Steps towards an international classification system for cancer pain

Transcription

1 1 EPCRC Steps towards an international classification system for cancer pain Stein Kaasa Ghent, 18 October, 2012 Cancer Clinic St. Olavs University Hospital, Trondheim, Norway

2 Background 2 Pain

3 Background: 3 Cancer pain Pain in cancer patients About 70% of cancer patients with advanced disease experience pain As many as 50 % are undertreated Teunissen et al. J. Pain Symptom Manage Holtan et al. Palliat Med 2007

4 Background: 4 Cancer pain The lack of standardised diagnostic tools is one reason for the undertreatment of cancer pain Bruera et al. Pain 1989 Fainsinger et al. Eur. J. Cancer 2008 Harding et al. J Pain Symptom Manage 2011

5 Background: 5 Classification Classification

6 Background: 6 Classification Classification what is it? To make a diagnosis is to classify To classify means to arrange groups of conditions The grouping is based upon predefined criteria Hempel 1961 Hjermstad et al. Curr. Opin. Support.Palliat. Care 2009

7 Background: 7 Assessment Assessment constitutes the basis for correct classification Measurement: Process of collecting and documenting information needed to conclude For subjective symptoms the gold-standard is patients self-report Hjermstad et al. Curr. Opin. Support.Palliat. Care 2009 Fayers PM, Machin D, editors. Quality of Life. The assessment, analysis and interpretation of patient-reported outcomes 2007

8 Background: 8 Assessment Numerical rating scale 0-10 (NRS-11) Body map

9 9 Commonly used terms in this presentation Symptom Cancer pain Domains Pain intensity Pain localisation Items NRS-11 Body map

10 Background. 10 Classification Classification systems in medicine International Classification of Diseases (ICD-10) Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) TNM (Tumour Node Metastasis) Classification of Malignant Tumors

11 Background 11 : Cancer pain classification Cancer pain classification For cancer pain, there is no international classification system that is widely used Caraceni et al. Oncology 2001 Fainsinger et al. Eur. J. Cancer 2010 Kaasa et al. BMJ Support. Palliat. Care 2011

12 Background: 12 EPCRC International collaboration needed European Palliative Care Research Collaborative EPCRC Among the aims: To improve treatment To develop an international, computer based assessment and classification system for pain, depression and cachexia in cancer patients

13 Background: 13 EPCRC Step wise research strategy of the EPCRC Assessment and classification system for cancer pain Empirical data Patients opinions Experts opinions Literature review Kaasa et al. J Clin Oncol 2008

14 14 Systematic literature review

15 15

16 Systematic 16 literature review Aim To identify existing approaches to cancer pain classification

17 Systematic 17 literature review Methods Systematic literature review Databases: MEDLINE and Embase English papers, adults Search terms: Pain Neoplasm / cancer Classification/categorisation/characterisation/grading/staging Screening of titles and abstract by four authors Reviewing of full-text papers by three authors

18 Systematic 18 literature review Results 692 hits -> 92 full-text papers (55 clinical studies) International Association for the Study of Pain Classification of Chronic Pain 1 Edmonton Classification System for Cancer Pain 2 Cancer Pain Prognostic Scale 3 Opioid Escalation Index 4 Prognostic tool for pain treatment 5 Pain Management Index 6 1.Merskey Fainsinger et al. Eur. J. Cancer Hwang et al. J. Pain Symptom Manage Mercadante et al. J. Pain Symptom Manage Mercadante et al. Pain Cleeland et al. N. Engl. J. Med. 1994

19 Systematic 19 literature review Results continued None of the systems are widely applied Domains identified in two or more of the systems: Pain intensity Breakthrough pain Pain mechanism Psychological distress Treatment response Assessment methods used varied across studies

20 20 Patients opinions

21 Patients 21 opinions

22 Patients 22 opinions Aims To investigate if the patients could verify the relevance of previously identifed pain domains To explore if domains were missing To gain in-depth understanding of pain domains

23 Patients 23 opinions Methods Qualitative study using semi-structured interviews with advanced cancer patients on opioids Quantitative part: Patients scoring of the importance of 12 domains relevant for cancer pain classification on an NRS-11

24 Patients 24 opinions Patients N = 33 Age (mean) 63 Male (n) 17 Female (n) 16 Karnofsky Performance Status (mean) 65 Gastro intestinal cancer (n) 11 Lung cancer (n) 8 Breast cancer (n) 4 Urological cancer (n) 8 Opioid dose (MEDD) mg (mean) 139

25 Patients 25 opinions Results Previously identified domains were confirmed to be relevant to the patients Poorer emotional and physical functioning were domains emphasised by the patients Sleep disturbances were important to the patients

26 Patients 26 opinions Results continued Ranking of domains importance by the patients 1. Etiology 2. Duration of pain 3. Pain intensity 4. Coping 5. Localisation of pain 6. Physical functioning 7. Psychological distress 8. Breakthrough pain 9. Congitive function 10. Neuropathic pain 11. Previous pain experience 12. Addiction

27 27 Experts opinions

28 Experts 28 opinions

29 Experts 29 opinions Aim To gain information on the most important/ relevant domains and items for a computerised pain assessment tool

30 Experts 30 opinions Methods Clinical / research experts (EPCRC, EAPC, IASP, WHO) N = 32, response rate = 72% Relevance rating of the previously ranked pain domains on an NRS-11

31 Experts 31 opinions Results and comparison Domain Patients ranking Experts ranking Etiology 1 - Duration 2 7 Intensity 3 1 Coping 4 10 Localisation 5 5 Physical functioning 6 6 Psychological distress 7 8 Breakthrough pain 8 2 Cognitive function 9 - Pain mechanism 10 4 Previous pain experience 11 9 Addiction 12 - Treatment, exacerbating - 3 and relieving factors

32 32 Empirical data I

33 Empirical 33 data EPOS

34 Empirical 34 data EPOS Aims To verify the relevance of domains identified in previous studies To explore new domains

35 Empirical 35 data EPOS European Pharmacogenetic Opioid Study (EPOS) Cross-sectional 11 European countries, 17 centres 2278 cancer patients on opioids evaluable Klepstad et al. Pain 2011

36 Empirical 36 data EPOS Patients EPOS N = 2278 Age (mean) 62 Male % 52.4 Female % 47.6 Karnofsky Performance Status (mean) 59 Gastro intestinal cancer % 23 Lung cancer % 18 Breast cancer % 13 Prostate cancer % 12 Pain intensity on the average (mean) 3.5 Pain intensity at its worst (mean) 5.25 Pain relief (mean) 74 Breakthrough pain % 58 Mixed pain % 34.2 Neuropathic pain % 4.8 Patients on opioids % 100 Opioid dose (MEDD) mg (mean) 341

37 Empirical 37 data EPOS Methods 46 independent domains/items were included based upon findings from previous studies Outcomes: Pain on average Pain at its worst Pain relief Bivariate correlation analysis Multivariate regression analysis with 21 domains/items

38 Empirical 38 data EPOS Results Breakthrough pain Psychological distress Pain mechanism Localisation of pain Opioid dose Use of non-opioids Sleep Addiction Cancer diagnosis Localisation of metastases R 2 = 12%-19%

39 39 Empirical data II

40 Empirical 40 data - CPOR

41 Empirical 41 data - CPOR Aims To confirm results from EPOS in an independent patient population To identify cancer pain predictors in a longitudinal study

42 Empirical 42 data - CPOR Cancer Pain Outcome Research (CPOR) Study Group Longitudinal 110 Italian centres 1801 cancer patients with pain Apolone et al. Br. J. Cancer 2009

43 Empirical 43 data - CPOR Methods - analysis of cross-sectional data Outcomes: Pain on average Pain at its worst Pain relief Multivariate regression analysis with the results from paper III as independent domains/items

44 Empirical 44 data - CPOR Methods analysis of longitudinal data 45 independent domains/items were included based upon findings from previous studies Outcomes on day 14: Pain on average Pain at its worst Pain relief Bivariate correlation analysis Multivariate regression analysis with 20 domains/items

45 Empirical 45 data - CPOR Results patients at study entry Sample A Crosssectional analysis N= 1529 Age (mean) Male % Female % Karnofsky Performance Status (mean) Gastro intestinal cancer % Lung cancer % Breast cancer % 17 9 Prostate cancer % 8 6 Pain intensity on the average (mean) Pain intensity at its worst (mean) Pain relief (mean) Breakthrough pain % Neuropathic pain % Patients on opioids % Opioid dose (MEDD) mg (mean) Sample B Longitudinal analysis N =352

46 Empirical 46 data - CPOR Results from cross-sectional data Breakthrough pain Localisation of pain Opioids Non-opioids Sleep R 2 = 21%-26%

47 Empirical 47 data - CPOR Results from longitudinal data Pain intensity at study entry Pain relief at study entry Breakthrough pain Localisation of pain Age Cancer diagnosis R 2 = 16%-24%

48 48 Conclusions

49 Conclusions 49 Conclusions from five EPCRC-publications There is no widely used international classification system for cancer pain Several aspects need to be considered when classifying cancer pain Pain domains Other subjective symptoms Patient s demographics Cancer disease

50 Conclusions 50 Identified domains relevant for cancer pain classification Pain intensity Pain relief Breakthrough pain Localisation of pain Neuropathic pain Opioids Psychological distress Non-opioids Sleep Addiction Cancer diagnosis Age Explained variance = 12% to 26 % Kaasa et al. BMJ Support Palliat Care 2011

51 Future 51 perspectives How do we proceed?

52 Future 52 perspectives Future perspectives What information can increase the explained variance? Agreement on domains to include and assessment methods to use is needed Prospective intervention studies are needed Continued international collaboration is needed

53 Future 53 perspectives Future perspectives continued An assessment and classification system needs to be combined with clinical treatment guidelines Implementation into clincal practice could be facilitated by information technology and clinical decision support systems

54 54 Acknowledgements The European Palliative Care Research Collaborative The European Palliative Care Research Centre The patients participating in the studies All researchers involved in the EPOS and CPOR studies All co-authors and contributors

55 55 EPCRC Thank you for your attention