The Evaluation With Artificial Neural Network of Pain Scales in Children (ANN)

• Conditions: Pain

• Registration Number: NCT02682875
• Lead Sponsor: Cukurova University

Brief Summary

The study evaluates with Artificial Neural Network (ANN) of pain scales in children. Pain of these patients' will be evaluated by many pain scales in the postoperative period.

Detailed Description

Pain of these patients' will be evaluated by many pain scales in the postoperative period. These scales include OUCHER, Visiuel Analog Scale, FLACC, Faces Pain Scale revise, Wong Baker Faces Scale, Faces Pain Scale, Numeric rating Scale, Verbal Rating Scale, CHEOPS and also age, blood pressure, respiratuar rate, heart rate will be recorded.

In the first step of this study, the parameters taken into account on widely used each pain scale practically and importance level of each parameter will be examined. The parameters taken into account in determining the degree of pain and degree of each pain scale will be recorded. Independent t-test of the pain scales will be analyzed whether different statistically. Pain scales used in the application at conclusion statistical analysis will be grouped.

In addition, parameters considered to be effective on pain (pulse, blood pressure, etc.) will be determined and also recorded. The degree of importance on pain of the current scales and other parameters to be determined and as result, new pain scale will be created. While determining the the level of importance will be utilized from the analytic hierarchy process (AHP). Making binary comparisons between AHP and parameters, the level of importance and weightiness score of each parameter will be determined. The mathematical formulation of the pain points will be presented considering AHP weightiness score of each parameter.

Although pain scores obtained mathematically, pain score with Artificial Neural Network (ANN) will be estimated considering the parameters that impact on the pain score.

The final step, the creation of new pain scale comparing the pain scores obtained by mathematical formulation and Artificial Neural Network and, it is intended to be compared with the current scales.

Study Timeline

• Study Start: 2015-11
• Study First Submitted: 2016-01-27
• Status Verified: 2016-02
• Study First Submitted QC: 2016-02-10
• Last Update Submitted: 2016-02-10
• Study First Posted: 2016-02-17
• Last Update Posted: 2016-02-17
• Primary Completion: 2016-03
• Study Completion: 2016-05

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 140

Inclusion Criteria

• 2 months -18 years
• Children undergoing elective surgery

Exclusion Criteria

• Children with mental and motor development retardation
• Patients scheduled emergency surgery
• Patients and parents, who refused to participate in this study

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Artificial Neural Network	4 months	the creation of new pain scale

Trial Locations

Locations (1)

Zehra Hatipoğlu; 🇹🇷 Adana, Turkey