AI Risk Assessment Model for Complication Prevention in Plastic Surgery (Artificial Intelligence)

NCT06507384 | Completed

• 1 other identifier: AIRA-PS-24
• Study Type: observational
• Target: 3,347
• Locations: 1 country
• Sites: 1

Brief 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 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.

Conditions

Risk Assessment; Risk Factors

Keywords

Plastic Surgery; Risk Assessment; Artificial intelligence; Patient Safety; Surgical Risk Factors; Personalized Recommendations; Postoperative Complications; Complication Prevention; Decision Support Systems; Elective Surgery

Outcome Measures

Primary Outcomes (1)

• 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.

  From enrollment to the end of treatment at 8 weeks

Study Arms (1)

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.

Other: AI-Based Risk Assessment Model

Interventions

AI-Based Risk Assessment Model OTHER

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.

Also known as: AIRA10, AestheticSafe, Bukret's AI model, Risk Assessment Algorithm, Bukret's AI Risk Assessment Model

Patients undergoing elective plastic surgery, evaluated with the AI-based risk assessment model.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Bukret Plastic Surgery: lead

Study Sites (1)

Bukret Plastic Surgery

Buenos Aires, 1107, Argentina

Location

Related Links

• A Novel Artificial Intelligence-Assisted Risk Assessment Model for Preventing Complications in Esthetic Surgery

MeSH Terms

Conditions

Postoperative Complications

Condition Hierarchy (Ancestors)

Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Williams E Bukret, MD, EMBA

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 12, 2024
• First Posted: July 18, 2024
• Study Start: January 1, 2021
• Primary Completion: May 31, 2024
• Study Completion: May 31, 2024
• Last Updated: July 18, 2024

Record last verified: 2024-07

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL
• Time Frame: Beginning 3 months after the publication of results and continuing for 1 year.
• Access Criteria: Researchers who wish to access the IPD and supporting information must submit a detailed research proposal outlining the planned analyses. The proposal should include the statistical methods and must be approved by an independent review committee. A data sharing agreement, which ensures the privacy and confidentiality of participants, must be signed. Requests can be submitted to the study's principal investigator via email. Proposals will be reviewed based on scientific merit, ethical considerations, and feasibility. Approval will be granted for analyses that align with the study's objectives and ethical guidelines.

Locations

AR (1)