Clinical trial NCT02682875: The Evaluation With Artificial Neural Network of Pain Scales in Children (ANN)
Official title: The Evaluation With Artificial Neural Network of Pain Scales in Children
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Pain
Lead sponsor: Cukurova University
Sponsor class: OTHER
First posted: 2016-02-17
Enrollment: 140
Sex: ALL
Minimum age (years): 2
Maximum age (years): 18
Standard ages: CHILD
ADULT
Accepts healthy volunteers: yes
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: pediatric patients between 2 months to 18 years undergoing any surgery was included on this study. 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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Artificial Neural Network
timeFrame: 4 months
Secondary outcomes:
Other outcomes:
Locations:
city: Adana
country: Turkey
facility: Zehra Hatipoğlu
Study officials:
affiliation: Çukurova University
name: Dilek Özcengiz
role: PRINCIPAL_INVESTIGATOR
References:
citation: Azimi P, Mohammadi HR, Benzel EC, Shahzadi S, Azhari S, Montazeri A. Artificial neural networks in neurosurgery. J Neurol Neurosurg Psychiatry. 2015 Mar;86(3):251-6. doi: 10.1136/jnnp-2014-307807. Epub 2014 Jul 1.