Algorithm-based pain management for people with dementia in nursing homes

Abstract

Background: People with dementia in nursing homes often experience pain, but often do not receive adequate pain therapy. The experience of pain has a significant impact on quality of life in people with dementia, and is associated with negative health outcomes. Untreated pain is also considered to be one of the causes of challenging behaviour, such as agitation or aggression, in this population. One approach to reducing pain in people with dementia in nursing homes is an algorithm-based pain management strategy, i.e. the use of a structured protocol that involves pain assessment and a series of predefined treatment steps consisting of various non-pharmacological and pharmacological pain management interventions.

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.

Search methods: We searched ALOIS, the Cochrane Dementia and Cognitive Improvement Group's register, MEDLINE, Embase, PsycINFO, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science Core Collection (ISI Web of Science), LILACS (Latin American and Caribbean Health Science Information database), ClinicalTrials.gov and the World Health Organization's meta-register the International Clinical Trials Registry Portal on 30 June 2021.

Selection criteria: We included randomised controlled trials investigating the effects of algorithm-based pain management interventions for people with dementia living in nursing homes. All interventions had to include an initial pain assessment, a treatment algorithm (a treatment plan consisting of at least two different non-pharmacological or pharmacological treatment steps to reduce pain), and criteria to assess the success of each treatment step. The control groups could receive usual care or an active control intervention. Primary outcomes for this review were pain-related outcomes, e.g. the number of participants with pain (self- or proxy-rated), challenging behaviour (we used a broad definition that could also include agitation or behavioural and psychological symptoms assessed with any validated instrument), and serious adverse events.

Data collection and analysis: Two authors independently selected the articles for inclusion, extracted data and assessed the risk of bias of all included studies. We reported results narratively as there were too few studies for a meta-analysis. We used GRADE methods to rate the certainty of the results.

Main results: We included three cluster-randomised controlled trials with a total of 808 participants (mean age 82 to 89 years). In two studies, participants had severe cognitive impairment and in one study mild to moderate impairment. The algorithms used in the studies varied in the number of treatment steps. The comparator was pain education for nursing staff in two studies and usual care in one study. We judged the risk of detection bias to be high in one study. The risk of selection bias and performance bias was unclear in all studies. Self-rated pain (i.e. pain rated by participants themselves) was reported in two studies. In one study, all residents in the nursing homes were included, but fewer than half of the participants experienced pain at baseline, and the mean values of self-rated and proxy-rated pain at baseline and follow-up in both study groups were below the threshold of pain that may require treatment. We considered the evidence from this study to be very low-certainty and therefore are uncertain whether the algorithm-based pain management intervention had an effect on self-rated pain intensity compared with pain education (MD -0.27, 95% CI -0.49 to -0.05, 170 participants; Verbal Descriptor Scale, range 0 to 3). In the other study, all participants had mild to moderate pain at baseline. Here, we found low-certainty evidence that an algorithm-based pain management intervention may have little to no effect on self-rated pain intensity compared with pain education (MD 0.4, 95% CI -0.58 to 1.38, 246 participants; Iowa Pain Thermometer, range 0 to 12). Pain was rated by proxy in all three studies. Again, we considered the evidence from the study in which mean pain scores indicated no pain, or almost no pain, at baseline to be very low-certainty and were uncertain whether the algorithm-based pain management intervention had an effect on proxy-rated pain intensity compared with pain education. For participants with mild to moderate pain at baseline, we found low-certainty evidence that an algorithm-based pain management intervention may reduce proxy-rated pain intensity in comparison with usual care (MD -1.49, 95% CI -2.11 to -0.87, 1 study, 128 participants; Pain Assessment in Advanced Dementia Scale-Chinese version, range 0 to 10), but may not be more effective than pain education (MD -0.2, 95% CI -0.79 to 0.39, 1 study, 383 participants; Iowa Pain Thermometer, range 0 to 12). For challenging behaviour, we found very low-certainty evidence from one study in which mean pain scores indicated no pain, or almost no pain, at baseline. We were uncertain whether the algorithm-based pain management intervention had any more effect than education for nursing staff on challenging behaviour of participants (MD -0.21, 95% CI -1.88 to 1.46, 1 study, 170 participants; Cohen-Mansfield Agitation Inventory-Chinese version, range 7 to 203). None of the studies systematically assessed adverse effects or serious adverse effects and no study reported information about the occurrence of any adverse effect. None of the studies assessed any of the other outcomes of this review.

Authors' conclusions: There is no clear evidence for a benefit of an algorithm-based pain management intervention in comparison with pain education for reducing pain intensity or challenging behaviour in people with dementia in nursing homes. We found that the intervention may reduce proxy-rated pain compared with usual care. However, the certainty of evidence is low because of the small number of studies, small sample sizes, methodological limitations, and the clinical heterogeneity of the study populations (e.g. pain level and cognitive status). The results should be interpreted with caution. Future studies should also focus on the implementation of algorithms and their impact in clinical practice.

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Figure 1: Study flow diagram

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Figure 2: Risk of bias graph

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Figure 3: Risk of bias summary

1.1. Analysis

Comparison 1: Algorithm‐based pain management vs control, Outcome 1: Pain (self‐rating)

1.2. Analysis

Comparison 1: Algorithm‐based pain management vs control, Outcome 2: Pain (proxy‐rating)

1.3. Analysis

Comparison 1: Algorithm‐based pain management vs control, Outcome 3: Challenging behaviour