Multifaceted Model of Pain Components

2. Pain Behavior Suffering Pain Nociception Multifaceted Model of Pain Components Chronic Acute

3. Four Dimensions of Pain • Sensory-discriminative • Motivational-affective • Cognitive-evaluative • Social-behavioral

4. Problem: Detecting pain • Subjective & no direct measurement • Clinical: Visual Analogue Scales (VAS) • Numerical Rating Scales (NRS) • Verbal Rating Scales (VRS) • Questionnaire

5. Descriptivepain scales Mild Moderate Severe No pain Worst possible pain Numeric pain scales 0 10 Worst possible pain 1 2 3 4 5 6 7 8 9 No pain 0 Visual analog scale (VAS) 10 Pain as worst as it could be No pain Pain Intensity Scales

6. 疼痛強度  完全不痛  輕微痛  非常痛  無法忍受 疼痛種類  酸脹  痠軟  緊繃  抽筋  冰冷  灼熱  放電  切割  麻癢  撕裂 完全不痛 無法忍受 正面 VAS VRS 疼痛量尺 (正面)

7. 完全不痛 無法忍受 反面 疼痛量尺 (反面)

8. Evaluation: face scales

9. McGill Pain Questionnaire • 4 groups of descriptors: • Sensory: 1-10 • Affective: 11-15 • Evaluative: 16 • Miscellaneous: 17-20 • Pain rating index (PRI) • Present pain intensity (PPI): 0-5

10. The Disadvantage of these methods: • Static Pain (not Dynamic Pain) • Maybe, the VAS values are similar but • the efforts are totally different • Time Consuming • The On-line Analyzing Pain is impossible Pain has become the “Fifth vital Sign” Patient’s chart : BP, HR, RR, and T

11. The functions of the PCA • Administer small bolus doses of • Pain-control drugs • At fixed intervals • Controlled by the patient • with the push of a button

12. VAS NRS VRS Button-pressing profile PCA Patient pain scores (between 0~10) controlled by patient (a) BP Pain Pattern Fuzzy Model Algorithm SP FPI index (between0~10) ZP pain Button-pressing profile PCA Patient (b) bolus infusion Figure1. Block diagram of modelling pain system to interpret the pain measurement (a) Conventional PCA system; (b) PCA+FPI system

13. i-Pain System: • Information, • Intelligent, • Sound similar to feeling pain

14. A Hierarchical Level of i-Pain System FPI Pain Pattern Mean Drug C. D/D Ratio Data Mining PCA Pain Database Internet Data in Computer PCA PDA Drug Demand by Patient 4th Level Clinical Data Analysis 3rd Level 2nd Level Clinical Data Acquisition 1st Level

15. PCA病患自控止痛系統 The i-Pain System Designed in Post-OP: • 資料接收 • 病患基本資料 • 檢視及輸出 • 調劑紀錄 • 選項 • 副作用 • 巡房紀錄 • 上傳至Server (via Internet)

16. 病患基本資料

17. 檢視及輸出

18. 調劑紀錄

19. 選項

20. 副作用

21. 巡房紀錄

22. (a) Pain Pattern: The definition of BP, SP, and ZP BP (Big Pain or Severe Pain): Two buttons are pushed at least during 4 lockout intervals SP (Small Pain or Little Pain): A button is pushed during 4 lockout intervals ZP: (Zero Pain or No Pain): No any button is pushed during 4 lockout intervals .

23. 75% Overlapped Calculation

24. =Delivery =Demand =Delivery =Demand • For 4 Lockout and Overlap：BP、BP、BP、BP • BP = 100%、SP=0%、ZP=0% Delivery：1mg • For 4Lockout and Overlap：SP、ZP、SP、BP • BP = 25%、SP=50%、ZP=25%

25. Why Fuzzy Logic in i-Pain System ? Anaesthetists Use: • rules of thumb • imprecise information • personal rules IFPain_intensity is Big_pain THENset the Morphine infusion rate to a HIGH LEVEL

26. Fuzzy Pain Intensity (FPI) Index Big Pain Fuzzy System Small Pain FPI index Zero Pain

27. Membership Function: Input Membership Function (9 Levels) Output Membership Function (11 Levels)

28. Fuzzy Rules: 36

29. Fuzzy Inference Engine: (1) (2) (3) (4) (5) (6) (7)

30. Fuzzy Defuzzification: Center of area (COA): Where M is the membership function; U is the universe of discourse; n is the number of rules.

31. =Delivery =Demand (b) PCA D/D Ratio: Demand/Delivery Ratio：Delivery=4，Demand=7；Demand/Delivery Ratio=7/4=1.75 (c) PCA Mean Drug Consumption:

32. Patients and Results: (a) Drugs: Morphine , Morphine + Keto for 1 & 2 ml

33. (b) Drugs: Morphine 1 ml or Morphine 2 ml with Different lockout time (6 or 10 min)

34. (c) Different Surgical Operations

35. A Hierarchical Level of i-Pain System FPI Pain Pattern Mean Drug C. D/D Ratio Data Mining PCA Pain Database Internet Data in Computer PCA PDA Drug Demand by Patient 4th Level Clinical Data Analysis 3rd Level 2nd Level Clinical Data Acquisition 1st Level

36. PDA for Interviewing Patients Applied in the assessment of postoperative pain

37. The Advantages of Using PDA for Interviewing Patient • Small，Light，Portable • High Technology、Fancy • Search and save data easily • Data can be shown in Figures • Pain Location : Displaying an anterior and posterior view of the body • VAS Score: Displaying an VAS image to the patient

38. Data Communications: PCA USB RS232 PDA IrDA Computer RF

39. PDA as a Messenger for Data Collection PC PCA PDA

40. Conventional Interviewing System(Paper Pain Diary) Current Interviewing System

41. PDA Interviewing System(Electronic Pain Diary)

42. Recent works: • Fuzzy D/D Ratio Index (2nd Dimen.) • Mining the Web Information • for Actionable Knowledge • (Drug Amount, Different Drug, • Lockout Time, Timing After OP) • The Real Time of FP Index RS-232 Receive Tablet PC PCA

43. Future works: • Acute Pain Center for • Multicenter Research • Share these technologies, • Information & Intelligence • for this area of research • Data Mining & Warehousing • Chronic Pain (Ex. Cancer Pain)