Risk AssessmentAINCT06507384
AI Risk Assessment Model for Complication Prevention in Plastic Surgery (Artificial Intelligence)
An observational study of Risk Assessment and Risk Factors, run by Bukret Plastic Surgery at 1 site in Argentina.
Trial summary
The goal of this observational study is to determine if an AI-based risk assessment model can help prevent complications in plastic surgery patients by improving decision-making, providing recommendations to address risk factors, and assisting doctors in choosing the optimal timing and setting for elective plastic… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Risk Assessment, Risk Factors
- What is being tested?
- AI-Based Risk Assessment Model
- How many people
- 3,347 participants
- Where
- 1 siteArgentina
- Study dates
- 1 Jan 2021to 31 May 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study population consists of patients evaluated at Bukret Plastic Surgery, a solo practice located in Buenos Aires, Argentina. This population includes individuals seeking elective plastic surgery who meet the inclusion criteria and are willing to undergo the AI-based risk assessment model evaluation. Participants are drawn from a diverse demographic background, encompassing various ages, genders, and health statuses. The focus is on those who can adhere to the preoperative and postoperative recommendations to optimize surgical outcomes and minimize complications.
Who may be able to join
- Age 18 years or older.
- Evaluated for elective plastic surgery.
- Ability to provide informed consent.
- Completion of the preoperative assessment using the AI-based risk assessment model.
- Agreement to follow preoperative and postoperative recommendations provided by Dr. Bukret, according to the AI-based risk assessment model.
Who cannot join
- Individuals under the age of 18.
- Patients undergoing emergency plastic surgery procedures.
- Inability to provide informed consent due to cognitive or psychological impairment.
- Pregnant or breastfeeding women.
- Patients with a history of non-compliance with medical recommendations.
- Patients currently enrolled in another clinical trial that could interfere with this study\'s outcomes.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Evidence-Based Prospective Study of the Artificial Intelligence Risk Assessment Model for Complication Prevention in Plastic Surgery
The goal of this observational study is to determine if an AI-based risk assessment model can help prevent complications in plastic surgery patients by improving decision-making, providing recommendations to address risk factors, and assisting doctors in choosing the optimal timing and setting for elective plastic surgery. The study aims to answer if the AI model can effectively identify high-risk patients and what specific risk factors predict complications.
Purpose:
Read more
Evaluate the clinical effectiveness of an AI-based risk assessment model in preventing complications in plastic surgery patients by analyzing clinical data and patient history, providing personalized recommendations to mitigate risk factors and enhance outcomes.
Hypothesis:
The AI model can more accurately identify high-risk patients and provide effective recommendations to reduce complications compared to traditional methods.
Participants:
Individuals undergoing elective plastic surgery. They will complete an online form collecting data on age, height, weight, smoking habits, and comorbidities. The system calculates risk scores, BMI, and Caprini scores.
Study Procedures:
Risk assessment using the AI model, which evaluates multiple factors and generates personalized recommendations, including weight management, smoking cessation, blood pressure control, Doppler ultrasound for DVT, nutritional consultations, and specialist referrals. Recommendations are reviewed and approved by plastic surgeons.
Follow-Up:
The follow-up period ranges from 2 to 41 months, with a mean of 15 months. Data on patient outcomes, including complication rates and satisfaction, will be collected and analyzed.
Outcomes Measured:
Incidence of complications, the accuracy of the AI model in predicting complications, and its impact on improving surgical outcomes.
Impact:
The study aims to provide insights into AI use in plastic surgery, leading to better risk assessment tools and protocols, enhancing preoperative planning, postoperative care, and patient safety and satisfaction.
Read the detailed description
Study Design and Procedures:
The goal of this observational study is to determine if an AI-based risk assessment model can help prevent complications in plastic surgery patients by improving decision-making, providing recommendations to address risk factors, and assisting doctors in choosing the right time and setting for elective plastic surgery.
Methodology:
The study was conducted from January 2021 to May 2024, involving 3,347 patients assessed using the AI risk assessment model in a solo practice setting. The model uses an algorithm to evaluate clinical data and patient history, calculate risk scores, highlight risk factors, and generate personalized recommendations.
Patient Assessments:
Data Collection: Participants complete an online form collecting data on various clinical factors, including BMI, age, Caprini score, smoking habits, and gender.
Risk Calculation: The algorithm calculates risk scores, flags abnormal values, and screens for Body Dysmorphic Disorder (BDD) using the Body Dysmorphic Disorder Questionnaire (BDDQ).
Risk Categorization: Patients are categorized into low, moderate, or high-risk groups based on their risk scores.
Personalized Recommendations: The model generates specific recommendations for each patient based on their risk assessment, including:
Weight Management: Target weight recommendations for patients with BMI ≥ 25.1. Smoking Cessation: Advice for patients who smoke to quit. Blood Pressure Control: Recommendations for hypertensive patients to monitor blood pressure daily and consult a cardiologist.
Deep Vein Thrombosis (DVT) and Varices: Suggestions for Doppler ultrasound of the lower limbs 24 to 72 hours before surgery for patients with DVT, coagulopathies, varices, or Caprini score ≥ 8.
Comorbidities: Recommendations for cardiac echocardiogram and stress tests for patients over 50 with hypertension or vascular pathology antecedents.
Nutritional Guidance: Advice for patients with BMI ≤ 25.1 to consult a nutritionist and undergo screening for eating disorders with psychiatric consultation.
Specialist Referrals: Suggestions for consultations with endocrinologists, bariatric surgeons, cardiologists, hematologists, or other specialists according to comorbidities findings, and psychiatric referrals according to BDD screening.
Data Management:
Data collected from participants are anonymized and stored securely to protect patient privacy and confidentiality. The data management process includes:
Anonymization of Sensitive Data: All personally identifiable information (PII) is removed or masked to ensure that participants\' identities are protected. Anonymized data is used for analysis to maintain confidentiality.
Quality Assurance Plan: Regular data validation and registry procedures, including site monitoring and auditing, to ensure data integrity.
Data Checks: Consistency checks for data fields and predefined rules for range. Source Data Verification: Comparison of registry data with external sources, such as medical records, to assess accuracy and completeness.
Data Dictionary: Detailed descriptions of each variable, including source, coding, and normal ranges.
Standard Operating Procedures (SOPs): Procedures for patient recruitment, data collection, management, analysis, adverse event reporting, and change management.
Statistical Analysis:
Statistical and inferential analyses were performed using a Colab notebook to ensure robustness and reproducibility. The primary outcome measure was the incidence of complications in each risk group. Secondary outcome measures included the correlation between risk factors and complications.
Sample Size Assessment:
The sample size was determined to ensure sufficient power to detect differences in complication rates between the risk groups.
Plan for Missing Data:
Procedures were in place to address missing data, including imputation techniques and sensitivity analyses.
Statistical Analysis Plan:
The analysis plan included descriptive statistics, correlation analyses, and regression models to evaluate the relationship between risk factors and complications.
- Conditions
- Risk Assessment, Risk Factors
- Being tested
- AI-Based Risk Assessment Model
- Keywords
- Plastic Surgery, Risk Assessment, Artificial intelligence, Patient Safety, Surgical Risk Factors, Personalized Recommendations, Postoperative Complications, Complication Prevention, Decision Support Systems, Elective Surgery
- Medical subject headings
- Postoperative Complications
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Risk Assessment ModelOther
Also called AIRA10, AestheticSafe, Bukret's AI model, Risk Assessment Algorithm
Bukret AI Risk Assessment Model is a sophisticated decision support system that evaluates clinical data and patient history to generate personalized risk scores and classify patients into risk group categories for those undergoing plastic surgery. This AI model analyzes various risk factors, including BMI, age, smoking habits, and medical history, to identify potential complications. Based on the AI-generated risk group and specific risk factors, the model provides tailored recommendations for preoperative management, such as weight management, smoking cessation, blood pressure monitoring, and specialist consultations, to minimize surgical risks and improve outcomes. This intervention aims to enhance surgical planning and patient safety by offering personalized, actionable recommendations.
| Study group | Role | What they receive |
|---|---|---|
| Patients undergoing elective plastic surgery, evaluated with the AI-based risk assessment model. | This cohort includes patients undergoing elective plastic surgery who are evaluated using an AI-based risk assessment model. The AI model analyzes clinical data and patient history to generate personalized risk scores and recommendations to minimize surgical complications.
Based on the AI-generated risk scores, patients are divided into three risk groups: low, moderate, and high-risk. The model provides tailored recommendations for each patient's altered risk factor, such as weight management, smoking cessation, blood pressure monitoring, and specialist consultations, to address specific risk factors and improve surgical outcomes. Receives: Other: AI-Based Risk Assessment Model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Incidence of Postoperative Complications Including DVT, PTE, and ASIA Syndrome Within 2 to 41 Months Post-Surgery
Postoperative complications will be assessed using a standardized complication reporting system. Complications include but are not limited to infections, hematomas, seromas, wound dehiscence, deep vein thrombosis (DVT), and pulmonary thromboembolism (PTE). Each participant will be monitored for any complications occurring within the follow-up range of 2 to 41 months (mean = 15 months) post-surgery. The presence and severity of complications, such as severe sepsis, hemorrhage, hematoma, seroma, wound dehiscence, ASIA syndrome (diagnosed up to 6 months post-surgery), infection, and pulmonary thromboembolism, will be documented during follow-up visits. The data will be collected through clinical evaluations and patient self-reports. Specific follow-up visits will be scheduled at 1 week, 2 weeks, 1 month, 2 months, 6 months, and additional follow-ups as needed based on individual patient conditions and complications.
Measured: From enrollment to the end of treatment at 8 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Jul 2024
- Study start1 Jan 2021
- Primary completion31 May 2024
- Study completion31 May 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Risk AssessmentStudies 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
- 3,347 participants (actual)
Sponsor and investigators
- Lead sponsor
- Bukret Plastic Surgery
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Williams E Bukret, MD, EMBA, Principal investigator
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06507384
- Sponsor's protocol ID
- AIRA-PS-24
- Acronym
- AI
- Status last verified
- Jul 2024
- First submitted
- 12 Jul 2024
- Last updated
- 18 Jul 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: All individual participant data (IPD) collected throughout the trial will be shared. This includes all disidentified data that underlie the results reported in the study publication, such as demographic data, clinical data, and outcome measures. Data will be shared in a format that ensures confidentiality and privacy of the participants. The IPD will be available to researchers who provide a methodologically sound proposal, subject to review and approval by the principal investigator. Data sharing agreements will be required to ensure the proper use of the shared data. The data will be available for 5 years following the completion of the study.
Study at a glance
From the registry record, last updated by the sponsor on 18 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evidence-Based Prospective Study of the Artificial Intelligence Risk Assessment Model for Complication Prevention in Plastic Surgery
- ClinicalTrials.gov ID
- NCT06507384
- Acronym / sponsor ID
- AI / AIRA-PS-24
- Conditions
- Risk Assessment, Risk Factors
- Keywords
- Plastic Surgery, Risk Assessment, Artificial intelligence, Patient Safety, Surgical Risk Factors, Personalized Recommendations, Postoperative Complications, Complication Prevention
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 3,347 participants (actual)
- Lead sponsor
- Bukret Plastic SurgeryUniversity, hospital or other
- Study start
- 1 Jan 2021 (actual)
- Primary completion
- 31 May 2024 (actual)
- Study completion
- 31 May 2024 (actual)
- First posted
- 18 Jul 2024 (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.
The goal of this observational study is to determine if an AI-based risk assessment model can help prevent complications in plastic surgery patients by improving decision-making, providing recommendations to address risk factors, and assisting doctors in choosing the optimal timing and setting for elective plastic surgery. The study aims to answer if the AI model can effectively identify high-risk patients and what specific risk factors predict complications.
Purpose:
Evaluate the clinical effectiveness of an AI-based risk assessment model in preventing complications in plastic surgery patients by analyzing clinical data and patient history, providing personalized recommendations to mitigate risk factors and enhance outcomes.
Hypothesis:
The AI model can more accurately identify high-risk patients and provide effective recommendations to reduce complications compared to traditional methods.
Participants:
Individuals undergoing elective plastic surgery. They will complete an online form collecting data on age, height, weight, smoking habits, and comorbidities. The system calculates risk scores, BMI, and Caprini scores.
Study Procedures:
Risk assessment using the AI model, which evaluates multiple factors and generates personalized recommendations, including weight management, smoking cessation, blood pressure control, Doppler ultrasound for DVT, nutritional consultations, and specialist referrals. Recommendations are reviewed and approved by plastic surgeons.
Follow-Up:
The follow-up period ranges from 2 to 41 months, with a mean of 15 months. Data on patient outcomes, including complication rates and satisfaction, will be collected and analyzed.
Outcomes Measured:
Incidence of complications, the accuracy of the AI model in predicting complications, and its impact on improving surgical outcomes.
Impact:
The study aims to provide insights into AI use in plastic surgery, leading to better risk assessment tools and protocols, enhancing preoperative planning, postoperative care, and patient safety and satisfaction.
Detailed description
Study Design and Procedures:
The goal of this observational study is to determine if an AI-based risk assessment model can help prevent complications in plastic surgery patients by improving decision-making, providing recommendations to address risk factors, and assisting doctors in choosing the right time and setting for elective plastic surgery.
Methodology:
The study was conducted from January 2021 to May 2024, involving 3,347 patients assessed using the AI risk assessment model in a solo practice setting. The model uses an algorithm to evaluate clinical data and patient history, calculate risk scores, highlight risk factors, and generate personalized recommendations.
Patient Assessments:
Data Collection: Participants complete an online form collecting data on various clinical factors, including BMI, age, Caprini score, smoking habits, and gender.
Risk Calculation: The algorithm calculates risk scores, flags abnormal values, and screens for Body Dysmorphic Disorder (BDD) using the Body Dysmorphic Disorder Questionnaire (BDDQ).
Risk Categorization: Patients are categorized into low, moderate, or high-risk groups based on their risk scores.
Personalized Recommendations: The model generates specific recommendations for each patient based on their risk assessment, including:
Weight Management: Target weight recommendations for patients with BMI ≥ 25.1. Smoking Cessation: Advice for patients who smoke to quit. Blood Pressure Control: Recommendations for hypertensive patients to monitor blood pressure daily and consult a cardiologist.
Deep Vein Thrombosis (DVT) and Varices: Suggestions for Doppler ultrasound of the lower limbs 24 to 72 hours before surgery for patients with DVT, coagulopathies, varices, or Caprini score ≥ 8.
Comorbidities: Recommendations for cardiac echocardiogram and stress tests for patients over 50 with hypertension or vascular pathology antecedents.
Nutritional Guidance: Advice for patients with BMI ≤ 25.1 to consult a nutritionist and undergo screening for eating disorders with psychiatric consultation.
Specialist Referrals: Suggestions for consultations with endocrinologists, bariatric surgeons, cardiologists, hematologists, or other specialists according to comorbidities findings, and psychiatric referrals according to BDD screening.
Data Management:
Data collected from participants are anonymized and stored securely to protect patient privacy and confidentiality. The data management process includes:
Anonymization of Sensitive Data: All personally identifiable information (PII) is removed or masked to ensure that participants\' identities are protected. Anonymized data is used for analysis to maintain confidentiality.
Quality Assurance Plan: Regular data validation and registry procedures, including site monitoring and auditing, to ensure data integrity.
Data Checks: Consistency checks for data fields and predefined rules for range. Source Data Verification: Comparison of registry data with external sources, such as medical records, to assess accuracy and completeness.
Data Dictionary: Detailed descriptions of each variable, including source, coding, and normal ranges.
Standard Operating Procedures (SOPs): Procedures for patient recruitment, data collection, management, analysis, adverse event reporting, and change management.
Statistical Analysis:
Statistical and inferential analyses were performed using a Colab notebook to ensure robustness and reproducibility. The primary outcome measure was the incidence of complications in each risk group. Secondary outcome measures included the correlation between risk factors and complications.
Sample Size Assessment:
The sample size was determined to ensure sufficient power to detect differences in complication rates between the risk groups.
Plan for Missing Data:
Procedures were in place to address missing data, including imputation techniques and sensitivity analyses.
Statistical Analysis Plan:
The analysis plan included descriptive statistics, correlation analyses, and regression models to evaluate the relationship between risk factors and complications.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients undergoing elective plastic surgery, evaluated with the AI-based risk assessment model. | – | Other: AI-Based Risk Assessment ModelThis cohort includes patients undergoing elective plastic surgery who are evaluated using an AI-based risk assessment model. The AI model analyzes clinical data and patient history to generate personalized risk scores and recommendations to minimize surgical complications. Based on the AI-generated risk scores, patients are divided into three risk groups: low, moderate, and high-risk. The model provides tailored recommendations for each patient's altered risk factor, such as weight management, smoking cessation, blood pressure monitoring, and specialist consultations, to address specific risk factors and improve surgical outcomes. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of patients evaluated at Bukret Plastic Surgery, a solo practice located in Buenos Aires, Argentina. This population includes individuals seeking elective plastic surgery who meet the inclusion criteria and are willing to undergo the AI-based risk assessment model evaluation. Participants are drawn from a diverse demographic background, encompassing various ages, genders, and health statuses. The focus is on those who can adhere to the preoperative and postoperative recommendations to optimize surgical outcomes and minimize complications.
Key inclusion criteria (5 listed)
- Age 18 years or older.
- Evaluated for elective plastic surgery.
- Ability to provide informed consent.
- Completion of the preoperative assessment using the AI-based risk assessment model.
- Agreement to follow preoperative and postoperative recommendations provided by Dr. Bukret, according to the AI-based risk assessment model.
Key exclusion criteria (6 listed)
- Individuals under the age of 18.
- Patients undergoing emergency plastic surgery procedures.
- Inability to provide informed consent due to cognitive or psychological impairment.
- Pregnant or breastfeeding women.
- Patients with a history of non-compliance with medical recommendations.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-Based Risk Assessment ModelAlso: AIRA10, AestheticSafe, Bukret's AI model, Risk Assessment AlgorithmBukret AI Risk Assessment Model is a sophisticated decision support system that evaluates clinical data and patient history to generate personalized risk scores and classify patients into risk group categories for those undergoing plastic surgery. This AI model analyzes various risk factors, including BMI, age, smoking habits, and medical history, to identify potential complications. Based on the AI-generated risk group and specific risk factors, the model provides tailored recommendations for preoperative management, such as weight management, smoking cessation, blood pressure monitoring, and specialist consultations, to minimize surgical risks and improve outcomes. This intervention aims to enhance surgical planning and patient safety by offering personalized, actionable recommendations. | Other | Patients undergoing elective plastic surgery, evaluated with the AI-based risk assessment model. |
Primary outcome
Incidence of Postoperative Complications Including DVT, PTE, and ASIA Syndrome Within 2 to 41 Months Post-Surgery
Time frame: From enrollment to the end of treatment at 8 weeks
Postoperative complications will be assessed using a standardized complication reporting system. Complications include but are not limited to infections, hematomas, seromas, wound dehiscence, deep vein thrombosis (DVT), and pulmonary thromboembolism (PTE). Each participant will be monitored for any complications occurring within the follow-up range of 2 to 41 months (mean = 15 months) post-surgery. The presence and severity of complications, such as severe sepsis, hemorrhage, hematoma, seroma, wound dehiscence, ASIA syndrome (diagnosed up to 6 months post-surgery), infection, and pulmonary thromboembolism, will be documented during follow-up visits. The data will be collected through clinical evaluations and patient self-reports. Specific follow-up visits will be scheduled at 1 week, 2 weeks, 1 month, 2 months, 6 months, and additional follow-ups as needed based on individual patient conditions and complications.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Jul 2024.
- A Novel Artificial Intelligence-Assisted Risk Assessment Model for Preventing Complications in Esthetic SurgeryLink provided by the sponsor.
- Individual participant dataSharing planned: Yes. All individual participant data (IPD) collected throughout the trial will be shared. This includes all disidentified data that underlie the results reported in the study publication, such as demographic data, clinical data, and outcome measures. Data will be shared in a format that ensures confidentiality and privacy of the participants. The IPD will be available to researchers who provide a methodologically sound proposal, subject to review and approval by the principal investigator. Data sharing agreements will be required to ensure the proper use of the shared data. The data will be available for 5 years following the completion of the study.
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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.
Williams E Bukret, MD, EMBA principal investigator
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 Risk Assessment and Risk Factors, run by Bukret Plastic Surgery at 1 site in Argentina.
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 5 inclusion and 6 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: finished, as of the sponsor's last update on 18 Jul 2024. 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 Argentina. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Incidence of Postoperative Complications Including DVT, PTE, and ASIA Syndrome Within 2 to 41 Months Post-Surgery (time frame: From enrollment to the end of treatment at 8 weeks). 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 18 Jul 2024.