Moving on - the next step in developing an International Classification System for Cancer Pain

Robin Fainsinger, Cheryl Nekolaichuk, Pablo Amigo, Amanda

Brisebois, Sarah Burton Macleod, Rebekah Gilbert, Yoko Tarumi, Vincent Thai, Gary Wolch, Lara

Fainsinger & Viki Muller

Division of Palliative Care Medicine University of Alberta

Acknowledgments

Covenant Health Palliative Institute

Covenant Health Research Trust Fund Grant

Office of the Provost and VP (Academic) Summer Research Award

Human Resources and Skills Development Canada: Canada Summer Jobs Program

Jerri-Lynn Goulet, Rachel Elston & Hue Quan

Nurse consultants at the RAH & UAH

2

I have no relationship that could be perceived as placing me in a real or apparent conflict of interest in the context of this presentation.

Disclosure

Advanced Cancer Pain

Underdiagnosis and undertreatment1

Complex pain syndromes often require more intense treatment and more time to achieve stable pain control

No universally accepted system to predict complexity of cancer pain management

1 Cleeland, JAMA, 1998; 17:1877-82 4

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Development of the Edmonton Classification System for Cancer Pain (ECS-CP)

ESS

rESS

ECS-CP

1989 - 1995

2000 - 2005

2005 - present

• Inter-rater reliability (Fainsinger et al, 2005)

• Predictive validity (Fainsinger et al, 2005)

• Construct validity (Nekolaichuk et al, 2005)

• Pain intensity as predictor (Fainsinger et al, 2009)

• Predictive validity in international sample (Fainsinger et al, 2010)

N - Mechanism of Pain I - Incident Pain P - Psychological Distress A - Addictive Behavior C - Cognitive Function

6

N - Mechanism of Pain I - Incident Pain P - Psychological Distress A - Addictive Behavior C - Cognitive Function

Ne Ii Pp Aa Co 7

New perspectives

Systematic review of cancer pain classification systems 1

Expert conference on cancer pain assessment and classification – need for international consensus 2

Domains that should be included in a cancer pain system 3

New guidelines for the assessment of neuropathic pain 4

1 Knudsen AK, Aass N, Fainsinger R, et al Classification of pain in cancer patients – a systematic review. Palliat Med 2009;23:295–30

2 Kaasa S, Apolone G, Klepstad P et al. Expert conference on cancer pain assessment and classification – the need for international consensus: Work proposals on international standards. BMJ Support Palliat Care, doi:10.1136/bmjspcare-2011-000078

3 Knudsen AK, Brunelli C, Klepstad P, et al Which domains should be included in a cancer pain system? Analyses of longitudinal data. Pain 2012;153:696-703

4 Haanpää M, Attal N, Backonja M, Baron R, Bennett M, Bouhassira D, Cruccu G, Hansson P, Haythornthwaite JA, Iannetti GD, Jensen TS, Kauppila T, Nurmikko TJ, Rice AS, Rowbotham M, Serra J, Sommer C, Smith BH, Treede RD. NeuPSIG guidelines on neuropathic pain assessment. Pain 2011; 152:14-27.

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Patient Generated

Assessments & Features

Objective Assessments

Physician Generated

Assessments

Pain Intervention

9

Pain Intervention

• Pain intensity (initial)

• Pain localization

• Pain relief

• Sleep disturbance

• Age

• Cancer diagnosis

• Genetic variation

• Chronic pain history

• Smoking history

ECS-CP features

• Pain mechanism

• Incident pain

• Psycholog distress

• Addictive behavior

• Cognition

Examples:

• Cog tests

• Phys exam

• Neuroimaging

• CAGE

Patient Generated

Assessments & Features

Objective Assessments

Physician Generated

Assessments

10

Pain Intervention

Outcomes • Stable pain control • Personal pain goal • Opioid dose (final) • Opioid dose escalat. • Opioid tolerance • Adjuvant analgesics • Adjuvant modalities • ID team needs • Pain intensity (final)

• Pain intensity (initial)

• Pain localization

• Pain relief

• Sleep disturbance

• Age

• Cancer diagnosis

• Genetic variation

• Chronic pain history

• Smoking history

ECS-CP features

• Pain mechanism

• Incident pain

• Psycholog distress

• Addictive behavior

• Cognition

Examples:

• Cog tests

• Phys exam

• Neuroimaging

• CAGE

Patient Generated

Assessments & Features

Objective Assessments

Physician Generated

Assessments

11

Pain Intervention

• Pain intensity (initial)

• Age

• Cancer diagnosis

• Chronic pain hx

• Smoking history

• Depression (initial)

ECS-CP features

• Pain mechanism

• Incident pain

• Psycholog distress

• Addictive behavior

• Cognition

Examples:

• MMSE

• CAGE

Patient Generated

Assessments & Features

Objective Assessments

Physician Generated

Assessments

Outcomes • Stable pain control • Personal pain goal • Opioid dose (final) • Adjuvant analgesics • Adjuvant modalities • ID team needs

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Research Hypothesis

Patients with less problematic pain features (as classified by the ECS-CP)

lower pain intensity and depression scores

absence of a smoking history

will

require a shorter time to achieve stable pain control

require less complicated analgesic regimens

be more responsive to opioid therapy and

use lower opioid doses

than patients with more complex pain syndromes. 13

Objectives

Assess the predictive validity of the Edmonton Classification System for Cancer Pain (ECS-CP) and additional variables as a tool for classifying cancer pain, in a pilot sample of 300 palliative patients in the Edmonton Zone Palliative Care Program (EZPCP) in Edmonton, AB Canada.

Test an internet multisite data collection system

Compare the achievement of personalized pain goals to the standard definition of stable pain control used in previous studies of the ECS-CP.

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Methods

Data Collection Sites

Royal Alexandra Hospital (RAH), n=100

University of Alberta Hospital (UAH), n=100

Grey Nuns Hospital, Tertiary Palliative Care Unit (TPCU), n=100

Completion of ECS-CP by physician/palliative care consultant

Initial

Weekly

Final

Direct data entry into web-based data form

Informed consent not obtained from patients, as only clinical data routinely documented in all services was collected (pilot study)

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Stable Pain Control

For 3 Consecutive

Days:

Cognitively

Intact

Cognitively

Impaired

< 3 PRN doses per

day

Pain-NRS < 3/10

Or < PPG

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Patient Demographics (Initial Assessment)

n %

Gender Male 166 55

Female 134 45

n Mean Range SD

Age (yrs) 300 69 19-98 13

Previous Opioid treatment for chronic non malignant pain (yrs)

21 7 1-30 7

Smoking History (pack yrs) 183 34 1-156 22

Depression (ESAS-r) 231 3.5 3

Median

Performance Status (PPS) 231 40 10-80

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Cancer Diagnosis (n=300)

n %

Gastro Intestinal 97

32%

Lung 73 24%

Genito-Urinary 55 18%

Hematology 23 8%

Head & Neck 15 5%

Breast 13 4%

Brain 10 3%

Unknown Primary 7 2%

Other 4 1%

Musculo-skeletal 3 1%

33%

24%

18%

8%

5%

4%

4%

2% 1% 1%

Gastro Int Lung Gastro Uri

Hematology Head & Neck Breast

Brain Unknown Primary Other

Musculo-skeletal18

Results: Stable Pain Control

Total Sample

(n=300)

Pain Syndrome

(n=231)

No Pain

(n=69)

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ECS-CP Pain Mechanism for Patients with Pain Syndrome on Initial Assessment (n=231)

Feature n %

Nociceptive Nc 175 76%

Neuropathic Ne 48 21%

Unable to assess Nx 8 3% 76%

21%

3%

Pain Mechanism

Nc

Ne

Nx

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Initial ECS-CP Features for Patients with Neuropathic Pain (n=48): Comparison with NeuPSIG Guidelines

Feature Total Total %

1. Pain distribution is neuroanotomically plausible 48 100

2. History is suggestive of relevant lesion or disease 48 100

3. Negative or positive sensory signs within innervations territory of lesions are present

34 71

4. A diagnostic test confirms lesion or disease 34 71

All 4 criteria present = definite NP 34/48 (71%)

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Initial ECS-CP Features for Patients with Pain Syndrome (n=231): Incident Pain

Feature n %

No Incident pain

Io 122 53%

Incident pain present

Ii 60 26%

Unable to assess

Ix 49 21% 53%

26%

21%

Incident Pain

Io

Ii

Ix

22

Initial ECS-CP Features for Patients with Pain Syndrome (n=231): Psychological Distress

Feature n %

No psychological distress

Po 142 61%

Psychological distress present

Pp 37 16%

Unable to assess Px 52 23% 61% 16%

23%

Psychological Distress

Po

Pp

Px

23

Initial ECS-CP Features for Patients with Pain Syndrome (n=231): Addictive Behavior

Feature n %

No addictive behavior

Ao 193 84%

Addictive behavior present

Aa 18 8%

Unable to assess Ax 20 9% 84%

8% 9%

Addictive Behavior

Ao

Aa

Ax

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Initial ECS-CP Features for Patients with Pain Syndrome (n=231): Cognitive Status

Feature n % Normal Co 154 67% Impaired Ci 50 22% Unresponsive Cu 23 10% Unable to assess Cx 4 2%

67%

22%

10%

2%

Cognitive Status

Co

Ci

Cu

Cx

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Pain Intensity Categories On Admission (n=229*)

Pain Intensity UAH % RAH % TPCU% Total% Mild (0-3) 39% 48% 35% 40%

Moderate (4-6) 39% 30% 32% 33%

Severe (7-10) 23% 23% 32% 27% * Unable to assess 2 patients

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

UAH % RAH % TPCU %* Total %

Severe

Moderate

Mild

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Results

Adjuvant Analgesic (n=259*)

Total Total %

None 109 42%

Corticosteroids 52 20%

Tylenol 39 15%

Anticonvulsant 28 11%

Other 10 4%

NSAIDS 9 3%

Tricyclic Antidepres 8 3%

Bisphosphonate 4 2%

Oral L Anest 0 0% *Does not add up to n=231 due to multiple

responses from unique patients

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Total %

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Results

Other Method of Pain Control (n=237*)

Total Total %

None 212 89%

Radiotherapy 12 5%

Chemotherapy 5 2%

Other 3 1%

Anesthes Procedure 2 1%

Surgical Procedure 2 1%

Accupuncture 1 0% Transcut Nerve Stimulation 0 0%

*Does not add up to n=231 due to multiple responses from unique patients

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Total %

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Results: Stable Pain Control

Total Sample

(n=300)

Pain Syndrome

(n=231)

No Pain

(n=69)

Stable Pain (n=135/227) (59%)

Death (n=47/227) (21%)

Ongoing (n=4)

Discharge (n=45/227) (20%)

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Results: Stable Pain Control

Stable Pain

(n=135)

PPG or Study Defn

(n=91)

PRN only

(n=44)

Both Defns

(n=81)

Study Defn Only

(n=6)

PPG Only

(n=4)

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Frequency distribution of the personalized pain goal (PPG) (n=169)

PPG (n=169*)

Score n % 0 5 3% 1 1 1% 2 33 20% 3 74 44% 4 21 12% 5 26 15% 6 6 4% 7 1 1% 8 1 1%

10 1 1% *Declined to Answer = 3

Unable to Assess= 59

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

0 1 2 3 4 5 6 7 8 10

Median = 3

PPG

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Time to Stable Pain Control (Days)

Stable Pain Control Definition

Sample Size Mean (days) Standard Deviation

Personalized Pain Goal

85

5.9

5.2

Study Definition (cognitively intact)

87

6.3

6.2

PRNs Only (cognitive impairment)

44

8.2

7.8

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Mean (hrs)

SD Range (hrs)

Stable Pain (n=63) 4.2 3.5 0 – 20.3

Death (n=19) 4.0 3.3 0.3 – 10.8

Discharge (n=12) 9.5 8.2 1.6 -29.6

Interdisciplinary Team Hours (n=94) TPCU ONLY

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Discussion: Findings

Value in using standardized criteria NeuPSIG criteria

added guidelines for incident pain

Effectiveness of additional outcome PPG range does appear to validate the original stable pain

definition as appropriate for most patients.

Effectiveness of additional predictors chronic pain smoking history depression interprofessional team involvement

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Small sample size – preliminary data analysis

Intricacies of multiple diverse study sites:

Various locations of care and weekend staffing limitations

Availability of stand alone computer vs. desired tablet

Weekly ECS-CP follow ups added increased workload to study

Tracking patients transferred from acute care site to TPCU (n=6)

Summer – proved to be challenging due to staffing (vacations)

Discussion: Methodological Issues

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36

Clinical Use

• conducted on admission to palliative care service

• subsequent assessments conducted as needed

• used to guide the ID team in pain management

Available at www.palliative.org 36

Conduct more consistent assessments using standardized criteria

Identify patients with complex pain profiles

Assist with pain management strategies

pharmacological

non-pharmacological: trigger for referrals to ID team members

Provide team with a common language and communication tool

medical record

team conference

Use for administrative purposes

appropriate use of resources : assist with patient triage and referral to appropriate setting

performance indicator

Clinical Applications

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Examples of Pain Profiles and Positive Risk Factor Combinations

Factors Age Pain Profile Median Time

(95% CI)

1 ≥ 60 NcIoPoAoCo-8 5 (4 – 7)

3 ≥ 60 NeIiPoAoCo-8 10 (8 – 14)

5 < 60 NeIiPpAoCo-8 30 (14 - )

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Demonstrated feasibility of pilot study

use of standardized criteria provide more consistent assessments

small sample in one program: need for international multisite study

value of using routine assessments in clinical practice (waiver of patient consent)

Clinical applications

Use of the NeuPSIG guidelines and revised incident pain definitions

Use of patient-generated personalized pain goal

Common language for clinical and administrative purposes

Further research regarding

relevance of chronic pain history, smoking history and depression

ID team involvement

Conclusion

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