Ignite Creation Date:
2024-10-26 @ 3:35 PM
Last Modification Date:
2024-10-26 @ 3:35 PM
Study NCT ID:
NCT06507384
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-07-12
Brief Title:
AI Risk Assessment Model for Complication Prevention in Plastic Surgery Artificial Intelligence
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Evidence-Based Prospective Study of the Artificial Intelligence Risk Assessment Model for Complication Prevention in Plastic Surgery
Status:
COMPLETED
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
AI
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
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 3347 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 251 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 251 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 participants39 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
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 3347 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 251 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 251 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 participants39 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None