Pain, Acute Post-OperativeNCT07274995
Machine Learning-Based Prediction of Postoperative Pain After Pediatric Ambulatory Surgery
An observational study of Pain, Acute Post-Operative and Ambulatory Surgical Procedures, run by Başakşehir Çam & Sakura City Hospital at 1 site in Turkey (Türkiye).
Trial summary
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. Read more about this study
- Who is it for?
- Ages 1 year to 3 years
- Conditions the study is for
- Pain, Acute Post-Operative, Ambulatory Surgical Procedures, Pediatric Pain
- How many people
- 90 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Aug 2025to 30 Nov 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 1 year to 3 years
- All sexes
- No healthy volunteers
- Child
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.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Başakşehir Çam ve Sakura Şehir Hastanesi
Istanbul, Başakşehir, 34480
About this study
Official title: 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).
Read more
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.
Read the 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.
- Conditions
- Pain, Acute Post-Operative, Ambulatory Surgical Procedures, Pediatric Pain, Pediatric Patient (1m-21y)
- Keywords
- postoperative pain, pain prediction, machine learning, artificial intelligence, pediatric surgery, ambulatory surgery, day-case surgery, analgesia, pain management, pediatric anesthesia
- Medical subject headings
- Pain, Postoperative, Agnosia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: Upon arrival to the post-anesthesia recovery room and 30 minutes after recovery room admission of the patient.
Secondary outcomes (4)
Heart rate (beats per minute)
Heart rate measured by standard monitor; recorded as a continuous variable at both time points.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Preoperative (≤60 minutes before induction of anesthesia/surgery)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Dec 2025
- Study start1 Aug 2025
- Primary completion30 Nov 2026 (estimated)
- Study completion30 Nov 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pain, Acute Post-OperativeStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 90 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Başakşehir Çam & Sakura City Hospital
- Sponsor type
- Other government
- Responsible party
- Principal investigator, Muzaffer GENCER, Başakşehir Çam & Sakura City Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07274995
- Sponsor's protocol ID
- BS-ANES-GBA-01
- Status last verified
- Dec 2025
- First submitted
- 28 Nov 2025
- Last updated
- 31 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 31 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Evaluation of Machine Learning Algorithms to Predict Postoperative Pain in Pedaitric Ambulatory Surgical Procedures
- ClinicalTrials.gov ID
- NCT07274995
- Acronym / sponsor ID
- BS-ANES-GBA-01
- Conditions
- Pain, Acute Post-Operative, Ambulatory Surgical Procedures, Pediatric Pain, Pediatric Patient (1m-21y)
- Keywords
- postoperative pain, pain prediction, machine learning, artificial intelligence, pediatric surgery, ambulatory surgery, day-case surgery, analgesia
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 90 participants (estimated)
- Lead sponsor
- Başakşehir Çam & Sakura City HospitalOther government
- Study start
- 1 Aug 2025 (actual)
- Primary completion
- 30 Nov 2026 (estimated)
- Study completion
- 30 Nov 2026 (estimated)
- First posted
- 10 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 1 year to 3 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child
- 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.
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Postoperative pain score (FLACC: Face, Legs, Activity, Cry, Consolability)
Time frame: Upon arrival to the post-anesthesia recovery room and 30 minutes after recovery room admission of the patient.
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).
4 secondary outcomes
Heart rate (beats per minute)
Time frame: Upon arrival to postoperative recovery room and 30 minutes after the recovery room admission.
Oxygen saturation (SpO₂, %)
Time frame: Upon arrival to postoperative recovery room and 30 minutes after the recovery room admission.
Change in pain score (ΔFLACC)
Time frame: Upon arrival to postoperative recovery room and 30 minutes after the recovery room admission
Parental anxiety (STAI-State)
Time frame: Preoperative (≤60 minutes before induction of anesthesia/surgery)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 Dec 2025.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pain, Acute Post-Operative and Ambulatory Surgical Procedures, run by Başakşehir Çam & Sakura City Hospital at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 31 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Postoperative pain score (FLACC: Face, Legs, Activity, Cry, Consolability) (time frame: Upon arrival to the post-anesthesia recovery room and 30 minutes after recovery room admission of the patient.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 31 Dec 2025.