Algoplus ® Scale in Older Patients with Dementia: A Reliable Real-World Pain Assessment Tool

Abstract

Pain is still a neglected clinical issue in elderly people with dementia and/or communicative disorders, with an unacceptable higher rate of under diagnosis and under treatment. Cognitive deficit and emotional and psychological disturbances entangle pain symptoms, affecting patient self-report. So far, observational pain tools do not have fully adequate clinimetric properties and quality requirements for easy-to-use daily rating. Older patients with dementia represent a clinical challenge. The assessment of pain is important for improving clinical outcomes, such as functional status, frailty trajectories, comorbidity, and quality of life. The PAINAID scale appears to be the most accurate pain tool in people with dementia along with the Algoplus^® scale, a recently developed tool to rapidly assess acute pain in hospitals settings. The present study aimed to assess the clinimetric properties of the Algoplus^®, as compared to PAINAID, for detecting acute pain in a real-world cohort of hospitalized older patients with dementia.

Keywords

aged dementia pain

INTRODUCTION

It is widely acknowledged that the assessment and treatment of persistent pain in elderly patients with dementia is a neglected issue [1, 2]. The assessment of acute pain is even more complex. Compared to elderly individuals who are able to communicate, those with dementia have an unacceptably higher rate of pain, which is underreported and undertreated [3, 4]. Factors affecting a patient’s self-report of pain include cognitive deficits, emotional disturbances, sensory deprivation, and a wide range of behavioral disturbances associated with dementia [5]. As a result, patients with dementia are unable to clearly describe their pain or the effects of treatment. For that reason, self-report scales for assessing pain in these patients are not effective, especially in acute care settings [6, 7]. The lack of validated observational tools for acute pain detection in patients with dementia contributes to poorer clinical outcomes and worse quality of life for these highly vulnerable patients [8].

Although accurate pain assessment tools are needed to identify both persistent and acute pain in patients with dementia [9, 10], no tools for acute pain have shown strong clinimetric properties or met quality requirements for clinical use. Thus, no gold standard exists to accurately assess acute pain in non-communicating elderly patients [11 –13]. The Pain Assessment in Advanced Dementia (PAINAD) scale appears to be among the most accurate tools for pain assessment in people with dementia [14, 15]. PAINAD has shown adequate clinimetric properties in head-to-head comparisons with other observational tools [16] and across different clinical settings. However, some authors recommend that PAINAD be used cautiously in both research and clinical settings, and only as part of a comprehensive approach to pain assessment [17]. The French Doloplus Collective Team developed and internationally validated another observational tool, the Doloplus 2^®, to assess pain in patients with dementia [18, 19]. The tool has shown good clinimetric properties for pain evolution but not incident pain. In addition, administering the tool is time-consuming and requires expertise.

To address the compelling need for specific tools to assess acute pain in non-communicating elderly patients, including those with dementia, the same group developed the Algoplus^® pain scale. The tool was validated in a multicenter cross-sectional study that included a variety of acute care settings [20]. Algoplus^® has shown good psychometric properties, with high sensitivity to changes in pain and rapid pain assessment, especially when repeated measurements are required.

Dementia is a life-threatening condition. Caring for these highly vulnerable elderly patients represents a real challenge [21], and the prompt assessment of pain is important for optimal clinical outcomes. Adequate pain management can significantly improve the patient’s functional status, frailty trajectory, comorbidity status, and quality of life [22]. Therefore, the conceptual framework of acute pain assessment should include the appropriateness of the tool used to assess different types of pain (visceral, mechanical, mixed, neuropathic), pain intensity, and response to treatment over time. In addition, identifying behaviors that are indicators of acute pain in individuals with dementia is important [23]. The development of an appropriate pain scale might help overcome resistance to pain assessment by nurses, who need a reliable tool that has been demonstrated to work in real-world situations. The present study assessed the psychometric properties of the Algoplus^® compared to PAINAD and its ability to detect acute pain in a cohort of hospitalized elderly patients with dementia.

METHODS

Study design

This prospective observational study was conducted in a transitional care ward of the IRCCS University Hospital, San Martino, Genoa, Italy. Acute pain was assessed using the Italian version of Algoplus^® (Pickering, Nicolas, Monacelli et al., submitted for publication) at baseline (t0) by an expert geriatrician, after 4 hours (t4) by another geriatrician, and after 24 hours (t24) by an expert nurse with experience in geriatrics.

Patient population

We recruited patients who were admitted to the transitional care ward from January 2016 to June 2016. Inclusion criteria were age ≥70 years and diagnosis of Alzheimer’s disease or mixed type dementia according to the revised criteria of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association [24]. Exclusion criteria were refusal to participate in the study (legal guardian); presence of delirium, clinical instability, multimorbidity (Cumulative Illness Rating Scale [CIRS] >7/13), or end-stage chronic or neoplastic diseases; receiving palliative care; and any change in the therapeutic regimen within the study period. Written consent was obtained by each patient’s legal guardian. The study was conducted in accordance with the ethical standards of the Declaration of Helsinki of 1975 and was approved by the local ethics committee.

Patient assessments

Each patient underwent a comprehensive geriatric assessment. The Mini-Mental State Examination (MMSE) was used to assess cognitive status [25], and the Severe Mini-Mental State Examination (Severe MMSE) [26] was used if the MMSE score was <10/30. The Barthel scale [27] was used to assess functional status. The SCALES mnemonic (sadness, cholesterol, albumin, loss of weight, eating, and shopping) [28] was used to assess malnutrition. The Morse Fall Scale was used to assess the risk of falling [29]. The Cornell Scale for Depression in Dementia (CSDD) [30] was used to assess signs and symptoms of major depression. The Cumulative Illness Rating Scale (CIRS) was used to assess multimorbidity [31]. The Neuropsychiatric Inventory (NPI) was used to assess behavioral disturbances [32]. The 4AT test was used to assess delirium [33]. Polypharmacy was also assessed by a review of medical records.

Before administration of the pain assessment tools, all patients underwent a clinical medical examination. In this study, the assessment of acute pain included breakthrough pain in patients with otherwise stabilized persistent pain.

Algoplus^®

Algoplus^® is a 5-item scale (total score, 0–5) consisting of the following behavioral clusters: facial expression, gaze, complaints, body position, and atypical behaviors (agitation, aggression, and grabbing onto something/someone). A score of 2 is suggestive of pain. We used the Italian version of Algoplus^®, which was evaluated as part of an international multicenter validation of the Algoplus^® tool translated into foreign languages (Pickering G, Nicolas M, Monacelli et al., submitted for publication).

PAINAD

The Italian version of the PAINAD scale [34] was used to assess pain at baseline (t0) by an expert physician. The PAINAD scale is a 5-item observational tool (total score, 0–10) that assesses facial expression, disruptive behaviors (consolability, negative vocalization), body language, and breathing. A score of 2 is suggestive of pain.

Statistical analysis

Data are reported as mean with standard deviation (SD) and interquartile range (IQR). Convergent validity between Algoplus^® and PAINAD was assessed using the nonparametric Spearman correlation coefficient. The mean Algoplus^® and PAINAD scores of patient subgroups were compared independently to assess convergent validity. These subgroups were defined according to scores on specific scales by independent samples t-test as follows. The subgroup of patients with behavioral disturbances was defined using the NPI scale (cut off value ≥20/144 for moderate-severe behavioral disturbances). The subgroup of patients with depression was defined using the CSDD (cut off value ≥5 for moderate-severe depression). The MMSE score was used to categorize patients according to severity of dementia as follows: mild-moderate dementia (10–24) or severe dementia (<10). Patients with normal and abnormal scores on the Algoplus^® and PAINAD were compared with patients with normal and abnormal scores within each subgroup (defined by the NPI, CSDD, and MMSE) using the chi-square test. The Algoplus^® longitudinal assessment was reported as mean difference in scores at t0, t4, and t24 with 95% confidence intervals (CIs). All the tests were two-tailed, and the level of significance was set at p < 0.05. Data analysis was carried out using STATA (v.13; StataCorp).

RESULTS

Patients’ demographic and clinical characteristics

Of the 435 patients admitted to the transitional care ward of the University Hospital of San Martino from January 2016 to June 2016, 96 patients (53 women and 39 men) were consecutively enrolled in this study. Mean patient age was 84.80±6.96 years (range 70–101). The patients’ clinical characteristics are shown in Table 1. The mean number of medications per patient was 5.2±0.87, and 63 of the 96 patients (66%) were taking a psychoactive drug: 31 patients (32%) were taking trazodone, 24 patients (25%) were taking atypical antipsychotics, 27 patients (28%) were taking selective serotonin reuptake inhibitors, and 14 patients (15%) were taking benzodiazepines. Twenty-four patients (25%) were taking multiple psychoactive drugs. Additionally, 46 patients (48%) received an analgesic therapy: 21 patients (22%) received a first-line analgesic therapy according to World Health Organization classification (acetaminophen), 19 patients (20%) received a second-line analgesic therapy with minor opioids (tramadol, or oxycodone/acetaminophen), and 6 patients (6%) received multiple analgesic drugs.

Psychometric properties of Algoplus^®

The convergent validity of Algoplus^® was evaluated by comparing baseline (t0) scores of the Algoplus^® and PAINAD scales. The mean Algoplus^® score at t0 was 1.50±1.51, and the mean PAINAD score was 2.11±0.22. The reliability of Algoplus^® was evaluated by comparing longitudinal assessments at t0, t4, and t24.

Convergent validity of Algoplus^® versus PAINAD

The Algoplus^® score significantly correlated with the PAINAD score at t0 (Spearman’s r = 0.78, p < 0.001, n = 96) (Fig. 1). Of the 48 patients with abnormal values on the Algoplus^® scale, 42 (88%) also showed abnormal values on the PAINAD scale. Of the 48 patients with normal values on the Algoplus^® scale, 39 (81%) also showed normal values on the PAINAD scale (p < 0.001). Results were discordant for only 15 of the 96 patients (16%).

Discriminant validity

Cornell scale for depression in dementia

For the elderly patients with dementia in our study, the mean CSDD score was 8 (SD 5.8). Of the 90 patients who underwent the CSDD assessment, 63 (70%) showed moderate to severe depression. Results of the discriminant validity test are shown in Table 2. The mean Algoplus^® score was 1.86 (SD 1.41) in patients with moderate-severe depression and 0.93 (SD1.38) in patients with mild depression (p = 0.005). Similarly, the mean PAINAD score was 2.40 (SD 2.12) in patients with moderate-severe depression and 0.74 (SD 1.16) in patients with mild depression (p < 0.001).

Moderate-severe depression was more common in patients with abnormal Algoplus^® scores (39/45, 86.7%) than in patients with normal Algoplus^® scores (24/45, 53.3%; p = 0.001). Similarly, moderate-severe depression was more common in patients with abnormal PAINAD scores (41/47, 87.2%) than in patients with normal PAINAD scores (22/43, 51.2%; p < 0.001).

NPI

Of the 93 patients who underwent the NPI assessment, the 28 (30.1%) who showed moderate-severe behavioral disturbances had a mean Algoplus^® score of 2.1 (SD 1.6), whereas the 65 patients with mild or no behavioral disturbance had a mean Algoplus^® score of 1.28 (SD 1.3) (p = 0.01). The mean PAINAD scores were 3.14 (SD 2.69) in patients with moderate-severe behavioral disturbances and 1.29 (SD 1.31) in patients with mild or no behavioral disturbances (p < 0.001).

Thirty-eight of the 65 patients (58.5%) with mild or no behavioral disturbances had normal scores Algoplus^®, and 18 of the 28 patients (64.3%) with moderate-severe behavioral disturbances had abnormal Algoplus^® scores (p = 0.044).

MMSE

The mean MMSE score in this patient population was 20.1 (SD 5.3). Patients with severe dementia according to the MMSE test (23/96, 24%) had a mean Algoplus^® score of 1.95 (SD 1.6), and patients with mild-moderate dementia (73/96, 76.0%) had a mean Algoplus^® score of 1.47 (SD 1.4), but this difference was not significant (p = 0.16). In contrast, PAINAD scores differed significantly between subgroups. Patients with severe dementia had a mean score of 3 (SD 3), and patients with mild-moderate dementia had a mean score of 1.62 (SD 1.57) (p = 0.005).

Reliability of the Algoplus^® pain scale

The mean Algoplus^® score at t0 was 1.50±1.51. The mean Algoplus^® score at t4 was 1.42 (SD 1.48), and at that time point 45 of the 96 patients (46.9%) had abnormal scores (≥2). At t24 the mean Algoplus^® score was 1.49 (SD 1.5), and at that time point 46 patients (47.9%) had abnormal scores. The mean difference at t4 and t0 was –0.17 (SD 0.56, 95% CI –0.28 to –0.05, IQR 0, range –2 to 1). At t24, the mean difference was –0.09 (SD 0.68, 95% CI –0.23 to 0.04, IQR 0). It is noteworthy that the Algoplus^® score at t0 showed a moderate positive correlation with the CSDD score for depression (r = +0.56, p < 0.001, n = 96) and with the NPI score for behavioral and psychological symptoms of dementia (r = +0.57, p < 0.001, n = 96).

DISCUSSION

The world’s elderly population continues to increase along with the prevalence of neurodegenerative diseases. Therefore, the ability to appropriately manage chronic conditions such as pain and dementia is of utmost importance. Numerous studies have evaluated the intertwined association between pain assessment and elderly people [35]. However, ageism, lack of evidence-based results, and clinical complexity have led to the underdiagnosis and undertreatment of pain in this patient population [36]. Furthermore, few studies have focused on understanding acute pain in elderly patients with dementia [37].

Patients with dementia have difficulty communicating, and the neurological and psychiatric factors associated with dementia also affect the expression of pain. However, these patients may have atypical clinical presentations that are indicators of pain including depression, behavioral disturbances, and acute worsening of cognitive and physical performance [38]. Therefore, research has focused on developing appropriate tests for pain in patients with dementia that are in accordance with the recommendations of European geriatric societies and the American Geriatric Society Panel on Chronic Pain in Older Persons [39, 40]. According to these recommendations, disruptive behaviors are highly suggestive of pain and deserve an in-depth analysis. Other behaviors associated with pain in persons with dementia include altered interpersonal interactions, changes in mental status and activity, and changes in facial expressions (e.g., grimacing, rapid blinking).

The Algoplus^® tool was specifically designed to assess pain in non-communicating subjects [20] and was internationally validated in a large cohort of hospitalized geriatric patients with communicative disorders. The results showed that Algoplus^® has good clinimetric properties. Additional studies have demonstrated that Algoplus^® is a reliable tool in different clinical settings. A recent pilot study supports the accuracy of Algoplus^® in assessing postoperative pain in elderly patients [41]. Moreover, Algoplus^® was found to be useful to overcome language barriers, as demonstrated in Cambodian patients treated by clinicians who did not speak their language [42].

Another advantage of the Algoplus^® tool is the rapidity of administration.

Analysis of the subitems has revealed some limitations of the Algoplus^® pain scale. Specifically, the “complaints” item shows less discriminatory ability for acute pain detection. To avoid bias, the patient’s complaints should to be rated without interpretation by the clinician. Clinicians may otherwise mark no complaints because of a poor understanding of dementia. Another potential concern about the Algoplus^® tool is that it may be too short to accurately detect pain.

The present study aimed to assess acute pain in a real-world cohort of elderly hospitalized patients with dementia using Algoplus^®. This tool evaluates behavioral indicators of acute pain in people with dementia, who are not adequately assessed by most pain scales because of their medical and psychosocial complexity. Our results show a significant convergent validity between the Algoplus^® and PAINAD observational tools. Both tools assess pain in dementia, require little time to administer, and do not require extensive knowledge of the patient. Although PAINAD is considered to be a useful pain assessment tool, it has a marked floor effect, particularly when patients are at rest, and should therefore be used after the patient has been active [17]. The breathing item of PAINAD contributes to the floor effect, affecting the validity of the tool. In addition, careful training in the use of PAINAD is important to accurately describe the patient’s behaviors [17], and there are concerns about inter-rater reliability and performance of the tool during routine care in a geriatric care setting.

Although the authors did not formulate a hypothesis regarding the superiority of one tool over the other, the psychometric analysis of Algoplus^® compared to a gold standard may provide useful knowledge for the further development of pain assessment tools. In this study, Algoplus^® showed adequate psychometric properties (convergent validity, discriminant validity) in a cohort of elderly patients with dementia. Our results with the Algoplus^® tool indicate that pain was underdiagnosed in almost 30% of the enrolled patients, which is consistent with previous studies showing that pain is underreported and less effectively treated in non-communicative patients compared to communicative patients [37].

The discriminant validity of Algoplus^® addresses a relevant issue in the field of pain and dementia. Analysis of patient subgroups showed a significantly higher mean Algoplus^® score for patients with more severe depression or behavioral disturbances. However, Algoplus^® scores of patients with severe dementia did not differ significantly from those of patients with mild to moderate dementia. These results add to the current knowledge about depression and behavioral disturbances, which are intertwined with pain and affect the expression of pain in patients with dementia. Although differentiating behaviors related to pain from those related to the dementia process itself is difficult, Algoplus^® appears to be able to distinguish pain in patients with dementia who are affected by depression or behavioral disturbances. These findings corroborate the recent observations of Bonin-Guillaume et al. [43], who reported that Algoplus^® was able to specifically detect behaviors generated by pain but not those caused by depression. These results may have important clinical implications to inform clinical decisions.

The correlation between the Algoplus^® score for pain and the CSDD score for depression (r = 0.56) is consistent with the bidirectional relationship between the two clinical conditions, which is partly due to shared neural mechanisms. Untreated pain can aggravate depression, and depression can decrease the pain threshold, amplifying pain-like behaviors and somatic complaints [44]. Further research is needed to understand how an affective disorder may affect the discriminatory ability of Algoplus^® to distinguish between depression and pain. It is generally accepted that effective pain management improves depressive symptoms; however, emerging epidemiological evidence indicates that the incidence/relapse of depression is higher after the initiation of opioid therapy [45]. Therefore, additional studies focusing on the neurobiological basis and psychosocial processes of dementia is needed to guide therapeutic decisions.

In the present study, behavioral symptoms showed a moderate association with the Algoplus^® score. Even if the behavioral symptoms have a multifactorial origin, accumulating evidence supports a causal role of pain in disruptive behavior in patients with dementia [46]. In particular, opposition to care, complaining, negativism, verbal aggression, and agitation are associated with the presence of pain [47]. Agitation, restlessness, and pacing all improve with analgesics, and a recent randomized trial in patients with moderate to severe dementia reported that opioid analgesics improved agitation and did not worsen psychotic symptoms [48]. However, behavioral disturbances may represent a confounding bias in assessing pain in dementia, resulting in the overestimation of pain or the inappropriate use of psychoactive drugs [8]. Therefore, studies investigating how pain treatment should be balanced with psychotropic drug administration are needed. Overuse of psychotropic drugs might also serve as a surrogate endpoint in pain assessment of agitated patients with dementia.

In the field of pain among elderly patients with dementia, the critical issue is to identify predictive behavioral clusters that can be used to guide therapeutic decisions. The availability of a reliable tool to detect pain in patients with dementia may facilitate this difficult process.

Strengths and limitations

The strengths of the study include the approximate real-life situations in which elderly patients with dementia and highly complex needs were assessed in a transitional care setting. This study, which was the first to compare the Algoplus^® and PAINAD tools, demonstrated that Algoplus^® has good psychometric properties. Algoplus^® showed good repeatability when used by different health care providers, which is an important issue in the multidisciplinary management of elderly patients. Algoplus^® is a short and easy-to-use instrument suitable for routine clinical practice, especially when repeated measurements are needed. Not least, Algoplus^® requires little training. These characteristics fit a nurse’s need for a simple and effective clinical tool for pain assessment in non-communicative patients.

The limitations of the study include the relatively small sample size and lack of data on the Algoplus^® scale’s sensitivity to changes in pain and response to treatment. In addition, we did not analyze each item in the scale; however, this issue was recently investigated in a head-to-head comparison between Algoplus^®, Doloplus, Pain Assessment Checklist for Seniors with Limited Ability to Communicate (PACSLAC), and numeric rating scale [43]. These authors concluded that the Algoplus^® items “gaze” and “body position” were not sensitive to changes in pain after the initiation of pain management, and “facial expression” and “complaints” were best able to discriminate between pain and non-pain symptoms in depressed patients. These last two items are considered good indicators of pain regardless of the presence of affective or cognitive disorders [43]. The discriminant validity of Algoplus^® must be more accurately assessed in patients in various stages of dementia to better understand pain expression and determine threshold scores according to the natural history of dementia. Our study did not evaluate changes in the administration of psychoactive drugs and antidepressants after analgesic treatment; these data may provide information to better discriminate between pain and depression or other psychiatric conditions in patients with dementia. In addition, our study did not address the issue of pain intensity. The ability to evaluate pain intensity using the total pain scale score or presence of specific behaviors is a critical issue. More sensitive tools are needed to ascertain the ability of specific behaviors to predict pain intensity in patients with dementia, and these items may need to be weighted more heavily in the total score.

In conclusion, the present study adds to the current knowledge in the field of acute pain in patients with dementia by exploring pain-like behaviors in an elderly population in a real-world situation. The study evaluated the psychometric properties of the Algoplus^® pain scale; however, our results must be confirmed using other validated versions of the tool translated into other languages.

DISCLOSURE STATEMENT

Authors’ disclosures available online (http://j-alz.com/manuscript-disclosures/16-0790r2).

References

Shega

, Hougham

, Stocking

, Cox-Hayley

, Sachs

(2004) Pain in community-dwelling persons with dementia: Frequency, intensity, and congruence between patient and caregiver report. J Pain Symptom Manage 28, 585–592.

Husebo

, Strand

, Moe-Nilssen

(2008) Who suffers most? Dementia and pain in nursing home patients: A cross-sectional study. J Am Med Dir Assoc 9, 427–433.

Haasum

, Fastbom

, Fratiglioni

, Kareholt

, Johnell

(2011) Pain treatment in elderly persons with and without dementia: A population-based study of institutionalized and home-dwelling elderly. Drugs Aging 28, 283–293.

Tan

, Visvanathan

, Hilmer

, Vitry

, Emery

, Robson

, Pitkala

, Ilomaki

, Bell

(2016) Analgesic use and pain in residents with and without dementia in aged care facilities. A cross sectional study. Australas J Aging 35, 180–187.

Corbett

, Husebo

, Malcangio

(2012) Assessment and treatment of pain in people with dementia. Nat Rev Neurol 8, 264–274.

Herr

, Coyne

, McCaffery

, Manworren

, Merkel

(2012) Pain assessment in the patient unable to self-report: Position statement with clinical practice recommendations. Pain Manag Nurs 12, 230–520.

Husebo

, Achterberg

, Flo

(2016) Identifying and managing pain in people with Alzheimer’s disease and other type of dementia: A systematic review. CNS Drugs 30, 481–497.

Zwakhalen

, Hamers

, Abu-Saad

, Berger

(2006) Pain in elderly people with severe dementia: A systematic review of behavioural pain assessment tools. BMC Geriatr 6, 3.

Herr

, Bjoro

, Decker

(2006) Tools for assessment of pain in nonverbal older adults with dementia: A state-of-the-science review. J Pain Sympt Manage 31, 170–192.

10.

Hadjistavropoulos

, Herr

, Turk

DCP

(2007) An interdisciplinary expert consensus statement on assessment of pain in older persons. Clin J Pain 23, S1–S43.

11.

Van Herk

, Van Dijk

, Baar

, Tibboel

, De Wit

(2007) Observation scales for pain assessment in older adults with cognitive impairments or communication difficulties. Nurs Res 56, 34–43.

12.

Herr

(2011) Pain assessment strategies in older patients. J Pain 12, S3–S13.

13.

Chapman

(2008) Progress in pain assessment: The cognitively compromised patient. Curr Opin Anaesthesiol 21, 610–615.

14.

Zwakhalen

, van

der Steen JT

, Najim

(2012) Which score most likely represents pain on the observational PAINAD pain scale for patients with dementia? Am Med Dir Assoc 13, 384–389.

15.

Lukas

, Barber

, Johnson

, Gibson

(2013) Observer-rated pain assessment instruments improve both the detection of pain and the evaluation of pain intensity in people with dementia. Eur J Pain 17, 1558–1568.

16.

Horgas

(2012) Assessing pain in older adults with dementia. J Am Med Dir Assoc 4, 9–15.

17.

Ersek

, Herr

, Neradilek

, Buck

, Black

(2010) Comparing the psychometric properties of the Checklist of Nonverbal Pain Behaviors (CNPI) and the Pain Assessment in Advanced Dementia (PAIN-AD) instruments. Pain Med 11, 395–404.

18.

Pautex

, Herrmann

, Michon

, Giannakopoulos

, Gold

(2007) Psychometric properties of the Doloplus-2 observational pain assessment scale and comparison to self-assessment in hospitalized elderly. Clin J Pain 23, 774–779.

19.

Pickering

, Gibson

, Serbouti

, Odetti

, Ferraz Gonçalves

, Gambassi

, Guarda

, Hamers

, Lussier

, Monacelli

, Pérez-Castejón Garrote

, Zwakhalen

, Barneto

; Collectif Doloplus, Wary

(2010) Reliability study in five languages of the translation of the pain behavioural scale Doloplus. Eur J Pain 14, 545.e1–10.

20.

Rat

, Jouve

, Pickering

, Donnarel

, Nguyen

, Michel

, Capriz-Ribière

, Lefebvre-Chapiro

, Gauquelin

, Bonin-Guillaume

(2011) Validation of an acute pain-behavior scale for older persons with inability to communicate verbally: Algoplus. Eur J Pain 15, 198.e1–198.

21.

Ientile

, De Pasquale

, Monacelli

, Odetti

, Traverso

, Cammarata

, Tabaton

, Dijk

(2013) Survival rate in patients affected by dementia followed by memory clinics (UVA) in Italy. J Alzheimers Dis 36, 303–309.

22.

Kojima

, Taniguchi

, Iliffe

, Walters

(2016) Frailty as a predictor of Alzheimer disease, vascular dementia, and all dementia among community-dwelling older people: A systematic review and meta-analysis. J Am Med Dir Assoc 17, 881–888.

23.

Flo

, Gulla

, Husebo

(2014) Effective pain management in patients with dementia: Benefits beyond pain? Drugs Aging 31, 863–871.

24.

McKhann

, Knopman

, Chertkow

, Hyman

, Jack

, Kawas

(2011) The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 263–269.

25.

Folstein

, Folstein

, McHugh

(1975) Mini-Mental State: A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12, 189–191.

26.

Harrell

, Marson

, Chatterjee

, Parrish

(2000) The Severe Mini-Mental State Examination: A new neuropsychological instrument for the bedside assessment of severely impaired patients with Alzheimer disease. Alzheimer Dis Assoc Disorder 14, 168–175.

27.

Shah

, Vanclay

, Cooper

(1989) Improving the sensitivity of the Barthel Index for stroke rehabilitation. Int J Clin Epidemiol 42, 703–709.

28.

Morley

(1991) Why do physicians fail to recognize and treat malnutrition in older persons? J Am Geriatr Soc 39, 1139–1140.

29.

Morse

, Morse

, Tylko

(1989) Development of a scale to identify the fall-prone patient. Can J Aging 8, 366–367.

30.

Alexopoulos

, Abrams

, Young

, Shamoian

(1988) Cornell scale for depression in dementia. Biol Psych 23, 271–284.

31.

Salvi

, Miller

, Grilli

, Giorgi

, Towers

, Morichi

, Spazzafumo

, Mancinelli

, Espinosa

, Rappelli

, Dessí-Fulgheri

(2008) A manual of guidelines to score the modified cumulative illness rating scale and its validation in acute hospitalized elderly patients. Am Geriatr Soc 56, 1926–1931.

32.

Cummings

, Mega

, Gray

, Rosenberg-Thompson

, Carusi

, Gornbein

(1994) The Neuropsychiatric Inventory: Comprehensive assessment of psychopathology in dementia. Neurology 44, 2308–2314.

33.

Bellelli

, Morandi

, Davis

, Mazzola

, Turco

, Gentile

, Ryan

, Cash

, Guerini

, Torpilliesi

, Del Santo

, Trabucchi

, Annoni

, MacLullich

(2014) Validation of the 4AT, a new instrument for rapid delirium screening: A study in 234 hospitalised older people. Age Ageing 43, 496–502.

34.

Costardi

, Rozzini

, Costanzi

, Ghianda

, Franzoni

, Padovani

, Trabucchi

(2007) The Italian version of the pain assessment in advanced dementia (PAINAD) scale. Arch Gerontol Geriatr 44, 175–180.

35.

Hadjistavropoulos

, Herr

, Prkachin

, Craig

, Gibson

, Lukas

, Smith

(2014) Pain assessment in elderly adults with dementia. Lancet Neurol 13, 1216–1227.

36.

Tan

, Jokanovic

, Koponen

(2015) Prevalence of analgesic use and pain in people with and without dementia or cognitive impairment in aged care facilities: A systematic review and meta-analysis. Curr Clin Pharmacol 10, 194–203.

37.

Corbett

, Husebo

, Achterberg

(2014) The importance of pain management in older people with dementia. Br Med Bull 111, 139–248.

38.

Herr

, Zwakhalen

, Swafford

(2016) Observation of pain in dementia. Curr Alzheimer Res. Jun 2. [Epub ahead of print] doi: 10.2174/1567205013666160602234526

39.

(1998) The management of chronic pain in older persons: AGS Panel on Chronic Pain in Older Persons. American Geriatrics Society. Geriatrics 53(Suppl 3), S8–S24.

40.

Gloth

3rd (2011) Pharmacological management of persistent pain in older persons: Focus on opioids and nonopioids. J Pain 12(3 Suppl 1), S14–S20.

41.

Duale

, Pereira

, Abbal

, Julien

, Rat

, Schoeffler

, Pickering

(2015) The Algoplus score to assess acute postoperative pain in elderly patients-A pilot observational study. Pain Manag Nurs 16, 890–899.

42.

Pickering

, Bunna

, Rat

, Madeline

, Lebost

, Serrie

, Pereia

(2013) Acute pain evaluation with Algoplus scale in Cambodian patients. Pain Med 14, 1971–1976.

43.

Bonin-Guillaume

, Jouve

, Laureatta

, Nalin

, Truillet

, Capriz

, Rat

(2016) Algoplus performance to detect pain in depressed and/or demented old adults. Eur J Pain 20, 1185–1193.

44.

Hooten

(2016) Chronic pain and mental health disorders: Shared neural mechanisms, epidemiology, and treatment. Mayo Clin Proc 91, 955–970.

45.

Ruan

, Kaye

, Chiravuri

(2016) Increased risk of depression recurrence after initiation of prescription opioids in noncancer pain patients. J Pain 17, 944–955.

46.

Husebo

, Ballard

, Aarsland

(2011) Pain treatment of agitation in patients with dementia: A systematic review. Int J Geriatr Psychiat 26, 1012–1018.

47.

van Dalen-Kok

, Pieper

, de Waal

, Lukas

, Husebo

, Achterberg

(2015) Association between pain, neuropsychiatric symptoms, and physical function in dementia: A systematic review and meta-analysis. BMC Geriatr 19, 15–49, Erratum in: BMC Geriatr. 2015;15:109.

48.

Habiger

, Flo

, Achterberg

, Husebo

(2016) The interactive relationship between pain, psychosis, and agitation in people with dementia: Results from a cluster-randomised clinical trial. Behav Neurol 70, 364–375.

Algoplus ® Scale in Older Patients with Dementia: A Reliable Real-World Pain Assessment Tool

Abstract

Keywords

INTRODUCTION

METHODS

Study design

Patient population

Patient assessments

Algoplus®

PAINAD

Statistical analysis

RESULTS

Patients’ demographic and clinical characteristics

Psychometric properties of Algoplus®

Convergent validity of Algoplus® versus PAINAD

Discriminant validity

Cornell scale for depression in dementia

NPI

MMSE

Reliability of the Algoplus® pain scale

DISCUSSION

Strengths and limitations

DISCLOSURE STATEMENT

References

Algoplus^®

Psychometric properties of Algoplus^®

Convergent validity of Algoplus^® versus PAINAD

Reliability of the Algoplus^® pain scale