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Currently submitted to: JMIR Research Protocols

Date Submitted: Jan 12, 2020
Open Peer Review Period: Jan 12, 2020 - Mar 8, 2020
(currently open for review)

A Prospective Study Evaluating a Pain Assessment Tool in Postoperative Environment: Protocol for Algorithm Testing and Enhancement

  • Emad Kasaeyan Naeini; 
  • Mingzhe Jiang; 
  • Elise Syrjälä; 
  • Michael-David Calderon; 
  • Riitta Mieronkoski; 
  • Kai Zheng; 
  • Nikil Dutt; 
  • Pasi Liljeberg; 
  • Sanna Salanterä; 
  • Ariana Nelson; 
  • Amir M Rahmani; 

ABSTRACT

Background:

Pain assessment is critical to the optimal treatment of pain. There is a high demand for accurate objective pain assessment for effectively optimizing pain management interventions. However, pain as a multivalent, dynamic, and ambiguous phenomenon is difficult to quantify, particularly when the patient’s ability to communicate is limited. The “gold standard” of pain intensity assessment is self-reporting. However, this unidimensional model is disparaged for its oversimplification and limited applicability in several vulnerable patient populations. Researchers have attempted to develop objective pain assessment tools through analysis of physiological pain indicators, such as Electrocardiography (ECG), Electromyography (EMG), Plethysmography (PPG), and Electrodermal Activity (EDA). However, pain assessment by using only these signals can be unreliable, as there are various other factors that alter these vital signs and the adaptation of vital signs to pain stimulation varies from person to person. Objective pain assessment using behavioral signs such as facial expressions has recently gained attention. Several techniques in this category, such as those using computer vision to extracting features from patients’ head-pose, suffer from feasibility issues in clinical settings due to privacy and practical barriers to deployment.

Objective:

This paper aims to develop an automatic and versatile pain assessment tool algorithm for detection and assessment of pain in a reliable and objective way in non-communicative patients through observational data collection by wearable technologies, measuring facial EMG, ECG, PPG, and EDA.

Methods:

This study was planned to be done in three different phases: (1) Evaluation and Test of usability, utility, and accuracy of the new pain assessment tool in 30 healthy working-age volunteers, (2) Further development and research of a pain assessment tool in patients likely experiencing mild to moderate pain, and (3) Conduct a trial to assess the effectiveness of the whole platform in uncommunicative patients at two different sites which serve as the sites of both intervention and control group. Currently available state-of-the-art standard sensors were used to measure bioelectrical EMG signals as well as changes in heart rate, respiratory rate, and oxygen saturation. Based on the results, the pain assessment tool was further developed and reconstituted with modern wearable sensors, devices, and algorithms. In this paper, we focus on the second part of the study. HUMAN RESEARCH PROTECTIONS Application for IRB Review (APP) was approved for this paper.

Results:

The development of the pain assessment tool is calculated to be ready in early 2020. Preliminary results will be ready for publication from Fall 2019.

Conclusions:

This paper is about the second phase of research on multimodal signals including facial muscle electrical activity, cardiac electrical activity, and electrodermal activity as indicators of pain. This paper will allow testing the smart pain assessment tool in uncommunicative patients in a multicenter, multinational setting in California/USA and Turku/Finland to promote pain management of patients and enhance the safety and quality of care.


 Citation

Please cite as:

Kasaeyan Naeini E, Jiang M, Syrjälä E, Calderon M, Mieronkoski R, Zheng K, Dutt N, Liljeberg P, Salanterä S, Nelson A, Rahmani AM

A Prospective Study Evaluating a Pain Assessment Tool in Postoperative Environment: Protocol for Algorithm Testing and Enhancement

JMIR Preprints. 12/01/2020:17783

DOI: 10.2196/preprints.17783

URL: https://preprints.jmir.org/preprint/17783


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