Ignite Creation Date:
2025-12-24 @ 2:04 PM
Ignite Modification Date:
2026-01-06 @ 2:54 AM
Study NCT ID:
NCT07274995
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-12-10
First Post:
2025-11-28
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Machine Learning Prediction of Postoperative Pain in Pediatric Ambulatory Surgery
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D010149', 'term': 'Pain, Postoperative'}, {'id': 'D000377', 'term': 'Agnosia'}], 'ancestors': [{'id': 'D011183', 'term': 'Postoperative Complications'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}, {'id': 'D010146', 'term': 'Pain'}, {'id': 'D009461', 'term': 'Neurologic Manifestations'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D010468', 'term': 'Perceptual Disorders'}, {'id': 'D019954', 'term': 'Neurobehavioral Manifestations'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 90}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'ACTIVE_NOT_RECRUITING', 'startDateStruct': {'date': '2025-08-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-11', 'completionDateStruct': {'date': '2026-11-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-11-28', 'studyFirstSubmitDate': '2025-11-28', 'studyFirstSubmitQcDate': '2025-11-28', 'lastUpdatePostDateStruct': {'date': '2025-12-10', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-12-10', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2026-11-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Postoperative pain score (FLACC)', 'timeFrame': 'Upon arrival to the post-anesthesia recovery room and 30 minutes after recovery room admission of the patient.', 'description': 'Pain intensity will be assessed using the FLACC scale (Face, Legs, Activity, Cry, Consolability), which ranges from 0 to 10. Higher scores indicate more severe pain.'}], 'secondaryOutcomes': [{'measure': 'Heart rate (beats per minute)', 'timeFrame': 'Upon arrival to postoperative recovery room and 30 minutes after the recovery room admission.', 'description': 'Heart rate measured by standard monitor; recorded as a continuous variable at both time points.'}, {'measure': 'Oxygen saturation (SpO₂, %)', 'timeFrame': 'Upon arrival to postoperative recovery room and 30 minutes after the recovery room admission.', 'description': 'Peripheral oxygen saturation measured by pulse oximetry; recorded as a continuous variable.'}, {'measure': 'Change in pain score (ΔFLACC)', 'timeFrame': 'Upon arrival to postoperative recovery room and 30 minutes after the recovery room admission', 'description': 'Difference between FLACC scores at 30 minutes and at arrival (30-min FLACC - arrival FLACC); positive values indicate increased pain.'}, {'measure': 'Parental anxiety (STAI-State)', 'timeFrame': 'Preoperative (≤60 minutes before induction of anesthesia/surgery)', 'description': '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.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['postoperative pain', 'pain prediction', 'machine learning', 'artificial intelligence', 'pediatric surgery', 'ambulatory surgery', 'day-case surgery', 'analgesia', 'pain management', 'pediatric anesthesia'], 'conditions': ['Pain, Acute Post-Operative', 'Ambulatory Surgical Procedures', 'Pediatric Pain', 'Pediatric Patient (1m-21y)']}, 'descriptionModule': {'briefSummary': "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.\n\nBefore surgery, we will record each child's age, sex, weight, and the parent's level of anxiety using a short questionnaire (STAI: State Trait Anxiety Inventory).\n\nDuring surgery, we will note the type of the surgery, how long it takes, and the medication given for pain relief.\n\nAfter surgery, we will check the child's pain 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. 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.\n\nAll 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.", 'detailedDescription': '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.\n\nConventional 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.\n\nIn 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.\n\nFor 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.\n\nThe 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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD'], 'maximumAge': '3 Years', 'minimumAge': '1 Year', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Children aged 1 to 3 years\n* scheduled for ambulatory (day-case) surgical procedures under general anesthesia\n* American Society of Anesthesiologists (ASA) Physical Status I-II\n* informed consent obtained from parent/legal guardian\n\nExclusion Criteria:\n\n* Known developmental delay or neurological disorder interfering with pain assessment\n* Chronic analgesic or sedative medication use\n* Emergency surgery cases\n* Incomplete data or refusal of parental consent'}, 'identificationModule': {'nctId': 'NCT07274995', 'briefTitle': 'Machine Learning Prediction of Postoperative Pain in Pediatric Ambulatory Surgery', 'organization': {'class': 'OTHER_GOV', 'fullName': 'Başakşehir Çam & Sakura City Hospital'}, 'officialTitle': 'Prospective Evaluation of Machine Learning Algorithms to Predict Postoperative Pain in Pedaitric Ambulatory Surgical Procedures', 'orgStudyIdInfo': {'id': 'BS-ANES-GBA-01'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Pediatric Ambulatory Surgery (1-3 years)', 'description': '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.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '34480', 'city': 'Istanbul', 'state': 'Başakşehir', 'country': 'Turkey (Türkiye)', 'facility': 'Başakşehir Çam ve Sakura Şehir Hastanesi', 'geoPoint': {'lat': 41.01384, 'lon': 28.94966}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Başakşehir Çam & Sakura City Hospital', 'class': 'OTHER_GOV'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate Professor Doctor', 'investigatorFullName': 'Muzaffer GENCER', 'investigatorAffiliation': 'Başakşehir Çam & Sakura City Hospital'}}}}