Comprehensive geriatric assessment for older adults admitted to hospital

Graham Ellis, Mike Gardner, Apostolos Tsiachristas, Peter Langhorne, Orlaith Burke, Rowan H Harwood, Simon P Conroy, Tilo Kircher, Dominique Somme, Ingvild Saltvedt, Heidi Wald, Desmond O'Neill, David Robinson, Sasha Shepperd, Graham Ellis, Mike Gardner, Apostolos Tsiachristas, Peter Langhorne, Orlaith Burke, Rowan H Harwood, Simon P Conroy, Tilo Kircher, Dominique Somme, Ingvild Saltvedt, Heidi Wald, Desmond O'Neill, David Robinson, Sasha Shepperd

Abstract

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.

Conflict of interest statement

Graham Ellis: none known.

Mike Gardner: none known.

Apostolos Tsiachristas: none known.

Peter Langhorne: none known.

Orlaith Burke: none known.

Rowan H Harwood: trialist.

Simon P Conroy: trialist.

Tilo Kircher: trialist.

Dominique Somme: trialist.

Ingvild Saltvedt: trialist.

Heidi Wald: trialist.

Desmond O'Neill: none known.

David Robinson: none known.

Sasha Shepperd: none known.

Figures

Figure 1
Figure 1
PRISMA flow diagram.
Figure 2
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.
Figure 3
Figure 3
Key components of CGA reported by trialists. ∙ Components critical to success
Figure 4
Figure 4
Components of in‐hospital control group: processes of care and staff profiles. • Present or carried out
Figure 5
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.
Figure 6
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).
Figure 7
Figure 7
Probability of CGA being cost‐effective.
Figure 8
Figure 8
Cost‐effectiveness plane with ICERs expressed as cost per QALY gained.
Figure 9
Figure 9
Cost‐effectiveness plane with ICER expressed as cost per LY gained.
Figure 10
Figure 10
Cost‐effectiveness plane with ICERs expressed as cost per LYLAH gained.
Analysis 1.1
Analysis 1.1
Comparison 1 CGA versus usual care, Outcome 1 Living at home (discharge).
Analysis 1.2
Analysis 1.2
Comparison 1 CGA versus usual care, Outcome 2 Living at home (end of follow‐up 3 to 12 months).
Analysis 1.3
Analysis 1.3
Comparison 1 CGA versus usual care, Outcome 3 Mortality (discharge).
Analysis 1.4
Analysis 1.4
Comparison 1 CGA versus usual care, Outcome 4 Mortality (end of follow‐up 3 to 12 months).
Analysis 1.5
Analysis 1.5
Comparison 1 CGA versus usual care, Outcome 5 Admission to a nursing home (discharge).
Analysis 1.6
Analysis 1.6
Comparison 1 CGA versus usual care, Outcome 6 Admission to a nursing home (end of follow‐up 3 to 12 months).
Analysis 1.7
Analysis 1.7
Comparison 1 CGA versus usual care, Outcome 7 Dependence.
Analysis 1.8
Analysis 1.8
Comparison 1 CGA versus usual care, Outcome 8 Activities of daily living.
Analysis 1.9
Analysis 1.9
Comparison 1 CGA versus usual care, Outcome 9 Cognitive function.
Analysis 1.10
Analysis 1.10
Comparison 1 CGA versus usual care, Outcome 10 Length of stay.
Analysis 1.11
Analysis 1.11
Comparison 1 CGA versus usual care, Outcome 11 Re‐admissions.

Source: PubMed

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