A Novel Fuzzy Pain Demand Index Derived From Patient-Controlled Analgesia for Postoperative Pain

A multilayer hierarchical structure for an intelligent analysis system is described in this paper. Four levels (patients', measurement, Web-based, and interpreting) are used to collect massive amounts from clinical information and analyze it with both traditional and artificial intelligent methods. To support this, a novel fuzzy pain demand (FPD) index derived from the interval of each bolus of patient-controlled analgesia (PCA) is designed and documented in a large-scale clinical survey. The FPD index is modeled according to a fuzzy modeling algorithm to interpret the self-titration of the drug delivery. A total of 255 patients receiving intravenous PCA using morphine (1 mg/ml) tested this index by offline analysis from this system. We found the FPD index modeled from a fuzzy modeling algorithm to interpret the self-titration of the drug delivery can show the patients' dynamic demand and past efforts to overcome the postoperative pain. Moreover, it could become an online system to monitor patients' demand or intent to treat their pain so these factors could be entered into a patient's chart along with temperature, blood pressure, pulse, and respiration rates when medical practitioners check the patients.

[1]  Derek A. Linkens,et al.  Hierarchical rule-based and self-organizing fuzzy logic control for depth of anaesthesia , 1999, IEEE Trans. Syst. Man Cybern. Part C.

[2]  Shigeo Abe,et al.  Neural Networks and Fuzzy Systems , 1996, Springer US.

[3]  L. V. von Segesser,et al.  Pain pattern and left internal mammary artery grafting. , 2000, The Annals of thoracic surgery.

[4]  R. Ohrbach,et al.  Five-year outcomes in TMD: relationship of changes in pain to changes in physical and psychological variables , 1998, Pain.

[5]  Paul P Stork,et al.  A randomized trial of electronic versus paper pain diaries in children: impact on compliance, accuracy, and acceptability , 2004, Pain.

[6]  Xiang Wang,et al.  DESIGN A HIERARCHICAL SYSTEM FOR MONITORING MOBILITY CHANGES OF THE ELDERLY USING INTELLIGENT ANALYSIS , 2005 .

[7]  S. Barnason,et al.  Managing pain: the fifth vital sign. , 2000, The Nursing clinics of North America.

[8]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[9]  R. Melzack.  The McGill Pain Questionnaire: Major properties and scoring methods , 1975, PAIN.

[10]  Stanton A. Glantz,et al.  Primer of biostatistics : statistical software program version 6.0 , 1981 .

[11]  W. Clark,et al.  Unidimensional pain rating scales: a multidimensional affect and pain survey (MAPS) analysis of what they really measure , 2002, Pain.

[12]  Samuel D. Stearns,et al.  Signal processing algorithms in MATLAB , 1996 .

[13]  Derek A. Linkens,et al.  Fuzzy logic for auditory evoked response monitoring and control of depth of anaesthesia , 1998, Fuzzy Sets Syst..

[14]  S. Shiffman,et al.  Patient compliance with paper and electronic diaries. , 2003, Controlled clinical trials.

[15]  G N Kenny,et al.  Patient-maintained analgesia with target-controlled alfentanil infusion after cardiac surgery: a comparison with morphine PCA. , 1998, British journal of anaesthesia.

[16]  C. Cleeland,et al.  Development of the Wisconsin Brief Pain Questionnaire to assess pain in cancer and other diseases , 1983, Pain.

[17]  Derek A. Linkens,et al.  A hierarchical system of on-line advisory for monitoring and controlling the depth of anaesthesia using self-organizing fuzzy logic , 2005, Eng. Appl. Artif. Intell..

[18]  Thomas D. Walsh.  Letter to the editor London, 13 September 1983 , 1984, Pain.

[19]  J. Shieh,et al.  Pain model and fuzzy logic patient-controlled analgesia in shock-wave lithotripsy , 2006, Medical and Biological Engineering and Computing.

[20]  D. Turk,et al.  Neglected topics in chronic pain treatment outcome studies: determination of success , 1993, Pain.

[21]  J. Dombi.  Membership function as an evaluation , 1990 .

[22]  Jesse A. Berlin,et al.  Defining the clinically important difference in pain outcome measures , 2000, PAIN.

[23]  Ronald Melzack,et al.  The short-form McGill pain questionnaire , 1987, Pain.

[24]  Maysam F. Abbod,et al.  Survey of utilisation of fuzzy technology in Medicine and Healthcare , 2001, Fuzzy Sets Syst..

[25]  Ebrahim H. Mamdani,et al.  A linguistic self-organizing process controller , 1979, Autom..

[26]  M. Braae,et al.  FUZZY RELATIONS IN A CONTROL SETTING , 1978 .

[27]  Henrik Kehlet,et al.  Does an Acute Pain Service Improve Postoperative Outcome? , 2002, Anesthesia and analgesia.

[28]  T. J. Wasylak,et al.  Reduction of post-operative morbidity following patient-controlled morphine , 1990, Canadian journal of anaesthesia = Journal canadien d'anesthesie.

[29]  A. Gift,et al.  Visual Analogue Scales: Measurement of Subjective Phenomena , 1989, Nursing research.

[30]  Derek A. Linkens,et al.  Hierarchical fuzzy modelling for monitoring depth of anaesthesia , 1996, Fuzzy Sets Syst..

[31]  Derek A. Linkens,et al.  A computer screen-based simulator for hierarchical fuzzy logic monitoring and control of depth of anaesthesia , 2004, Math. Comput. Simul..

[32]  Madan Gupta,et al.  Multivariable Structure of Fuzzy Control Systems , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[33]  D. Goldstein,et al.  A Comparison of Paper with Electronic Patient-Completed Questionnaires in a Preoperative Clinic , 2005, Anesthesia and analgesia.

[34]  M. Mccaffery,et al.  Pain ratings: the fifth vital sign. , 1997, The American journal of nursing.