Efficiency of Facial Expression Diagnostic System in Pain Assessment After Geriatric Surgery

NCT05756140 | Unknown

• 1 other identifier: 2022-16-09
• Study Type: observational
• Target: 68
• Locations: 1 country
• Sites: 1

Brief Summary

By integrating the methods used in the assessment of pain in geriatric surgery patients with literature, theory and research, this study aims to: evaluate the effectiveness of the facial diagnosis system in the evaluation of pain after geriatric surgery. The research hypotheses are as follows: H1: In the evaluation of pain after geriatric surgery, there is a concordance between the pain score evaluated by the patient and the pain score obtained from facial expression diagnostic system analysis. H1: In the evaluation of pain after geriatric surgery, there is a correlation between the pain score evaluated by the nurse and the pain score obtained from the analysis of the facial expression diagnosis system. H1: In the evaluation of pain after geriatric surgery, there is a correlation between the pain score evaluated by the patient and the pain score evaluated by the nurse.

Conditions

Pain; Aging Problems

Keywords

Geriatric surgery; Pain assessment; Facial diagnosis system

Outcome Measures

Primary Outcomes (2)

• Determination of the average scores from the "Wong Baker Facial Pain Scale"

  Wong Baker Facial Pain Scale: It consists of 6 different facial expressions, starting with a smiling face and ending with a crying face. The patient and the nurse will be asked to give a pain score to the patient when immediately after the patient comes from the surgery and 1 hour after coming to the service.

  6 months

• Determination of the average scores from the "Numerical Rating Scale"

  Numerical Rating Scale: Patients are asked to draw a number from 0 to 10, 0 to 20, or 0 to 100 that best fits pain intensity. Zero usually indicates "no pain", while the upper limit represents "unbearable pain". The patient and the nurse will be asked to give a pain score to the patient when immediately after the patient comes from the surgery and 1 hour after coming to the service.

  6 months

Secondary Outcomes (1)

• Machine learning will be provided using a computer-assisted facial expression recognition system.

  12 months

Study Arms (1)

Patients Group

Geriatric patients in the postoperative period hospitalized in surgical wards

Other: No intervention

Interventions

No intervention OTHER

No Control

Patients Group

Eligibility Criteria

• Age: 65 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The research sample will consist of geriatric patients aged 65 and over who were hospitalized in the post-operative general surgery service.

You may qualify if:

• The patient is 65 years or older,
• According to the American Society of Anesthesiologists (ASA) classification; Being in class -II and III; ASA II: Patient with mild systemic disease, ASA III: Patient with severe systemic disease but not affecting activities of daily living.
• Willingness to participate in the research,
• The patient is awake and oriented after the surgery,
• The patient is not discharged within the first 24 hours after the operation,

You may not qualify if:

• Not being willing to participate in the research,
• The presence of any facial anomalies that may change the facial expression analysis of the patient,
• The patient's regular use of opiates in the last 6 months,
• The patient has undergone surgical intervention that requires not being in the prone or semifowler position,
• The patient is not awake and oriented after the surgery,
• Discharge of the patient within the first 24 hours after surgery.

Sponsors & Collaborators

• Zonguldak Bulent Ecevit University: lead

Study Sites (1)

Zonguldak Ataturk State Hospital

Zonguldak, Centre, 67100, Turkey (Türkiye)

RECRUITING

Related Publications (1)

• Kur Alkant T, Tasdemir N. Testing Machine Learning-Based Pain Assessment for Postoperative Geriatric Patients. Comput Inform Nurs. 2025 Nov 1;43(11):e01248. doi: 10.1097/CIN.0000000000001248.

  PMID: 39761361 DERIVED

MeSH Terms

Conditions

Pain

Condition Hierarchy (Ancestors)

Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Officials

• Tülin KURT ALKAN, Expert

  https://zonguldakataturkdh.saglik.gov.tr/?_Dil=1

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Tülin KURT ALKAN, Expert

CONTACT

+905393711902; tulinkurt0@gmail.com

Nurten TASDEMİR, Doc. Dr.

CONTACT

+905072344018; ntasdemir@gmail.com

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Doctoral Candidate in Surgical Nursing Science

Study Record Dates

• First Submitted: February 16, 2023
• First Posted: March 6, 2023
• Study Start: October 1, 2022
• Primary Completion: June 30, 2023
• Study Completion: June 30, 2024
• Last Updated: January 23, 2024

Record last verified: 2024-01

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)