric evaluation and management clinic versus usual outpatient care (Cohen 2002 UCOP; Cohen 2002 GEMC). We also counted Nikolaus 1999 as two trials owing to the different CGA interventions evaluated: CGA ward and CGA ward plus early supported discharge intervention versus usual care (Nikolaus 1999; Nikolaus 1999 plus ESD).

Most trials described the control group as receiving usual care. In three trials, the control group received enhanced usual care (Boustani 2012; Edmans 2013; Goldberg 2013), and in one trial (Goldberg 2013), care on geriatric medical wards (70%) and general medical wards (30%). In another study (Boustani 2012), 49% of the control group received CGA compared with 56% of the intervention group. Nine trials provided outpatient follow‐up (Barnes 2012; Cohen 2002 GEMC; Collard 1985; Counsell 2000; Edmans 2013; Fretwell 1990; Naughton 1994; Nikolaus 1999 plus ESD; Rubenstein 1984). Duration of follow‐up ranged from 3 to 12 months.

Elements of CGA

Thirteen of the 29 trialists completed the survey (Applegate 1990; Asplund 2000; Edmans 2013; Goldberg 2013; Hogan 1987; Kircher 2007; Reuben 1995; Rubenstein 1984; Saltvedt 2002; Somme 2010; Thomas 1993; Wald 2011; White 1994), and reported tailoring treatment plans to the individual (13/13 trials); holding multi‐disciplinary team meetings (12/13 trials); providing clinical leadership (11/13 trials); having speciality knowledge, experience, and competence (11/13 trials); and involving participants and carers in goal setting (10/13 trials) were the most common key components of CGA (Figure 3). In Figure 4, we detailed the processes of care and information on staff working in the control group, as reported by the trialists. In Figure 2, we displayed the staff profile of the CGA intervention group for comparison.


Key components of CGA reported by trialists.∙ Components critical to success

Key components of CGA reported by trialists.

∙ Components critical to success


Components of in‐hospital control group: processes of care and staff profiles.• Present or carried out

Components of in‐hospital control group: processes of care and staff profiles.

• Present or carried out

Excluded studies

We excluded eight trials. Reasons for exclusion were secondary analyses (Gharacholou 2012; Nipp 2012) of an included trial (Cohen 2002 GEMC); a non‐randomised study (Mudge 2012; Yoo 2013a; Yoo 2013b; Yoo 2014); an ineligible intervention (Abizanda 2011); and elective admission of participants to inpatient care (Kehusmaa 2010). (See Characteristics of excluded studies.)

Risk of bias in included studies

We reported risk of bias assessments of the included studies in Figure 5. As two trials were available only as abstracts reporting limited information (Li 2015; Powell 1990), we were unable to complete a risk of bias assessment. For the domain of other bias, we assessed risk of bias due to contamination of the control group.


'Risk of bias' graph: review authors' judgements about each risk of bias item presented as percentages across all 29 included studies. Only one risk of bias classification is given for the split studies (Cohen 2002 GEMC and Cohen 2002 UCOP; Nikolaus 1999 and Nikolaus 1999 plus ESD). therefore represents the risk of bias classification for the 29 included studies. White spaces reflect the unassessed split studies.

'Risk of bias' graph: review authors' judgements about each risk of bias item presented as percentages across all 29 included studies. Only one risk of bias classification is given for the split studies (Cohen 2002 GEMC and Cohen 2002 UCOP; Nikolaus 1999 and Nikolaus 1999 plus ESD). Figure 5 therefore represents the risk of bias classification for the 29 included studies. White spaces reflect the unassessed split studies.

Allocation

We assessed 26 trials as having low or unclear risk of bias for random sequence generation, and one trial as having high risk of bias (Wald 2011), as the sequence was generated by odd or even numbers on the medical record. For allocation concealment, we classified 25 trials as having low (Asplund 2000; Barnes 2012; Cohen 2002 GEMC; Counsell 2000; Edmans 2013; Goldberg 2013; Landefeld 1995; Naughton 1994; Nikolaus 1999 plus ESD; Saltvedt 2002; Somme 2010; Winograd 1993), or unclear risk of bias (Applegate 1990; Boustani 2012; Collard 1985; Fretwell 1990; Hogan 1987 ;Kay 1992; Kircher 2007; McVey 1989; Reuben 1995; Rubenstein 1984; Shamian 1984; Thomas 1993; White 1994). We assessed two trials as having high risk of bias for allocation concealment because investigators used an open allocation schedule (Harris 1991; Wald 2011).

Blinding

We classified all trials as having high risk of performance bias, as it was not possible to blind participants or researchers to the allocated intervention (detection bias). We assessed objective measures of outcome as having low risk of bias, including the primary outcome 'living at home'. We assessed several trials as having low risk of bias for blinding of assessment of subjective outcomes, as researchers described outcome assessors as blind to the allocation (Cohen 2002 GEMC; Goldberg 2013; Kircher 2007; McVey 1989; Naughton 1994; Nikolaus 1999 plus ESD; Nikolaus 1999). One trial stated that outcome assessors were not blinded to functional status, and we assessed this study as having high risk of bias (Wald 2011).

Incomplete outcome data

We assessed three trials as having high risk of bias for addressing incomplete outcome data (attrition bias) (Asplund 2000; Collard 1985; Naughton 1994). One trial reported attrition for functional outcomes that exceeded 25% (Collard 1985). We classified six trials as having low risk of bias for incomplete outcome data (attrition bias) (Barnes 2012; Boustani 2012; Fretwell 1990; Goldberg 2013; Kircher 2007; Landefeld 1995). We classified 18 trials as having unclear risk of bias (Applegate 1990; Cohen 2002 GEMC; Counsell 2000; Edmans 2013; Harris 1991; Hogan 1987; Kay 1992; McVey 1989; Nikolaus 1999 plus ESD; Reuben 1995; Rubenstein 1984; Saltvedt 2002; Shamian 1984; Somme 2010; Thomas 1993; Wald 2011; White 1994; Winograd 1993), as investigators provided no data on attrition or exclusions.

Selective reporting

Twenty‐five trials did not publish a protocol, hence we assessed them as having unclear risk of selective reporting bias. Four trials did publish protocols (Edmans 2013; Goldberg 2013; Kircher 2007; Reuben 1995), and two trials published protocols with prespecified outcomes (Edmans 2011; Harwood 2011).

Other potential sources of bias

We assessed risk of bias due to contamination of the control group. A total of 21 trials provided little evidence that the control group had received CGA (Applegate 1990; Asplund 2000; Barnes 2012; Cohen 2002 GEMC; Collard 1985; Counsell 2000; Edmans 2013; Fretwell 1990; Harris 1991; Hogan 1987; Kay 1992; Landefeld 1995; McVey 1989; Naughton 1994; Rubenstein 1984; Saltvedt 2002; Shamian 1984; Somme 2010; Thomas 1993; White 1994; Winograd 1993). In six trials, it is likely that the control group received the intervention, hence we classified these trials as having high risk of bias as to whether the study adequately protected against contamination (Boustani 2012; Goldberg 2013; Kircher 2007; Nikolaus 1999 plus ESD; Reuben 1995; Wald 2011). In one of these trials (Boustani 2012), 49% of the control group versus 56% of the intervention group received CGA; we did not include data from this trial in the meta‐analysis.

We assessed publication bias by creating a funnel plot for the main outcome living at home at the end of follow‐up (Figure 6). The Harbord test (bias = 0.87, P = 0.18) and Egger's test (bias = 0.87, P = 0.17) show little evidence of small‐trial bias for the main outcome living at home at the end of follow‐up (3 to 12 months).


Funnel plot of comparison: 1 CGA versus usual care, outcome: 1.2 Living at home (end of follow‐up 3 to 12 months).

Funnel plot of comparison: 1 CGA versus usual care, outcome: 1.2 Living at home (end of follow‐up 3 to 12 months).

Effects of interventions

See: Summary of findings for the main comparison Comprehensive geriatric assessment (CGA) versus admission to hospital without CGA

Living at home

CGA increases the likelihood that patients will be alive and in their own homes ('living at home') at hospital discharge (risk ratio (RR) 1.05, 95% confidence interval (CI) 1.01 to 1.10; 11 trials; 4346 participants (32% of the total number of participants); high‐certainty evidence; I² = 43%) (Barnes 2012; Collard 1985; Fretwell 1990; Kay 1992; Landefeld 1995; McVey 1989; Naughton 1994; Rubenstein 1984; Wald 2011; White 1994; Winograd 1993). See Analysis 1.1.

CGA also increases the likelihood that patients will be 'living at home' at 3 to 12 months' follow‐up (RR 1.06, 95% CI 1.01 to 1.10; 16 trials; 6799 participants (49% of the total number of participants); high‐certainty evidence; I² = 13%) (Applegate 1990; Asplund 2000; Cohen 2002 GEMC; Cohen 2002 UCOP; Counsell 2000; Edmans 2013; Goldberg 2013; Kircher 2007; Landefeld 1995; McVey 1989; Nikolaus 1999; Nikolaus 1999 plus ESD; Rubenstein 1984; Saltvedt 2002; Somme 2010; Winograd 1993). See Analysis 1.2.

Mortality (death)

CGA results in little or no difference in mortality at discharge (RR 1.04, 95% CI 0.82 to 1.32; 11 trials; 4346 participants (32% of the total number of participants); high‐certainty evidence; I² = 16%) (Barnes 2012; Collard 1985; Fretwell 1990; Kay 1992; Landefeld 1995; McVey 1989; Naughton 1994; Rubenstein 1984; Wald 2011; White 1994; Winograd 1993), or at 3 to 12 months' follow‐up (RR 1.00, 95% CI 0.93 to 1.07; 21 trials; 10,023 participants (73% of the total number of participants); high‐certainty evidence; I² = 0%) (Applegate 1990; Asplund 2000; Cohen 2002 GEMC; Cohen 2002 UCOP; Counsell 2000; Edmans 2013; Fretwell 1990; Goldberg 2013; Harris 1991; Kircher 2007; Landefeld 1995; McVey 1989; Nikolaus 1999; Nikolaus 1999 plus ESD; Reuben 1995; Rubenstein 1984; Saltvedt 2002; Shamian 1984; Somme 2010; Thomas 1993; Winograd 1993). See Analysis 1.3 and Analysis 1.4.

Admission to a nursing home during follow‐up

CGA decreases the likelihood that patients will be admitted to a nursing home at discharge (RR 0.89, 95% CI 0.81 to 0.98; 12 trials; 4459 participants (32% of the total number of participants); high‐certainty evidence; I² = 31%) (Barnes 2012; Collard 1985; Fretwell 1990; Hogan 1987; Kay 1992; Landefeld 1995; McVey 1989; Naughton 1994; Rubenstein 1984; Wald 2011; White 1994; Winograd 1993); and at 3 to 12 months' follow‐up (RR 0.80, 95% CI 0.72 to 0.89; 14 trials; 6285 participants (46% of the total number of participants); high‐certainty evidence; I² = 3%) (Applegate 1990; Asplund 2000; Cohen 2002 GEMC; Cohen 2002 UCOP; Counsell 2000; Edmans 2013; Goldberg 2013; Kircher 2007; Landefeld 1995; McVey 1989; Nikolaus 1999; Nikolaus 1999 plus ESD; Rubenstein 1984; Saltvedt 2002; Winograd 1993). See Analysis 1.5 and Analysis 1.6. It is not clear from the trials that contributed to the analysis of admission to nursing home at discharge if participants were a new nursing home admission, or if they had previously resided in a nursing home.

Dependence

CGA results in little or no difference in dependence (RR 0.97, 95% CI 0.89 to 1.04; 14 trials; 6551 participants (48% of the total number of participants); high‐certainty evidence; I² = 0%) (Asplund 2000; Barnes 2012; Collard 1985; Counsell 2000; Edmans 2013; Fretwell 1990; Landefeld 1995; McVey 1989; Nikolaus 1999; Nikolaus 1999 plus ESD; Rubenstein 1984; Saltvedt 2002; Somme 2010; Thomas 1993). We included data from one trial despite a large dropout rate (25.7% for intervention; 44.0% for control) for this one outcome (Collard 1985). Analysis that excludes the data from this trial has little effect on the summary estimate (RR 0.95, 95% CI 0.88 to 1.04; 13 trials; 6122 participants (44% of the total number of participants); I² = 0%). See Analysis 1.7.

Cognitive function

A total of five trials reported cognitive function at follow‐up, due to a high level of statistical heterogeneity we did not retain the meta‐analysis (3534 participants (26% of the total number of participants); low‐certainty evidence; I2 = 73%) (Asplund 2000; Goldberg 2013; Kircher 2007; Reuben 1995; Winograd 1993). For cognitive function we calculated standardised mean differences to standardise the results of the studies to a uniform scale before they could be combined. This was because the outcome was measured in a variety of ways.The standardised mean difference ranged from ‐0.22 to 0.35. We are uncertain of the impact of CGA on cognitive function, as the certainty of this evidence is low.

Length of stay

A total of 17 trials reported length of stay data. Owing to a high level of statistical heterogeneity, we did not retain the meta‐analysis (5303 participants (39% of the total number of participants); low‐certainty evidence; I² = 80%) (Asplund 2000; Cohen 2002 GEMC; Cohen 2002 UCOP; Edmans 2013; Fretwell 1990; Goldberg 2013; Harris 1991; Hogan 1987; McVey 1989; Naughton 1994; Nikolaus 1999; Nikolaus 1999 plus ESD; Saltvedt 2002; Somme 2010; Thomas 1993; Wald 2011; Winograd 1993). Mean hospital length of stay ranged from 3.4 days to 40.7 days in the CGA group, and from 3.1 days to 42.8 days in the control group, with a mean difference of ‐23.60 to 9.00 days. See Analysis 1.10.

Costs and cost‐effectiveness

Table 2 presents the costs reported by trialists; owing to variation in time periods (1985 to 2013) and in resources that were costed we did not include these data in the analysis of costs. Instead, we used length of inpatient stay, as this unit is commonly used in costing hospital resources because it is the main driver of resource use. We used the meta‐analysis of published data from 17 trials to estimate the incremental cost, as well as individual patient data (IPD) from five trials to estimate incremental health outcomes of CGA versus usual care (Edmans 2013; Goldberg 2013; Kircher 2007; Saltvedt 2002; Somme 2010). We estimated healthcare costs (including hospitalisation and intervention costs) per participant in the CGA group at GBP 234 higher than general medical care without CGA (95% CI GBP ‐£144 to GBP 605) (17 trials; low‐certainty evidence). CGA may lead to a slight increase in QALYs of 0.012 (95% CI ‐0.024 to 0.048) at GBP 19,802 per QALY gained (3 trials provided data on QALYS and 17 trials provided data on resource use; low‐certainty evidence), a slight increase in LYs of 0.037 (95% CI 0.001 to 0.073) at GBP 6305 per LY gained (4 trials provided data on LYs and 17 trials provided data on resource use; low‐certainty evidence), and a slight increase in LYLAH of 0.019 (95% CI ‐0.019 to 0.155) at GBP 12,568 per LYLAH gained (2 trials provided data on LYLAH and 17 trials provided data on resource use; low‐certainty evidence) (Table 3).

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Table 2. Cost data reported by trials

Cost analysis

Trial

Year

Country

Treatment arm

Costs

Comments

Cohen

2002

USA

(US Dollars)

Geriatric Unit + Usual Care Outpatient

36,592 (1844 SD)

Direct cost comparison separated into institutional costs and costs estimated for nursing home admissions based on standardised HMO rates

Usual Care Inpatient + Usual Care Outpatient (Control)

38,624 (2037)

Geriatric Unit + Geriatric Outpatient

35,935 (1829)

Usual Care Inpatient + Geriatric Outpatient (Control)

35,951 (1827)

Collard

1985

USA

(US Dollars)

Choate (Experimental)

4015.17 (SE 0.03)

Direct cost comparison (hospital costs only)

Choate (Control)

4545.13 (SE 0.03)

Symmes (Experimental)

3591.42 (SE 0.03)

Symmes (Control)

4155.54 (SE 0.02)

Fretwell

1990

USA

(US Dollars)

Experiment

3148 (7210 SD)

Direct cost comparison (hospital costs only)

Control

4163 (18,406)

Applegate

1990

USA

(US Dollars)

Geriatric Unit (Rehab Diagnosis)

32,978 (35,130 SD)

Health and social care costs up to 1 year after randomisation

Geriatric Unit (Medical/Surgical Diagnosis)

25,846 (29,628)

Usual Care (Rehab/Diagnosis)

18,409 (16,555)

Usual Care (Medical/Surgical Diagnosis)

15,248 (13,152)

Asplund

2000

Sweden

(Swedish Kronar)

Experiment

10,800 (9300 ‐ 12,300 IQR)

Direct cost comparison (hospital costs only)

Control

12,800 (11,500 ‐ 14,100)

Counsell

2000

USA

(US Dollars)

Experiment

5640

Included in experimental group costs are costs of renovation of geriatric unit

Control

5754

Hogan

1987

Canada

(Canadian Dollars)

Experiment

98.36

Monthly costings for physician services only

Control

77.68

Landefeld

1995

USA

(US Dollars)

Experiment

6608

Direct cost comparison (hospital costs only)

Control

7240

Nikolaus

1999

Germany

(Deutschmark)

Geriatric Unit + ESD

3,365,000 (1,922,400)

Costs for hospital care and nursing homes (estimated as costs per 100 people per year)

Geriatric Unit only

3,983,000 (2,276,000)

Control

4,145,000

Rubenstein

1984

USA

(US Dollars)

Experiment

22,597

Costs per year survived including hospital and nursing home costs

Control

27,826

Naughton

1994

USA

(US Dollars)

Experiment

4525 (5087 SD)

Direct cost comparison (hospital costs only)

Control

6474 (7000)

White

1994

USA

(US Dollars)

Experiment

23,906

Direct cost comparison (hospital costs only)

Control

45,189

Barnes

2012

USA

(US Dollars)

Experiment

9477

Direct cost comparison (hospital costs only)

Control

10,451

Edmans

2013

UK

(UK Pounds)

Experiment

4475 (95% CI 3901 to 5141)

Care cost + intervention cost up to 90 days after hospital discharge

Control

4,057 (95% CI 3367 to 4882)

Wald

2011

USA

(US Dollars)

Experiment

24,617 (15,828 SD)

Direct cost comparison (hospital costs only)

Control

21,488 (13,407 SD)

Owing to variation in time periods (1985 to 2013) and resources costed, these data are not used in the analysis of costs.

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Table 3. Results from main cost‐effectiveness analysis

Incremental healthcare costs (95% CI)

Incremental outcomes (95% CI)

ICER

Probability of CGA being more costly

Probability of CGA being more costly and more effective

Probability of CGA being cost‐effective at GBP 20,000 ceiling ratio

Cost‐utility analysis (outcome is QALY)

GBP 234

(‐144 to 605)

0.012

(‐0.024 to 0.048)

GBP 19,802

0.89

0.66

0.50

Cost‐effectiveness analysis (outcome is LY)

GBP 234

(‐144 to 605)

0.037

(0.001 to 0.073)

GBP 6305

0.89

0.87

0.89

Cost‐effectiveness analysis (outcome is LYLAH)

GBP 234

(‐144 to 605)

0.019

(‐0.019 to 0.155)

GBP 12,568

0.89

0.74

0.47

The probability that CGA would be cost‐effective at a GBP 20,000 ceiling ratio (which is the GBP 20,000 threshold suggested by the National Institute for Health and Care Excellence for QALY, LY, and LYLAH was 0.50, 0.89, and 0.47, respectively (17 trials; low‐certainty evidence) (NICE 2013).

We addressed uncertainty by performing 10,000 draws of all incremental costs and incremental health outcome parameters; CGA was more costly in 89% of 10,000 generated incremental cost‐effectiveness ratios (ICERs) and led to QALY gains in 66% of cases, LY gains in 87% of cases, and LYLAH gains in 74% of cases. When effectiveness is measured in LYs gained, the probability that CGA will be cost‐effective is above 90% when the ceiling ratio is increased to GBP 25,000 or higher (Figure 7). However, the probability that CGA will be considered cost‐effective is 68% and 72% for QALY and LYLAH gained, respectively, at a ceiling ratio of GBP 75,000 (Figure 7). We have plotted in Figure 8, Figure 9, and Figure 10 the distribution of each draw of all incremental cost and incremental health outcome parameters and have displayed the uncertainty in estimated ICERs (Appendix 2).


Probability of CGA being cost‐effective.

Probability of CGA being cost‐effective.


Cost‐effectiveness plane with ICERs expressed as cost per QALY gained.

Cost‐effectiveness plane with ICERs expressed as cost per QALY gained.


Cost‐effectiveness plane with ICER expressed as cost per LY gained.

Cost‐effectiveness plane with ICER expressed as cost per LY gained.


Cost‐effectiveness plane with ICERs expressed as cost per LYLAH gained.

Cost‐effectiveness plane with ICERs expressed as cost per LYLAH gained.

Activities of daily living (ADLs)

CGA probably leads to little or no difference in ADLs (SMD 0.04, 95% CI ‐0.06 to 0.15; 7 trials; 1445 participants (10% of the total number of participants); moderate‐certainty evidence; I² = 0%) (Applegate 1990; Goldberg 2013; Nikolaus 1999; Nikolaus 1999 plus ESD; Somme 2010; Thomas 1993; Winograd 1993). See Analysis 1.8.

Re‐admission

CGA results in little or no difference in re‐admission to hospital (RR 1.02, 95% CI 0.94 to 1.11; 13 trials; 6698 participants (49% of the total number of participants); high‐certainty evidence; low heterogeneity; I² =0%) (Asplund 2000; Barnes 2012; Counsell 2000; Edmans 2013; Goldberg 2013; Kircher 2007; Landefeld 1995; Nikolaus 1999; Nikolaus 1999 plus ESD; Rubenstein 1984; Saltvedt 2002; Wald 2011; White 1994). See Analysis 1.11.

Results from meta‐regression

CGA delivery on wards or by teams

Differences in effectiveness of CGA delivery between wards and teams on living at home were uncertain, as this analysis was underpowered (at discharge: F = 1.91, P = 0.20, N = 8 trials ward, N = 3 trials team; end of follow‐up (3 to 12 months): F = 3.54, P = 0.08, N = 12 trials ward, N = 4 trials team).

Age or frailty as a criterion for targeting delivery of CGA

Differences in effectiveness between age and frailty as a criterion for targeting CGA delivery on living at home were uncertain (at discharge: F = 0.18, P = 0.68, N = 7 trials age, N = 4 trials frailty; end of follow‐up (3 to 12 months): F = 0.98, P = 0.34, N = 5 trials age, N = 11 trials frailty).

Timing of admission from emergency department (direct or stepdown)

Differences in effectiveness of CGA delivery between direct and stepdown admission from emergency department on living at home were uncertain (at discharge: F = 0.51, P = 0.49, N = 6 trials direct, N = 4 trials stepdown; end of follow‐up (3 to 12 months): F = 0.45, P = 0.51, N = 4 trials direct, N = 7 trials stepdown).

Outpatient follow‐up

Differences in effectiveness of CGA delivery between outpatient follow‐up and no outpatient follow‐up on living at home were uncertain (at end of follow‐up: F = 0.17, P = 0.69, N = 5 trials outpatient follow‐up, N = 7 trials no outpatient follow‐up).

Subgroup analysis using IPD

Results of subgroup analysis using IPD indicate that in the five trials providing IPD (1692 participants (12% of the total number of participants); adjusted for age, sex, and frailty) there was little or no difference in the odds of living at home at the end of follow‐up for participants in the intervention group versus the control group (odds ratio (OR) 0.95, 95% CI 0.74 to 1.24; I² = 0%; Edmans 2013; Goldberg 2013; Kircher 2007; Somme 2010; Saltvedt 2002) (Table 4; Table 5; Table 6). Similarly, results on mortality indicate little or no difference in the odds of mortality at end of follow‐up (OR 0.92, 95% CI 0.70 to 1.21; I² = 0%). Time‐to‐event meta‐analysis allowed for the possibility that each trial may have a different baseline hazard function; results show little or no difference in the time to death (hazard ratio (HR) 0.88, 95% CI 0.72 to 1.08) (Appendix 3).

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Table 4. Outcome living at home: FE meta‐analysis (intervention vs control) adjusting for baseline Barthel measures (binary), age, and sex

Study

OR

95% lower

95% upper

% weight

Edmans

0.711

0.376

1.346

16.39

Goldberg

1.147

0.821

1.603

59.66

Kircher

0.733

0.359

1.496

13.11

Somme

0.339

0.018

6.396

0.77

Saltvedt

0.79

0.35

1.783

10.07

Overall effect

0.954

0.737

1.236

100

In Saltvedt 2002, baseline Barthel Index was assessed 1.7 days after inclusion in the control group and at 3.5 days in the intervention group
Also in this trial, baseline Barthel Index was self‐reported in the control group and was performance‐based in the intervention group. This trial showed an impact on mortality and living at home at 3 to 6 months. For consistency, however, data from 12‐month outcomes are provided

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Table 5. Outcome death: FE meta‐analysis (intervention vs control) adjusting for baseline Barthel measures (binary), age, and sex

Study

OR

95% lower

95% upper

% weight

Edmans

0.965

0.412

2.259

10.49

Goldberg

0.915

0.621

1.349

50.41

Kircher

0.852

0.379

1.916

11.55

Somme

0.784

0.231

2.664

5.08

Saltvedt

0.989

0.553

1.769

22.47

Overall effect

0.922

0.7

1.214

100

In Saltvedt 2002, baseline Barthel Index was assessed 1.7 days after inclusion in the control group and at 3.5 days in the intervention group

Also in this trial, baseline Barthel Index was self‐reported in the control group and was performance‐based in the intervention group. This trial showed an impact on mortality and living at home at 3 to 6 months. For consistency, however, data from 12‐month outcomes are provided

Open in table viewer
Table 6. Outcome time to event (death): FE meta‐analysis (intervention vs control) adjusted for age, sex, and Barthel baseline (binary)

Hazard ratio

SE

95% CI lower

95% CI upper

P value

Treatment

0.883

0.091

0.723

1.080

0.227

Age

0.996

0.008

0.980

1.012

0.597

Sex

0.955

0.122

0.743

1.227

0.718

Barthel BL

0.648

0.117

0.455

0.922

0.016

In Saltvedt 2002, baseline Barthel Index was assessed 1.7 days after inclusion in the control group and at 3.5 days in the intervention group

Also in this trial, baseline Barthel Index was self‐reported in the control group and was performance‐based in the intervention group

Sensitivity analysis

Re‐running analyses using random‐effects rather than fixed‐effect models had little effect on associations between intervention and primary or secondary outcome measures (data not shown). Re‐running the analysis while excluding trials that did not omit nursing home admissions at baseline had little effect on associations between intervention and admission to a nursing home at 3 to 12 months' follow‐up (data not shown). Also, re‐running the analysis for living at home at 3 to 12 months' follow‐up by using data from 6 months' rather than from 12 months' follow‐up for three trials that reported both lengths of follow‐up, had little effect on the association (data not shown). CGA became more cost‐effective when incremental QALYs for a more dependent population were calculated on the basis of data from two trials (Goldberg 2013; Somme 2010). We also calculated cost per LYLAH gained using the summary estimate for living at home that was derived from trials evaluating CGA delivered on a specialist ward and by a mobile team. We found that CGA delivered on a specialist ward is slightly more cost‐effective.

Discussion

Summary of main results

See summary of findings Table for the main comparison

We included 29 randomised trials evaluating the effectiveness of comprehensive geriatric assessment (CGA) versus inpatient care without CGA. Older people admitted to hospital who receive CGA may be more likely to survive and return home (16 trials, 6799 participants) and were less likely to be admitted to a nursing home during 3 to 12 months' follow‐up (14 trials, 6285 participants). We are uncertain whether results show a difference in effect between wards and teams, as this analysis was underpowered. Evidence for the cost‐effectiveness analysis is of low‐certainty owing to imprecision and inconsistency among studies.

Overall completeness and applicability of evidence

The included trials were published between 1984 and 2013, and were conducted in nine countries ‐ most (16 trials) in the USA. Delivery of healthcare services and the role of the CGA will inevitably have evolved during this period. Despite this, CGA has maintained a central position in delivery of person‐centred health care for older people with frailty. Findings from the survey of trialists suggest more commonalities than differences in the way CGA is organised and delivered but some variation in the way the intervention was implemented. In one trial (Goldberg 2013), the CGA ward was a specialist medical and mental health unit, and in another (Edmans 2013), the intervention consisted of case management by a geriatrician at the point of discharge. In most trials, the control group received care on the general medical ward, and in two trials (Goldberg 2013; Kircher 2007), control group care could include a dedicated ward for older people. Social care costs, which were relevant to evaluation of CGA, were not included in the cost‐effectiveness analysis because of lack of reliable data. Applying these findings to other settings would require re‐estimation of the model based on context‐specific unit costs and utilities (Shemilt 2011).

Certainty of evidence

We judged the certainty of evidence as high for the outcome 'living at home' and for mortality, admission to a nursing home, and dependency. Overall risk of bias was low, and trials showed consistency, except in cognitive function and hospital length of stay. A limitation of this review is that we received individual patient data (IPD) from only 5 of 29 trials, hence this subgroup analysis was not representative of the 29 included trials. We judged the certainty of evidence to be low for the cost‐effectiveness analysis owing to imprecision and inconsistency. We derived outcomes in the cost‐effectiveness analysis from five trials providing IPD, and we based hospitalisation costs on data from 17 trials reporting length of stay data and providing low‐certainty evidence. We based the cost of CGA delivery on one trial and found no data for social care costs. The effect of CGA delivered by teams is uncertain, and subgroup analysis of the effect of ward‐delivered versus team‐delivered CGA was underpowered (Appendix 4).

Potential biases in the review process

We limited publication bias by conducting an extensive search that included different databases of published articles and sources of unpublished literature. One review author screened all search results and generated a long list (using an overly inclusive approach), from which two review authors independently selected eligible studies.

Agreements and disagreements with other studies or reviews

The findings of this review are consistent with those of the first systematic review on CGA (Stuck 1993); this review reported that CGA increased the likelihood that patients will be living at home at follow‐up, and that control over medical recommendations and extended outpatient follow‐up were likely to improve health outcomes. Subsequently, several published systematic reviews provided consistent findings (Bachmann 2010; Baztan 2009; Ellis 2005; Van Craen 2010). A review of CGA assessment performed to improve outcomes for frail older people who were rapidly discharged from acute hospital care (up to 72 hours) included five trials (2287 participants) and found little evidence of benefit for this type of CGA intervention in terms of mortality, institutionalisation, re‐admission, functional outcomes, quality of life, and cognition (Conroy 2011). Another systematic review looked at effects of hospital‐wide interventions (CGA wards, CGA teams, nursing care models, and structural changes in physical environment) provided to improve care for frail older patients and did not identify a single best hospital‐wide intervention (Bakker 2011). Large uncertainty surrounding cost‐effectiveness results is consistent with trial‐based economic evaluation (Melis 2008; Tanajewski 2015).

PRISMA flow diagram.
Figures and Tables -
Figure 1

PRISMA flow diagram.

Components of in‐hospital CGA and staff profiles.∙ Present or carried out∘ Recommendation made or staff accessed from general poolWhen it was unclear or was not explicitly stated in the paper, it has been left blank.Two trials (Li 2015; Powell 1990) are excluded from , as full details of the intervention components were not available.
Figures and Tables -
Figure 2

Components of in‐hospital CGA and staff profiles.

∙ Present or carried out

∘ Recommendation made or staff accessed from general pool

When it was unclear or was not explicitly stated in the paper, it has been left blank.

Two trials (Li 2015; Powell 1990) are excluded from Figure 2, as full details of the intervention components were not available.

Key components of CGA reported by trialists.∙ Components critical to success
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Figure 3

Key components of CGA reported by trialists.

∙ Components critical to success

Components of in‐hospital control group: processes of care and staff profiles.• Present or carried out
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Figure 4

Components of in‐hospital control group: processes of care and staff profiles.

• Present or carried out

'Risk of bias' graph: review authors' judgements about each risk of bias item presented as percentages across all 29 included studies. Only one risk of bias classification is given for the split studies (Cohen 2002 GEMC and Cohen 2002 UCOP; Nikolaus 1999 and Nikolaus 1999 plus ESD). therefore represents the risk of bias classification for the 29 included studies. White spaces reflect the unassessed split studies.
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Figure 5

'Risk of bias' graph: review authors' judgements about each risk of bias item presented as percentages across all 29 included studies. Only one risk of bias classification is given for the split studies (Cohen 2002 GEMC and Cohen 2002 UCOP; Nikolaus 1999 and Nikolaus 1999 plus ESD). Figure 5 therefore represents the risk of bias classification for the 29 included studies. White spaces reflect the unassessed split studies.

Funnel plot of comparison: 1 CGA versus usual care, outcome: 1.2 Living at home (end of follow‐up 3 to 12 months).
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Figure 6

Funnel plot of comparison: 1 CGA versus usual care, outcome: 1.2 Living at home (end of follow‐up 3 to 12 months).

Probability of CGA being cost‐effective.
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Figure 7

Probability of CGA being cost‐effective.

Cost‐effectiveness plane with ICERs expressed as cost per QALY gained.
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Figure 8

Cost‐effectiveness plane with ICERs expressed as cost per QALY gained.

Cost‐effectiveness plane with ICER expressed as cost per LY gained.
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Figure 9

Cost‐effectiveness plane with ICER expressed as cost per LY gained.

Cost‐effectiveness plane with ICERs expressed as cost per LYLAH gained.
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Figure 10

Cost‐effectiveness plane with ICERs expressed as cost per LYLAH gained.

Comparison 1 CGA versus usual care, Outcome 1 Living at home (discharge).
Figures and Tables -
Analysis 1.1

Comparison 1 CGA versus usual care, Outcome 1 Living at home (discharge).

Comparison 1 CGA versus usual care, Outcome 2 Living at home (end of follow‐up 3 to 12 months).
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Analysis 1.2

Comparison 1 CGA versus usual care, Outcome 2 Living at home (end of follow‐up 3 to 12 months).

Comparison 1 CGA versus usual care, Outcome 3 Mortality (discharge).
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Analysis 1.3

Comparison 1 CGA versus usual care, Outcome 3 Mortality (discharge).

Comparison 1 CGA versus usual care, Outcome 4 Mortality (end of follow‐up 3 to 12 months).
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Analysis 1.4

Comparison 1 CGA versus usual care, Outcome 4 Mortality (end of follow‐up 3 to 12 months).

Comparison 1 CGA versus usual care, Outcome 5 Admission to a nursing home (discharge).
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Analysis 1.5

Comparison 1 CGA versus usual care, Outcome 5 Admission to a nursing home (discharge).

Comparison 1 CGA versus usual care, Outcome 6 Admission to a nursing home (end of follow‐up 3 to 12 months).
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Analysis 1.6

Comparison 1 CGA versus usual care, Outcome 6 Admission to a nursing home (end of follow‐up 3 to 12 months).

Comparison 1 CGA versus usual care, Outcome 7 Dependence.
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Analysis 1.7

Comparison 1 CGA versus usual care, Outcome 7 Dependence.

Comparison 1 CGA versus usual care, Outcome 8 Activities of daily living.
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Analysis 1.8

Comparison 1 CGA versus usual care, Outcome 8 Activities of daily living.

Comparison 1 CGA versus usual care, Outcome 9 Cognitive function.
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Analysis 1.9

Comparison 1 CGA versus usual care, Outcome 9 Cognitive function.

Comparison 1 CGA versus usual care, Outcome 10 Length of stay.
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Analysis 1.10

Comparison 1 CGA versus usual care, Outcome 10 Length of stay.

Comparison 1 CGA versus usual care, Outcome 11 Re‐admissions.
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Analysis 1.11

Comparison 1 CGA versus usual care, Outcome 11 Re‐admissions.

Summary of findings for the main comparison. Comprehensive geriatric assessment (CGA) versus admission to hospital without CGA

Comprehensive geriatric assessment (CGA) versus admission to hospital without CGA

Patient or population: older adults admitted to hospital
Setting: unplanned hospital admissions in 9 largely high‐income countries
Intervention: CGA
Comparison: usual care

Outcomes

Anticipated absolute effects* (95% CI)

Relative effect
(95% CI)

No. of participants
(studies)

Certainty of the evidence
(GRADE)

Risk with usual care

Risk with CGA

Living at home (end of follow‐up 3 to 12 months)

Study population

RR 1.06
(1.01 to 1.10)

6799
(16 RTs)

⊕⊕⊕⊕
HIGH

561 per 1000

595 per 1000
(567 to 617)

Mortality (end of follow‐up 3 to 12 months)

Study population

RR 1.00
(0.93 to 1.07)

10,023
(21 RTs)

⊕⊕⊕⊕
HIGH

230 per 1000

230 per 1000
(214 to 247)

Admission to a nursing home (end of follow‐up
3 to 12 months)

Study population

RR 0.80
(0.72 to 0.89)

6285
(14 RTs)

⊕⊕⊕⊕
HIGH

186 per 1000

151 per 1000
(136 to 169)

Dependence

Study population

RR 0.97
(0.89 to 1.04)

6551
(14 RTs)

⊕⊕⊕⊕
HIGH

291 per 1000

282 per 1000
(259 to 302)

Cognitive function

Standardised mean difference ranged from ‐0.22 to 0.35.

3534
(5 RTs)

⊕⊕⊝⊝
LOWa,

Length of stay

Not estimable

Mean length of stay in the control group ranged from 1.8 days to 42.8 days.

Mean length of stay in the intervention group ranged from 1.63 days to 40.7 days.

5303
(17 RTs)

⊕⊕⊝⊝
LOWa,

Cost and cost‐effectiveness

Healthcare costs per participant in the CGA group were on average GBP 234 (95% CI GBP ‐144 to GBP 605) higher than in the usual care group (17 trials); CGA led to 0.012 (95% CI ‐0.024 to 0.048) more QALYs (3 trials), 0.037 (95% CI 0.001 to 0.073) more LYs (4 trials), and 0.019 (95% CI ‐0.019 to 0.155) more LYLAH (2 trials) per participant. Costs per QALYs gained was GBP 19,802, per LY gained was GBP 6305, and per LYLAH gained was GBP 12,568. CGA was more costly in 89% of 10,000 generated ICERs and led to QALY gains in 66% of cases, LY gains in 87% of cases, and LYLAH gains in 74% of cases. The probability that CGA would be cost‐effective at a GBP 20,000 ceiling ratio for QALY, LY, and LYLAH was 0.50, 0.89, and 0.47, respectively.

5303 (17 RTs)

⊕⊕⊝⊝

LOWa,

*The risk in the intervention group (and its 95% confidence interval) is based on assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).

CGA: comprehensive geriatric assessment: CI: confidence interval; ICER: incremental cost‐effectiveness ratio; LY: life year; LYLAH: life year living at home; OR: odds ratio; QALY: quality‐adjusted life year; RR: risk ratio; RT: randomised trial.

GRADE Working Group grades of evidence
High certainty: We are very confident that the true effect lies close to the estimate of effect.
Moderate certainty: We are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of effect but may be substantially different.
Low certainty: Our confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of effect.
Very low certainty: We have very little confidence in the effect estimate: The true effect is likely to be substantially different from the estimate of effect.

aThe evidence was downgraded due to imprecision and substantial heterogeneity

Figures and Tables -
Summary of findings for the main comparison. Comprehensive geriatric assessment (CGA) versus admission to hospital without CGA
Table 1. Parameters used in the decision model for the economic evaluation

Value

Standard error

Distribution

Alpha

Beta

Source

Probabilities

Risk ratio: living at home (end of follow‐up on ward)

1.070

0.92

Gamma

1.34

0.80

Main meta‐analysis

Risk ratio: living at home (end of follow‐up on ward and by team)

1.060

1.20

Gamma

0.78

1.36

Main meta‐analysis

Risk ratio: admitted to a nursing home (end of follow‐up on ward)

0.780

0.06

Gamma

173.99

0.00

Main meta‐analysis

Risk ratio: admitted to a nursing home (end of follow‐up on ward and by team)

0.810

0.06

Gamma

207.55

0.00

Main meta‐analysis

Resource utilisation

Mean difference in length of stay in hospital

0.029

0.22

Normal

Main meta‐analysis

Mean length of stay in a nursing home after discharge ‐ CGA

49.91

8.12

Gamma

38

1

Saltvedt

Mean length of stay in a nursing home after discharge ‐ UC

40.87

8.44

Gamma

23

2

Saltvedt

Health outcomes

Mean difference in LYLAH

0.009

0.022

Normal

Meta‐analysis based on IPD (Edmans, Saltvedt)

Mean difference in QALY

0.012

0.019

Normal

Meta‐analysis based on IPD (Edmans, Kircher, Saltvedt)

Mean difference in QALY (severe patients)

0.018

0.024

Normal

Meta‐analysis based on IPD (Goldberg, Somme)

Mean difference in time to death

13.061

6.664

Normal

Meta‐analysis based on IPD (Edmans, Goldberg, Kircher, Saltvedt)

Unit costs

Cost of bed day in hospital

874

Weighted average of elective and non‐elective hospitalisation based on national reference costs 2013/2014

Cost of nursing home day

77

Personal social services: Expenditure and unit costs, England ‐ 2013‐14, final release: Unit costs by CASSR

Cost of CGA per patient

208

8.929

Gamma

543

0

Tanajewski et al. 2015, AMIGOS trial

Mean difference in QALY was based on mapping the IPD for the Barthel from three trials (Edmans 2013; Kircher 2007; Saltvedt 2002).

In Saltvedt 2002, baseline Barthel Index was assessed 1.7 days after inclusion in the control group and at 3.5 days in the intervention group.

In Saltvedt 2002, baseline Barthel Index was self‐reported in the control group and was performance‐based in the intervention group.

Figures and Tables -
Table 1. Parameters used in the decision model for the economic evaluation
Table 2. Cost data reported by trials

Cost analysis

Trial

Year

Country

Treatment arm

Costs

Comments

Cohen

2002

USA

(US Dollars)

Geriatric Unit + Usual Care Outpatient

36,592 (1844 SD)

Direct cost comparison separated into institutional costs and costs estimated for nursing home admissions based on standardised HMO rates

Usual Care Inpatient + Usual Care Outpatient (Control)

38,624 (2037)

Geriatric Unit + Geriatric Outpatient

35,935 (1829)

Usual Care Inpatient + Geriatric Outpatient (Control)

35,951 (1827)

Collard

1985

USA

(US Dollars)

Choate (Experimental)

4015.17 (SE 0.03)

Direct cost comparison (hospital costs only)

Choate (Control)

4545.13 (SE 0.03)

Symmes (Experimental)

3591.42 (SE 0.03)

Symmes (Control)

4155.54 (SE 0.02)

Fretwell

1990

USA

(US Dollars)

Experiment

3148 (7210 SD)

Direct cost comparison (hospital costs only)

Control

4163 (18,406)

Applegate

1990

USA

(US Dollars)

Geriatric Unit (Rehab Diagnosis)

32,978 (35,130 SD)

Health and social care costs up to 1 year after randomisation

Geriatric Unit (Medical/Surgical Diagnosis)

25,846 (29,628)

Usual Care (Rehab/Diagnosis)

18,409 (16,555)

Usual Care (Medical/Surgical Diagnosis)

15,248 (13,152)

Asplund

2000

Sweden

(Swedish Kronar)

Experiment

10,800 (9300 ‐ 12,300 IQR)

Direct cost comparison (hospital costs only)

Control

12,800 (11,500 ‐ 14,100)

Counsell

2000

USA

(US Dollars)

Experiment

5640

Included in experimental group costs are costs of renovation of geriatric unit

Control

5754

Hogan

1987

Canada

(Canadian Dollars)

Experiment

98.36

Monthly costings for physician services only

Control

77.68

Landefeld

1995

USA

(US Dollars)

Experiment

6608

Direct cost comparison (hospital costs only)

Control

7240

Nikolaus

1999

Germany

(Deutschmark)

Geriatric Unit + ESD

3,365,000 (1,922,400)

Costs for hospital care and nursing homes (estimated as costs per 100 people per year)

Geriatric Unit only

3,983,000 (2,276,000)

Control

4,145,000

Rubenstein

1984

USA

(US Dollars)

Experiment

22,597

Costs per year survived including hospital and nursing home costs

Control

27,826

Naughton

1994

USA

(US Dollars)

Experiment

4525 (5087 SD)

Direct cost comparison (hospital costs only)

Control

6474 (7000)

White

1994

USA

(US Dollars)

Experiment

23,906

Direct cost comparison (hospital costs only)

Control

45,189

Barnes

2012

USA

(US Dollars)

Experiment

9477

Direct cost comparison (hospital costs only)

Control

10,451

Edmans

2013

UK

(UK Pounds)

Experiment

4475 (95% CI 3901 to 5141)

Care cost + intervention cost up to 90 days after hospital discharge

Control

4,057 (95% CI 3367 to 4882)

Wald

2011

USA

(US Dollars)

Experiment

24,617 (15,828 SD)

Direct cost comparison (hospital costs only)

Control

21,488 (13,407 SD)

Owing to variation in time periods (1985 to 2013) and resources costed, these data are not used in the analysis of costs.

Figures and Tables -
Table 2. Cost data reported by trials
Table 3. Results from main cost‐effectiveness analysis

Incremental healthcare costs (95% CI)

Incremental outcomes (95% CI)

ICER

Probability of CGA being more costly

Probability of CGA being more costly and more effective

Probability of CGA being cost‐effective at GBP 20,000 ceiling ratio

Cost‐utility analysis (outcome is QALY)

GBP 234

(‐144 to 605)

0.012

(‐0.024 to 0.048)

GBP 19,802

0.89

0.66

0.50

Cost‐effectiveness analysis (outcome is LY)

GBP 234

(‐144 to 605)

0.037

(0.001 to 0.073)

GBP 6305

0.89

0.87

0.89

Cost‐effectiveness analysis (outcome is LYLAH)

GBP 234

(‐144 to 605)

0.019

(‐0.019 to 0.155)

GBP 12,568

0.89

0.74

0.47

Figures and Tables -
Table 3. Results from main cost‐effectiveness analysis
Table 4. Outcome living at home: FE meta‐analysis (intervention vs control) adjusting for baseline Barthel measures (binary), age, and sex

Study

OR

95% lower

95% upper

% weight

Edmans

0.711

0.376

1.346

16.39

Goldberg

1.147

0.821

1.603

59.66

Kircher

0.733

0.359

1.496

13.11

Somme

0.339

0.018

6.396

0.77

Saltvedt

0.79

0.35

1.783

10.07

Overall effect

0.954

0.737

1.236

100

In Saltvedt 2002, baseline Barthel Index was assessed 1.7 days after inclusion in the control group and at 3.5 days in the intervention group
Also in this trial, baseline Barthel Index was self‐reported in the control group and was performance‐based in the intervention group. This trial showed an impact on mortality and living at home at 3 to 6 months. For consistency, however, data from 12‐month outcomes are provided

Figures and Tables -
Table 4. Outcome living at home: FE meta‐analysis (intervention vs control) adjusting for baseline Barthel measures (binary), age, and sex
Table 5. Outcome death: FE meta‐analysis (intervention vs control) adjusting for baseline Barthel measures (binary), age, and sex

Study

OR

95% lower

95% upper

% weight

Edmans

0.965

0.412

2.259

10.49

Goldberg

0.915

0.621

1.349

50.41

Kircher

0.852

0.379

1.916

11.55

Somme

0.784

0.231

2.664

5.08

Saltvedt

0.989

0.553

1.769

22.47

Overall effect

0.922

0.7

1.214

100

In Saltvedt 2002, baseline Barthel Index was assessed 1.7 days after inclusion in the control group and at 3.5 days in the intervention group

Also in this trial, baseline Barthel Index was self‐reported in the control group and was performance‐based in the intervention group. This trial showed an impact on mortality and living at home at 3 to 6 months. For consistency, however, data from 12‐month outcomes are provided

Figures and Tables -
Table 5. Outcome death: FE meta‐analysis (intervention vs control) adjusting for baseline Barthel measures (binary), age, and sex
Table 6. Outcome time to event (death): FE meta‐analysis (intervention vs control) adjusted for age, sex, and Barthel baseline (binary)

Hazard ratio

SE

95% CI lower

95% CI upper

P value

Treatment

0.883

0.091

0.723

1.080

0.227

Age

0.996

0.008

0.980

1.012

0.597

Sex

0.955

0.122

0.743

1.227

0.718

Barthel BL

0.648

0.117

0.455

0.922

0.016

In Saltvedt 2002, baseline Barthel Index was assessed 1.7 days after inclusion in the control group and at 3.5 days in the intervention group

Also in this trial, baseline Barthel Index was self‐reported in the control group and was performance‐based in the intervention group

Figures and Tables -
Table 6. Outcome time to event (death): FE meta‐analysis (intervention vs control) adjusted for age, sex, and Barthel baseline (binary)
Comparison 1. CGA versus usual care

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

1 Living at home (discharge) Show forest plot

11

4346

Risk Ratio (M‐H, Fixed, 95% CI)

1.05 [1.01, 1.10]

1.1 Ward

8

3853

Risk Ratio (M‐H, Fixed, 95% CI)

1.06 [1.02, 1.11]

1.2 Team

3

493

Risk Ratio (M‐H, Fixed, 95% CI)

0.95 [0.85, 1.07]

2 Living at home (end of follow‐up 3 to 12 months) Show forest plot

16

6799

Risk Ratio (M‐H, Fixed, 95% CI)

1.06 [1.01, 1.10]

2.1 Ward

12

5705

Risk Ratio (M‐H, Fixed, 95% CI)

1.07 [1.03, 1.12]

2.2 Team

4

1094

Risk Ratio (M‐H, Fixed, 95% CI)

0.97 [0.88, 1.07]

3 Mortality (discharge) Show forest plot

11

4346

Risk Ratio (M‐H, Fixed, 95% CI)

1.04 [0.82, 1.32]

3.1 Ward

8

3853

Risk Ratio (M‐H, Fixed, 95% CI)

1.00 [0.77, 1.29]

3.2 Team

3

493

Risk Ratio (M‐H, Fixed, 95% CI)

1.29 [0.72, 2.31]

4 Mortality (end of follow‐up 3 to 12 months) Show forest plot

21

10023

Risk Ratio (M‐H, Fixed, 95% CI)

1.00 [0.93, 1.07]

4.1 Ward

15

6444

Risk Ratio (M‐H, Fixed, 95% CI)

0.99 [0.91, 1.09]

4.2 Team

6

3579

Risk Ratio (M‐H, Fixed, 95% CI)

1.01 [0.90, 1.14]

5 Admission to a nursing home (discharge) Show forest plot

12

4459

Risk Ratio (M‐H, Fixed, 95% CI)

0.89 [0.81, 0.98]

5.1 Ward

8

3853

Risk Ratio (M‐H, Fixed, 95% CI)

0.87 [0.79, 0.96]

5.2 Team

4

606

Risk Ratio (M‐H, Fixed, 95% CI)

1.05 [0.80, 1.39]

6 Admission to a nursing home (end of follow‐up 3 to 12 months) Show forest plot

14

6285

Risk Ratio (M‐H, Fixed, 95% CI)

0.80 [0.72, 0.89]

6.1 Ward

11

5512

Risk Ratio (M‐H, Fixed, 95% CI)

0.77 [0.69, 0.86]

6.2 Team

3

773

Risk Ratio (M‐H, Fixed, 95% CI)

1.44 [0.91, 2.30]

7 Dependence Show forest plot

14

6551

Risk Ratio (M‐H, Fixed, 95% CI)

0.97 [0.89, 1.04]

7.1 ADL

9

2420

Risk Ratio (M‐H, Fixed, 95% CI)

1.06 [0.94, 1.19]

7.2 Decline in ADL

5

4131

Risk Ratio (M‐H, Fixed, 95% CI)

0.91 [0.83, 1.01]

8 Activities of daily living Show forest plot

7

1445

Std. Mean Difference (IV, Fixed, 95% CI)

0.04 [‐0.06, 0.15]

8.1 Ward

5

1116

Std. Mean Difference (IV, Fixed, 95% CI)

0.08 [‐0.04, 0.20]

8.2 Team

2

329

Std. Mean Difference (IV, Fixed, 95% CI)

‐0.08 [‐0.30, 0.14]

9 Cognitive function Show forest plot

5

Std. Mean Difference (IV, Fixed, 95% CI)

Totals not selected

9.1 Ward

2

Std. Mean Difference (IV, Fixed, 95% CI)

0.0 [0.0, 0.0]

9.2 Team

3

Std. Mean Difference (IV, Fixed, 95% CI)

0.0 [0.0, 0.0]

10 Length of stay Show forest plot

17

Mean Difference (IV, Fixed, 95% CI)

Totals not selected

10.1 Ward

11

Mean Difference (IV, Fixed, 95% CI)

0.0 [0.0, 0.0]

10.2 Team

6

Mean Difference (IV, Fixed, 95% CI)

0.0 [0.0, 0.0]

11 Re‐admissions Show forest plot

13

6698

Risk Ratio (M‐H, Fixed, 95% CI)

1.02 [0.94, 1.11]

11.1 Ward

11

5992

Risk Ratio (M‐H, Fixed, 95% CI)

1.01 [0.92, 1.11]

11.2 Team

2

706

Risk Ratio (M‐H, Fixed, 95% CI)

1.07 [0.90, 1.28]

Figures and Tables -
Comparison 1. CGA versus usual care