โก Quick Summary
This study protocol outlines the development of machine learning models to predict postoperative complications following general surgery in Bandar Abbas, Iran. By utilizing various supervised ML techniques, the research aims to enhance surgical outcomes and patient safety.
๐ Key Details
- ๐ Study Location: Tertiary referral medical center in Bandar Abbas, Iran
- ๐ฅ Participants: Adults aged 18 and older undergoing general surgery
- ๐๏ธ Study Duration: September 2025 to September 2026
- โ๏ธ ML Techniques: Logistic regression, decision trees, random forests, extreme gradient boosting
- ๐ Evaluation Metrics: Accuracy, precision, recall, F1 score
๐ Key Takeaways
- ๐ Understanding complications is crucial for improving surgical care.
- ๐ค Machine learning offers innovative solutions for predicting risks associated with surgery.
- ๐ Study will analyze data from patients hospitalized for general surgery.
- ๐ Predictors include patient-related, surgery-related, and postoperative factors.
- ๐ Ethical considerations include informed consent and anonymous data collection.
- ๐ Findings will be published in scientific journals to share insights with the medical community.
๐ Background
Postoperative complications can significantly impact patient recovery and overall surgical success. Understanding the risk factors associated with these complications is essential for surgeons to enhance patient care. The integration of machine learning into surgical practice represents a promising avenue for improving predictive capabilities and ultimately reducing adverse outcomes.
๐๏ธ Study
This research will be conducted at a tertiary referral medical center in Bandar Abbas, Iran, focusing on patients aged 18 years and older who undergo various types of general surgery. The study aims to collect and analyze data from September 2025 to September 2026, with a primary focus on identifying complications that arise within 30 days post-surgery.
๐ Results
The study will employ four distinct supervised machine learning techniques to develop predictive models. The performance of these models will be evaluated using metrics such as accuracy, precision, recall, and F1 score, providing a comprehensive assessment of their effectiveness in predicting postoperative complications.
๐ Impact and Implications
The implications of this research are significant. By identifying risk factors and predicting complications, surgeons can make more informed decisions, potentially leading to improved patient outcomes and reduced healthcare costs. The application of machine learning in this context could pave the way for enhanced surgical protocols and better overall patient care.
๐ฎ Conclusion
This study protocol highlights the potential of machine learning to transform the landscape of surgical care by predicting postoperative complications. As we move forward, the integration of these advanced technologies into clinical practice could significantly enhance patient safety and surgical success rates. Continued research in this area is essential for realizing these benefits.
๐ฌ Your comments
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Prognostic machine learning models for predicting postoperative complications following general surgery in Bandar Abbas, Iran: a study protocol.
Abstract
INTRODUCTION: To enhance the quality of surgical care, complications need to be minimised. Consequently, comprehending the occurrence and risk elements for postoperative complications is essential. Subsequently, we will apply machine learning (ML) algorithms to build risk factor prediction models that will assist surgeons in identifying the risk factors associated with the development of postoperative complications after general surgery.
METHODS AND ANALYSIS: This research will take place at a tertiary referral medical centre located in Bandar Abbas, Hormozgan, Iran. The inclusion criteria are (1) individuals aged 18 years or older who have any type of general surgery and (2) hospitalised from September 2025 to September 2026. Individuals with insufficient data will be excluded. The main outcomes of the study are complications within 30 days of surgery. Patients will be divided into two groups based on whether they develop complications or not. The predictors are classified as (1) patient-related factors, (2) surgery-related factors and (3) postoperative factors. We intend to detect postoperative complications following general surgery using four distinct supervised ML techniques: (1) logistic regression, (2) decision trees, (3) random forests and (4) extreme gradient boosting. Accuracy, precision, recall and F1 score will be used to evaluate the performance of ML models.
ETHICS AND DISSEMINATION: With approval from the Hormozgan University of Medical School Research Ethics Board (IR.HUMS.REC.1404.137), we will carry out a forward-looking analysis of the medical records of patients undergoing general surgery. We will obtain informed consent, and all information will be collected and examined anonymously. The findings of this research will be released in appropriate scientific publications.
Author: [‘Vatankhah Tarbebar M’, ‘Mohammadi M’, ‘Mehrnoush V’, ‘Darsareh F’]
Journal: BMJ Open
Citation: Vatankhah Tarbebar M, et al. Prognostic machine learning models for predicting postoperative complications following general surgery in Bandar Abbas, Iran: a study protocol. Prognostic machine learning models for predicting postoperative complications following general surgery in Bandar Abbas, Iran: a study protocol. 2025; 15:e108019. doi: 10.1136/bmjopen-2025-108019