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

Abstract

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.

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“…The Questionnaire was developed with guidance from the “Intelligent Patient-Controlled Pain Management Expert Consensus”, 12 and relevant literatures. 3 , 13 , 14 The initial draft underwent revisions based on feedback from two senior experts specializing in anesthesia and thoracic surgery both holding the title of Chief Physician. Subsequently, a preliminary trial was conducted on a limited scale (n=48), resulting in a Cronbach’s alpha coefficient value of 0.875, indicating good internal consistency.…”

Continuous wavelet transform and higher-order spectrum: combinatory potentialities in breath sound analysis and electroencephalogram-based pain characterization

“…Objective pain characterization is essential and has long been desired for clinical pain assessment and management, as subjective perception of the feeling of pain is difficult to quantify [28]. Towards such endeavour, different approaches have been explored so far, with the most commonly one the use of pain intensity measures, including visual analogue scales, numerical rating scales (NRSs) and verbal rating scales [29]. Nevertheless, they are characterized by intrinsic limitations, as they require human intervention (e.g.…”

An Innovative EEG-Based Pain Identification and Quantification: A Pilot Study

“…Among other issues related to this important global health problem, assessing chronic pain remains a challenge. Traditionally, pain assessment relies on hetero- or self-reported pain [ 4 , 5 , 6 ]. Although verbal description is only one of several behaviors to express pain, these possibilities for assessing chronic pain unfortunately lose all their robustness when it comes to people who have difficulty expressing themselves, such as the older, those with cognitive disorders, or those suffering from a psychological disorder [ 7 , 8 ].…”