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

Overview

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.

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.