Assessment of Postoperative Pain in Children With Facial Expression Analysis

NCT ID: NCT05345275

Last Updated: 2022-04-25

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 83 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2019-11-01
• Study Completion Date: 2021-06-30

Brief Summary

The present study was conducted to evaluate the use of computer-aided facial expression analysis to assess postoperative pain in children. The study population consisted of patients in the age group of 7-18 years who underwent surgery at Bursa Uludağ University Faculty of Medicine Health Application and Research Hospital Pediatric Surgery Clinic between November 2019 and June 2021. The sample of the study consisted of total 83 children who agreed to participate in the study and who met the sample selection criteria. Data were collected by the researcher using the Wong Baker Faces (WBS) pain rating scale and Visual Analog Scale (VAS). Data were collected from the child, mother, nurse, and one external observer. Facial action units associated with pain were used for machine estimation. OpenFace was used to analyze the child's facial action units and Python was used for machine learning algorithms. Intraclass correlation coefficient, Kappa coefficient, and linear regression analysis were used for statistical analysis of the data. The pain score predicted by the machine and the pain score assessments of the child, mother, nurse, and observer were compared.

Conditions

Pain, Postoperative

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: OTHER

Study Groups

pain assessments

In the study, primarily the pain assessment data of children were collected. In these pain assessments, assessments of the child, mother, nurse and an independent observer were taken. Simultaneously, a video recording of the child's facial expressions was made.

The facial expressions analyzed

Intervention Type: OTHER

The facial expressions associated with pain were analyzed by the researchers in the OpenFace program. The patient's pain scores were estimated in the Python program by combining the numerical outputs of facial expression analyses with machine learning algorithms.

Interventions

The facial expressions analyzed

The facial expressions associated with pain were analyzed by the researchers in the OpenFace program. The patient's pain scores were estimated in the Python program by combining the numerical outputs of facial expression analyses with machine learning algorithms.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• The patient is awake and oriented after the surgery,
• The patient must be within the first 12 hours after the operation and not be discharged before the first 36 hours after the operation,
• The patient is between the ages of 7-18,
• It is the mother's staying with the patient as a companion.

Exclusion Criteria

• Presence of any facial anomalies that may alter facial expression analysis,
• The patient has neurological and mental disorders,
• Having regular opiate use in the last 6 months,
• These are surgical procedures that require the patient to be in a prone position.

• Minimum Eligible Age: 7 Years
• Maximum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Uludag University

OTHER

Sponsor Role: lead

Responsible Party

Ayla Irem Aydin

Principal Investigator, PhD, RN

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Bursa Uludağ University

Bursa, , Turkey (Türkiye)

Countries

Turkey (Türkiye)

Other Identifiers

517517517

Identifier Type: -

Identifier Source: org_study_id