Machine Learning-Based Prediction of Postoperative Pain After Pediatric Ambulatory Surgery

This study aims to predict pain after surgery in children of ages 1 to 3 years by using computer programming (machine learning). Participant children will be observed before, during, and after surgery. Before surgery, the investigators will record each child's age, sex, weight, and the parent's level of anxiety using a short questionnaire (STAI: State Trait Anxiety Inventory). During surgery, the investigators will note the type of the surgery, how long it takes, and the medication given for pain relief. After surgery, the child's pain will be checked using the FLACC (Face, Legs, Activity, Cry, Consolability) scale, which assesses the child's face, legs, activity, crying, and how easy they are to comfort. For each assesment the children will be given points from 0 to 2. Pain will be measured 2 times. Firstly when the child reaches to the postoperative recovery room after they are monitorized. Secondly after 30 minutes spending in recovery room. At both times the pain scores and vital signs (pulse pressure and saturation) will be noted. No additional medication or intervention will be done throughout the study. All information will be stored without names or personal details. A computer model will study 80% of the data and then test itself on the remaining 20% of the collected data to see how well it can predict pain.

NCT Number	NCT07274995
Lead Sponsor	Başakşehir Çam & Sakura City Hospital
Conditions	Pain, Acute Post-Operative, Ambulatory Surgical Procedures, Pediatric Pain, Pediatric Patient (1m-21y)
Enrollment	90 participants
Start Date	2025-08-01
Primary Completion	2026-11-30 (estimated)
Study Completion	2026-11-30 (estimated)
Updated on ClinicalTrials.gov	2025-12-31