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

Prospective Evaluation of Machine Learning Algorithms to Predict Postoperative Pain in Pedaitric Ambulatory Surgical Procedures

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Ambulatory Surgical Procedures; Pediatric Patient (1m-21y); Pediatric Pain; Pain, Acute Post-Operative

Detailed Description

Postoperative pain in early childhood remains a significant clinical challenge, particularly in ambulatory surgical practice. Children between one and three years of age represent a vulnerable population, as their limited ability to communicate makes pain assessment and management more complex. Unrecognized or undertreated pain at this developmental stage may prolong recovery and hospital durations.

Conventional perioperative risk assessments are constrained by their reliance on a limited number of clinical predictors and subjective judgment. Recent advances in computational science and machine learning have provided new opportunities to enhance predictive modeling in perioperative medicine. By integrating demographic, psychosocial, surgical, anesthetic, and physiological data, machine learning algorithms may detect intricate and non-linear relationships that surpass the predictive capacity of traditional statistical methods.

In this study, data will be prospectively collected from children undergoing ambulatory surgical procedures. Preoperative variables will include demographic characteristics and parental psychological status (STAI). Intraoperative variables will consist of surgical type, duration, and anesthetic management. Postoperative outcomes will focus on pain assessment (FLACC score) and physiological monitoring (saturation and pulse pressure). All data will be anonymized and recorded in a secure electronic database.

For data processing, rigorous quality control will be applied to minimize missing or inconsistent entries. The dataset will be randomly partitioned into training and test subsets. Multiple supervised machine learning algorithms will then be implemented to construct predictive models, with performance evaluated using standard classification metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (ROC-AUC). Cross-validation techniques will be employed to ensure model generalizability and to mitigate overfitting.

The ultimate aim of this research is to establish a reliable, data-driven predictive model for postoperative pain in young children, which may be incorporated into clinical decision-support frameworks. Such a model could facilitate individualized perioperative planning, optimize analgesic strategies, reduce the incidence of unanticipated adverse outcomes, and ultimately enhance both patient safety and parental satisfaction.

• Study Type: Observational
• Enrollment (Estimated): 90

Contacts and Locations

Study Locations

• Turkey (Türkiye)
  • Başakşehir
    • Istanbul, Başakşehir, Turkey (Türkiye), 34480
      • Başakşehir Çam ve Sakura Şehir Hastanesi

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Children aged 1 to 3 years scheduled for elective ambulatory surgical procedures under anesthesia at a single tertiary care center. Eligible participants will be consecutive patients meeting inclusion criteria, with parental informed consent obtained prior to enrollment.

Description

Inclusion Criteria:

• Children aged 1 to 3 years
• scheduled for ambulatory (day-case) surgical procedures under general anesthesia
• American Society of Anesthesiologists (ASA) Physical Status I-II
• informed consent obtained from parent/legal guardian

Exclusion Criteria:

• Known developmental delay or neurological disorder interfering with pain assessment
• Chronic analgesic or sedative medication use
• Emergency surgery cases
• Incomplete data or refusal of parental consent

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Pediatric Ambulatory Surgery (1-3 years); Children aged 1-3 years scheduled for day-case surgery. Demographic, psychosocial, surgical, anesthetic, and perioperative physiological variables are recorded. Postoperative pain is assessed twice in the recovery unit using the FLACC scale. No additional interventions beyond standard care.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Postoperative pain score (FLACC: Face, Legs, Activity, Cry, Consolability)	Pain intensity will be assessed using the FLACC scale (Face, Legs, Activity, Cry, Consolability), the pain score is a validated behavioral assessment tool used to evaluate pain in pediatric patients who are unable to communicate verbally. The scale consists of five observational categories: facial expression, leg movement, activity level, crying, and consolability. Each category is scored from 0 to 2, resulting in a total score ranging from 0 to 10, with higher scores indicating greater pain intensity. Pain severity was classified as no pain (0), mild pain (1-3), moderate pain (4-6), and severe pain (7-10).	Upon arrival to the post-anesthesia recovery room and 30 minutes after recovery room admission of the patient.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Heart rate (beats per minute)	Heart rate measured by standard monitor; recorded as a continuous variable at both time points.	Upon arrival to postoperative recovery room and 30 minutes after the recovery room admission.
Oxygen saturation (SpO₂, %)	Peripheral oxygen saturation measured by pulse oximetry; recorded as a continuous variable.	Upon arrival to postoperative recovery room and 30 minutes after the recovery room admission.
Change in pain score (ΔFLACC)	Difference between FLACC scores at 30 minutes and at arrival (30-min FLACC - arrival FLACC); positive values indicate increased pain.	Upon arrival to postoperative recovery room and 30 minutes after the recovery room admission
Parental anxiety (STAI-State)	arent/caregiver state anxiety measured with the STAI-State subscale (range 20-80); higher scores indicate greater anxiety. Administered to one primary caregiver; recorded as a continuous variable.	Preoperative (≤60 minutes before induction of anesthesia/surgery)

Collaborators and Investigators

• Sponsor: Başakşehir Çam & Sakura City Hospital

Study record dates

Study Major Dates

• Study Start (Actual): August 1, 2025
• Primary Completion (Estimated): November 30, 2026
• Study Completion (Estimated): November 30, 2026

Study Registration Dates

• First Submitted: November 28, 2025
• First Submitted That Met QC Criteria: November 28, 2025
• First Posted (Estimated): December 10, 2025

Study Record Updates

• Last Update Posted (Actual): December 31, 2025
• Last Update Submitted That Met QC Criteria: December 26, 2025
• Last Verified: December 1, 2025

More Information

Terms related to this study

• Keywords: machine learning; artificial intelligence; analgesia; postoperative pain; pediatric surgery; pediatric anesthesia; pain management; ambulatory surgery; day-case surgery; pain prediction
• Additional Relevant MeSH Terms: Pain; Neurologic Manifestations; Nervous System Diseases; Postoperative Complications; Pathologic Processes; Neurobehavioral Manifestations; Perceptual Disorders; Pathological Conditions, Signs and Symptoms; Signs and Symptoms; Pain, Postoperative; Agnosia
• Other Study ID Numbers: BS-ANES-GBA-01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No