Abstract
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:
To assess the effects of algorithm‐based pain management interventions to reduce pain and challenging behaviour in people with dementia living in nursing homes.
To describe the components of the interventions and the content of the algorithms.
Background
Description of the condition
Dementia is a syndrome characterised by progressive cognitive and functional decline. About 24 million people are affected by dementia worldwide, among them approximately six million people in Europe, and the number is expected to rise due to demographic change (Prince 2013; Prince 2015; Wittchen 2011). In long‐term care facilities, the prevalence of dementia ranges from 50% to 80% (Hoffmann 2014a; Seitz 2010; Stewart 2014).
People with dementia in nursing homes often experience acute or chronic pain (Rajkumar 2017; Takai 2010). An epidemiological study in seven European countries and one non‐European country (Services and Health for the Elderly in Long Term Care (SHELTER) Study) found an overall prevalence of pain in nursing home residents of 48.4% (1900 residents with pain out of 3926 residents) and approximately 46% of the sample had a diagnosis of dementia. Chronic pain was found in 12% of all residents with pain (Lukas 2013a).
Although guidelines for caring for people with dementia recommend adequate pain treatment (e.g. NICE 2016; RNAO 2013), people with dementia are often under‐treated for pain (Hemmingsson 2018). In the SHELTER study, 24% of the total sample of residents experiencing pain did not receive any pain medication; however, a great variation between different countries was found, ranging from 3.4% of residents without any pain medication in Finland to 69.5% in Italy (Lukas 2013b). Other studies reported differences in pain treatment between people with comparable diagnoses with and without cognitive impairment (Monroe 2014; Tan 2015).
Assessing pain in people with cognitive impairment is challenging. Self‐report is considered to be the gold standard for assessing pain since pain is a subjective experience (RNAO 2013). People with mild and moderate dementia are often able to express their pain verbally or by using simple visual or numerical pain intensity scales. In people with severe dementia, the use of self‐rating scales is often not feasible due to the cognitive decline and the loss of communication skills (Lichtner 2014). Proxy‐rating instruments have been developed to assess pain based on observation, for example by nursing staff. However, the level of pain cannot be assessed accurately by proxy rating and there is limited evidence about the psychometric properties of these instruments (Lichtner 2014).
Untreated acute and chronic pain might be one reason for challenging behaviour (Rajkumar 2017; van Dalen‐Kok 2015). Challenging behaviour occurs commonly in dementia (Stewart 2014); it often has a significant impact, leading to stress and reduced quality of life for both people with dementia and their caregivers, and increasing the burden of care (Feast 2016; Hurt 2008).
Treatment of pain might be one approach to reduce challenging behaviour and is recommended in guidelines for the management of challenging behaviour (e.g. NICE 2016).
Description of the intervention
There are various non‐pharmacological and pharmacological approaches to the treatment of acute and chronic pain. However, pain treatment is complex in people with cognitive impairment due to the challenges of assessing pain and evaluating the success of the treatment. Algorithm‐based pain management aims to overcome these challenges by offering potentially effective pain treatments in a structured, stepwise intervention, with each step defined by a different treatment and dose (or dose range), and by criteria for assessing the success of the particular treatment.
Algorithms often comprise both non‐pharmacological and pharmacological pain treatments. If non‐pharmacological pain treatments are included, then they are often used prior to pharmacological treatments — that is, in earlier steps of the algorithm.
How the intervention might work
Untreated acute and chronic pain has been identified as one possible reason for challenging behaviour in dementia (Rajkumar 2017; van Dalen‐Kok 2015). Other possible causes are brain changes and unmet needs (Cohen‐Mansfield 2010). Discriminating between challenging behaviour caused by pain and by other reasons is often difficult. A structured pain management plan, for example based on an algorithm, could be one approach for reducing pain and challenging behaviour (Husebo 2011b). Algorithm‐based interventions represent a pragmatic approach, addressing the complexity of pain management in people with dementia by using a stepwise approach with different treatments that can effectively reduce pain.
Algorithms comprise several steps and for each step a specific pain treatment is defined. Some algorithms include both non‐pharmacological and pharmacological treatments (e.g. Pieper 2016); and others consist of pharmacological treatments (i.e. pain medications) only (e.g. Husebo 2011b). Guidelines recommend non‐pharmacological approaches as the first choice for managing pain in people with dementia (NICE 2016; RNAO 2013). Pharmacological treatment steps often start with non‐opioid analgesics, such as non‐steroidal anti‐inflammatory drugs (NSAID), followed by opioids, either alone or in combination with non‐opioids to maximise the efficacy of the treatment and to minimise adverse effects of the medication (RNAO 2013). However, algorithms might also specify different sequences of pain medications. In the study by Husebo 2011b, the pain medications defined for the different steps of the algorithm followed the recommendations of the American Geriatrics Society (American Geriatric Society Panel 1998).
To implement an algorithm, the treatment defined in the first step is applied to the person with dementia. The effect of the treatment is regularly evaluated, for example two or three times per day. If there is no treatment effect, the treatment defined in the next step is applied and evaluated. This procedure continues until the treatment is successful or until the last step of the algorithm is applied.
Given the high prevalence of undetected or insufficiently treated pain in nursing homes, using such a pragmatic approach in residents with challenging behaviour might reduce both pain and challenging behaviour.
Why it is important to do this review
Two systematic reviews have investigated the effects of pain treatment on challenging behaviour: Husebo 2011a included three studies with several methodological limitations and found inconsistent effects; Pieper 2013 included 16 publications evaluating a wide range of interventions and using different study designs (experimental and observational studies). Most of the included studies did not distinguish between acute and chronic pain. The results of the systematic review by Pieper 2013 suggest that pain treatment might be effective in reducing challenging behaviour, but tailored pharmacological approaches seem to be more effective than fixed treatment regimes. Due to the methodological and clinical heterogeneity of the studies and interventions, the internal validity and the generalisability of the results are limited. Both reviews recommended further studies using rigorous study designs (Husebo 2011a; Pieper 2013).
Since these reviews were published, several randomised controlled trials (RCTs) investigating algorithm‐based interventions have been conducted (e.g. Ersek 2016, Husebo 2011b and Pieper 2016). A systematic review is warranted to describe these interventions and to summarise their effects on acute and chronic pain and challenging behaviour.
Algorithm‐based interventions are complex, including different components and treatment options (Craig 2008). To assess the effects of complex interventions, a description of the interventions' characteristics, i.e. aim and theoretical basis, and their components is required to ensure the comparability of the interventions and to draw clear conclusions for clinical practice (Burford 2013). In this review, the description of the interventions' characteristics will be guided by the Criteria for Reporting the Development and Evaluation of Complex Interventions in healthcare 2 (CReDECI 2; Möhler 2015) and the Template for Intervention Description and Replication (TiDieR) guideline (Hoffmann 2014b). The effects of complex interventions are also influenced by the fidelity of implementation and process‐related outcomes (e.g. degree of implementation and response of the target group). Therefore, we will also include implementation‐related information in this review.
The results of this review will be highly relevant for clinical practice and will help to improve the quality of care and pain treatment in people with dementia. Also, the systematic review may reveal knowledge gaps which can inform further research in the field.
Objectives
To assess the effects of algorithm‐based pain management interventions to reduce pain and challenging behaviour in people with dementia living in nursing homes.
To describe the components of the interventions and the content of the algorithms.
Methods
Criteria for considering studies for this review
Types of studies
We will include all individually or cluster‐randomised controlled trials investigating the effects of algorithm‐based pain management interventions for reducing pain and challenging behaviour in people with dementia. There will be no language restrictions.
Types of participants
We will include people with dementia or cognitive impairment living in long‐term care facilities. There will be no restrictions regarding the stage of dementia or cognitive impairment. We will also include studies including participants without dementia if the proportion of these participants is less than 20%.
Types of interventions
We will include all interventions offering pain treatment based on an algorithm, that is a set of unambiguous instructions explaining how to apply a pre‐determined, stepped approach to pain management. All interventions must include the following elements.
Treatment algorithm: a stepwise treatment plan that comprises at least two treatment steps to reduce pain. Algorithms may include different pharmacological and non‐pharmacological treatments, i.e. different types of pain medications, acupuncture or other non‐pharmacological interventions, to reduce pain. For all treatments, dose or dose range and frequency of application or delivery has to be defined.
Criteria to assess the success of each treatment step, including an assessment method and a predefined response threshold.
We will include interventions using terms other than 'algorithm' — such as 'decision tree' or 'clinical pathway' for example — if they meet the above‐defined criteria.
We will exclude interventions offering only one specific non‐pharmacological or pharmacological treatment.
Comparison: control groups may receive usual care (standard pain assessment and treatment in the participants' care setting) or an active control intervention (i.e. other non‐pharmacological or pharmacological treatments for reducing pain or challenging behaviour not based on an algorithm).
Types of outcome measures
Primary outcomes
Primary outcomes will be:
-
pain‐related outcomes;
number of participants with pain;
number of participants with at least 50% improvement in pain intensity;
mean change in pain intensity;
challenging behaviour;
mortality;
number of people with serious adverse events.
To assess the presence of pain, we will include both self‐ and proxy‐rating scales. We will prefer self‐rating instruments, for example a verbal rating scale (VRS), but we also expect to find studies using proxy‐rating instruments since people with dementia living in long‐term care institutions often have moderate‐to‐severe cognitive impairment and may not be able to use self‐assessment instruments. If results from both types of instrument are available, we will use the values from self‐assessment instruments in the analyses. For pain intensity we expect only self‐rating instruments to be used, since a valid assessment of pain intensity based on proxy ratings is not feasible.
Challenging behaviour could include agitation or, if no agitation scale is used, overall behavioural and psychological symptoms. It may be assessed with different validated instruments, for example the Cohen‐Mansfield Agitation Inventory (CMAI) or the Neuropsychiatric Inventory (NPI) (Cohen‐Mansfield 1989; Cummings 1994).
We expect a variety of instruments to be used and do not define a minimal clinically important difference for pain or challenging behaviour (see Data synthesis).
We will define serious adverse events as life‐threatening events or events requiring hospitalisation.
Secondary outcomes
Secondary outcomes will be:
quality of life, assessed by e.g. EuroQol (EQ‐5D) or DEMQOL;
performance of activities of daily living, including mobility, assessed by appropriate validated instruments;
depression, assessed by appropriate validated instruments, e.g. the Cornell Scale for Depression in Dementia (CSDD);
number of people experiencing adverse events, e.g. sedation, constipation, nausea;
effect on the caregivers, including caregivers' distress (assessed by e.g. Neuropsychiatric Inventory Caregiver Distress Scale (NPI‐D)), burden (assessed by e.g. the Zarit Burden Interview) or quality of life (assessed by e.g. EuroQol (EQ‐5D));
intervention costs.
Search methods for identification of studies
Electronic searches
We will search ALOIS (www.medicine.ox.ac.uk/alois), which is the Cochrane Dementia and Cognitive Improvement Group’s (CDCIG) specialised register.
ALOIS is maintained by the Information Specialists for the CDCIG, and contains studies that fall within the areas of dementia prevention, dementia treatment and management, and cognitive enhancement in healthy elderly populations. The studies are identified through:
searching a number of major healthcare databases — MEDLINE, Embase, CINAHL and PsycINFO;
searching a number of trial registers — ClinicalTrials.gov and the World Health Organization’s International Clinical Trials Register Platform (ICTRP) which covers ISRCTN; the Chinese Clinical Trials Register; the German Clinical Trials Register; the Iranian Registry of Clinical Trials; and the Netherlands National Trials Register, plus others;
searching the Cochrane Library's Central Register of Controlled Trials (CENTRAL);
searching grey literature sources: ISI Web of Science Core Collection.
To view a list of all sources searched for ALOIS, please follow this link to the ALOIS web site (www.medicine.ox.ac.uk/alois).
Details of the search strategies run in healthcare bibliographic databases and used for the retrieval of reports of dementia, cognitive improvement and cognitive enhancement trials can be viewed on the Cochrane Dementia and Cognitive Improvement Group’s website: dementia.cochrane.org/searches
We will run additional searches in MEDLINE, Embase, PsycINFO, CINAHL, LILACS, ClinicalTrials.gov and the WHO Portal/ICTRP to ensure that the searches for this review are as comprehensive and as up to date as possible. The search strategy that we will use for the retrieval of reports of trials from MEDLINE (via the Ovid SP platform) can be seen in Appendix 1.
Searching other resources
We will check the reference lists of included studies and relevant reviews and we will perform forward citation tracking for all included studies (using Google Scholar). Additionally, we will contact study authors and experts in the field to identify unpublished and additional ongoing studies.
Data collection and analysis
Selection of studies
We will use Covidence for study selection (Covidence 2017). After excluding any duplicates, two review authors will independently screen all titles/abstracts against the inclusion criteria to identify potentially relevant studies. In a second step, two review authors will independently screen all potentially relevant titles in full text for inclusion or exclusion. We will use a language translation service for relevant articles not available in English or German. We will resolve disagreement by discussion or, if necessary, by consulting a third review author.
Data extraction and management
Two reviewers will extract all relevant data independently using Covidence (Covidence 2017). We will check extracted data for accuracy. In case of disagreement we will consult a third review author to reach consensus.
For each study, we will extract the following data: information about prospective trial registration or a published study protocol or both; study design; characteristics of participants; baseline data; length of follow‐up; outcome measures; study results; and adverse effects. For cluster‐randomised trials, we will also extract estimates of the intra‐cluster correlation coefficient (ICC) if possible.
For each intervention, we will extract the following data: method of pain assessment, characteristics of the algorithm (number of steps, treatment options defined in each step), procedures to evaluate the treatment effect and rules to switch to the next step of the algorithm.
If any of the above information is missing, then we will seek it from study authors.
If reported, we will also extract information about the fidelity with which the intervention was implemented and process‐related data, for example adherence to the algorithm and deviations from the protocol, and barriers to — or facilitators of — the use of the algorithm.
Assessment of risk of bias in included studies
Two authors will independently assess the methodological quality of the included studies in order to identify any potential sources of bias, as described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2017). ‘Risk of bias’ assessment will address the following domains: sequence generation, allocation concealment, blinding, incomplete outcome data, selective outcome reporting, and other sources of bias. We will determine study validity by categorising individual studies as being at low, high or unclear risk of bias. We will resolve any disagreements through reaching a consensus with a third review author.
We will rate the quality of evidence using the criteria proposed by the GRADE working group (Guyatt 2011). Two review authors will independently perform the GRADE rating and will resolve disagreement by discussion or, if necessary, by consulting a third review author.
Measures of treatment effect
For dichotomous data, we will calculate risk ratios (RR) with 95% confidence intervals (CI). For continuous outcome data assessed with the same rating scale, we will calculate the mean difference with 95% CI. Where different rating scales are used, we will calculate the standardised mean difference (SMD) with 95% CI. We will perform statistical analysis using Review Manager 5 (RevMan 5) (Review Manager 2014).
We will not define a minimal clinically important difference for pain or challenging behaviour because we could not identify generally accepted thresholds for clinical importance in the literature.
We will assess the implementation fidelity in each study as a percentage of the extent to which the participants received the treatments defined in the algorithm. Fidelity will be categorised as adequate in studies with a rate of at least 95% treatment fidelity (Lavallée 2017).
Unit of analysis issues
If we include cluster‐randomised trials, we will check for unit of analysis issues. If clustering has not been sufficiently addressed in the analysis of primary studies (e.g. randomisation on cluster level, but analyses on patient level without adjusting for clusters), we will recalculate the effect estimates using the studies' intracluster correlation coefficient (ICC). If ICC values are not available, we will obtain an external estimate of the ICC from similar studies to recalculate the effect estimates, as described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011).
If two or more experimental intervention groups from a single study are included in the same meta‐analysis then we will split the sample size for the control group, as described in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011).
Dealing with missing data
In case of missing data in the publications, we will contact study authors to obtain missing data. We will check for data imputation in the included studies. If data imputation was used in any study, we will report the imputation methods used. We will use data from intention‐to‐treat (ITT) analyses if available. If no true ITT data are available, then we will use data on study completers (see Sensitivity analysis).
Assessment of heterogeneity
We will assess clinical heterogeneity of interventions, i.e. differences in the algorithms used (see Types of interventions). In case of any clinical differences, two authors will discuss whether the interventions are sufficiently similar in clinical and methodological characteristics to be included in a meta‐analysis. To assess statistical heterogeneity, we will calculate the I² and Chi² statistics using RevMan 5 (Review Manager 2014).
Assessment of reporting biases
We will assess reporting bias by comparing information about planned trials (e.g. identified in study registers and from conference abstracts) against the publications of included studies. If we include at least 10 studies in a meta‐analysis, we will explore reporting bias with a funnel plot.
Data synthesis
We will perform meta‐analyses using a random‐effects model if included studies are sufficiently similar in terms of participants, interventions and outcomes. We will perform meta‐analyses using the generic inverse variance method in RevMan 5 (Review Manager 2014).
In case of pronounced clinical heterogeneity of the interventions, for example algorithms including several steps with non‐pharmacological treatments and only one or two steps with pain medications (e.g. Kovach 2006) versus algorithms including only pharmacological treatments (Husebo 2011b), we will group the interventions and perform separate meta‐analyses for each group.
If we include studies with active (standardised) control interventions and usual care control groups, we will perform separate comparisons.
We will address risk of bias in the synthesis by performing additional analyses excluding studies with high risk of bias (see Sensitivity analysis).
If no meta‐analysis is possible, we will conduct a narrative analysis of the study results (Popay 2006).
Subgroup analysis and investigation of heterogeneity
We will perform subgroup analysis for studies with adequate and inadequate treatment fidelity, as defined above. If possible, we will also perform subgroup analysis by stage of dementia, for example studies mainly including residents in early stages of dementia versus studies mainly including residents with severe dementia.
In case of substantial statistical heterogeneity (I² > 50%), we will explore the potential reasons for heterogeneity. We will inspect forest plots for studies with non‐overlapping confidence intervals, consider potential explanations and perform additional subgroup or sensitivity analyses if necessary.
Sensitivity analysis
We will perform sensitivity analysis excluding studies with high risk of bias, i.e. studies with a high risk of bias rating in at least two domains of the Cochrane ‘Risk of bias’ tool (Higgins 2017). If feasible, we will use sensitivity analyses to assess the impact of different methods of dealing with missing data, for example ITT analysis with data imputation versus data on completers only.
‘Summary of findings’ tables
‘Summary of findings’ tables offer key information concerning the best estimate of effect of the interventions included and the quantity and the quality of the evidence (Higgins 2011). We will present the results of the following outcomes in ‘Summary of findings’ tables: pain prevalence; challenging behaviour; mortality; number of people with serious adverse events; number of people with adverse events; depression; and quality of life of the people with dementia.
Acknowledgements
We thank the Cochrane Dementia and Cognitive Improvement Group, especially Sue Marcus and Anna Noel‐Storr. Also we acknowledge the support of the consumer representative.
We would like to thank peer reviewers Neil O'Connell and Patricia Schofield for their comments and feedback.
Appendices
Appendix 1. MEDLINE search strategy
1 exp Dementia/
2 Delirium/
3 Wernicke Encephalopathy/
4 Delirium, Dementia, Amnestic, Cognitive Disorders/
5 dement*.mp.
6 alzheimer*.mp.
7 (lewy* adj2 bod*).mp.
8 (chronic adj2 cerebrovascular).mp.
9 ("organic brain disease" or "organic brain syndrome").mp.
10 "benign senescent forgetfulness".mp.
11 (cerebr* adj2 deteriorat*).mp.
12 (cerebral* adj2 insufficient*).mp.
13 "major neurocognitive disorder*".ti,ab.
14 or/1‐13
15 exp Residential Facilities/
16 exp Halfway Houses/
17 exp Long‐Term Care/
18 exp Homes for the Aged/
19 exp Nursing Homes/
20 "homes for the aged".ti,ab.
21 "residential facilit*".ti,ab.
22 "nursing home*".ti,ab.
23 "care home*".ti,ab.
24 ((care or nursing or residential or rest or old* people* or old folk* or group or geriatric or aged or elderly) adj2 (home or homes or facility or facilities)).ti,ab.
25 or/15‐24
26 14 and 25
27 exp ANALGESICS/
28 exp PAIN/
29 exp Pain Management/
30 exp NARCOTICS/
31 pain*.ti,ab.
32 analges*.ti,ab.
33 or/27‐32
34 26 and 33
35 randomized controlled trial.pt.
36 controlled clinical trial.pt.
37 randomized.ab.
38 placebo.ab.
39 drug therapy.fs.
40 randomly.ab.
41 trial.ab.
42 groups.ab.
43 or/35‐42
44 exp animals/ not humans.sh.
45 43 not 44
46 34 and 45
Contributions of authors
RM developed the main concept. VL and RM reviewed the relevant literature and prepared the draft manuscript with support of ES and RT. All authors reviewed the final protocol.
Sources of support
Internal sources
No sources of support supplied
External sources
-
Ministry of Education and Research, Germany.
(Grant number 01GL1733)
-
NIHR, UK.
This protocol was supported by the National Institute for Health Research (NIHR), via Cochrane Infrastructure funding to the Cochrane Dementia and Cognitive Improvement group. The views and opinions expressed herein are those of the authors and do not necessarily reflect those of the Systematic Reviews Programme, NIHR, National Health Service or the Department of Health
Declarations of interest
Valérie Labonté: none known Erika G Sirsch: none known Rüdiger Thiesemann: none known Ralph Möhler: none known
New
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