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

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:

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.

Study Overview

• Status: Completed
• Conditions: Risk Factors; Risk Assessment
• Intervention / Treatment: Other: AI-Based Risk Assessment Model

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 Type: Observational
• Enrollment (Actual): 3347

Contacts and Locations

Study Locations

• Argentina
  • Buenos Aires, Argentina, 1107
    • Bukret Plastic Surgery

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• 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.

Description

Inclusion Criteria:

• 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.

Exclusion Criteria:

• 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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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; 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.; Other Names: AIRA10; AestheticSafe; Bukret's AI model; Risk Assessment Algorithm; Bukret's AI Risk Assessment Model

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Bukret Plastic Surgery
• Investigators: Principal Investigator: Williams E Bukret, MD, EMBA

Publications and helpful links

Helpful Links

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

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2021
• Primary Completion (Actual): May 31, 2024
• Study Completion (Actual): May 31, 2024

Study Registration Dates

• First Submitted: July 12, 2024
• First Submitted That Met QC Criteria: July 12, 2024
• First Posted (Actual): July 18, 2024

Study Record Updates

• Last Update Posted (Actual): July 18, 2024
• Last Update Submitted That Met QC Criteria: July 12, 2024
• Last Verified: July 1, 2024

More Information

Terms related to this study

• Keywords: Patient Safety; Risk Assessment; Artificial intelligence; Postoperative Complications; Elective Surgery; Plastic Surgery; Decision Support Systems; Complication Prevention; Surgical Risk Factors; Personalized Recommendations
• Other Study ID Numbers: AIRA-PS-24

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: 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.
• IPD Sharing Time Frame: Beginning 3 months after the publication of results and continuing for 1 year.
• IPD Sharing 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.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL

Study Data/Documents

1. Study Protocol
   Information comments: To access the study protocol, please visit the provided URL and follow the instructions for requesting access.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No