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Surgical MAchine learning model for predicting Risk of postoperative infecTions in general elective surgery (SMART):
Nehal Hassan1, Robert Slight2, Graham Morgan3
1School of Pharmacy, Newcastle University, Newcastle upon Tyne, UK.
Objective:
To apply a machine learning (ML) model that we developed and internally validated for predicting postoperative infection likelihood after elective general abdominal surgery, SMART, among 2716 patients.
Methods:
The United Kingdom Health Data Research (UKHDR) Hub for Acute Care (PIONEER) supplied retrospective pseudonymised data for model training. These data contained demographic information, vital signs, microbiological investigations, comorbidities, surgical information, and infection diagnosis for elective general surgical patients (n = 2,716). Predictors were selected using an integrated approach of ML methods (feature elimination) and expert input. Recursive feature elimination with cross-validation was run on these predictors using Python(v3.8.2). Twelve algorithms were used, and an ensemble model with the three highest performing models was developed.
Results:
Nineteen predictors were selected to build the model, including demographics (e.g. age), comorbidities, microbiology data (e.g. multidrug-resistant infections), and laboratory investigation (CRP). The gradient-boosting classifier was found to be the best-performing model. The ensemble model showed high performance during training with 85.3% sensitivity, 74.6% specificity, and AUC=0.89, and during internal validation, with 96.9% sensitivity, 74.1% specificity, and AUC=0.86.
Conclusions:
The ML model showed high performance in predicting postoperative infections in elective surgery. It used modifiable predicators that aided its clinical application. Identifying patients at higher risk of postoperative infections before surgery can promote early interventions and reduce antimicrobial resistance risk. External validation and testing are necessary for successful clinical implementation.
Insights
A new machine learning (ML) model, SMART, accurately predicts postoperative infections in elective surgery patients. This tool can help identify high-risk individuals for early intervention and reduce antimicrobial resistance.
Area of Science:
- Medical Informatics
- Surgical Oncology
- Machine Learning in Healthcare
Background:
- Postoperative infections are a significant surgical complication, often poorly predicted by existing machine learning (ML) models.
- Previous ML models for infection prediction lacked representation of surgical patient populations.
- A novel ML model, SMART, was developed and validated for predicting postoperative infection risk in elective general abdominal surgery.
Purpose of the Study:
- To develop and internally validate a machine learning model (SMART) for predicting postoperative infection likelihood.
- To improve the accuracy of infection risk prediction in patients undergoing elective general abdominal surgery.
- To identify modifiable predictors for clinical application in preventing postoperative infections.
Main Methods:
- Retrospective data from 2,716 elective general surgical patients were utilized from the UK Health Data Research (UKHDR) Hub for Acute Care (PIONEER).
- Predictors were selected using integrated ML methods (recursive feature elimination) and expert input.
- An ensemble model combining the top three performing algorithms, including a gradient boosting classifier, was developed and validated.
Main Results:
- Nineteen predictors were identified, including demographics, comorbidities, microbiology data (e.g., MDR-infections), and C-reactive protein levels.
- The ensemble model achieved high performance during internal validation: 96.9% sensitivity, 74.1% specificity, and an Area Under the Curve (AUC) of 0.86.
- The Gradient Boosting Classifier demonstrated the best individual model performance.
Conclusions:
- The SMART model demonstrates high performance in predicting postoperative infections for elective surgery patients.
- The model's use of modifiable predictors facilitates clinical application for early intervention.
- Further external validation is crucial for successful clinical implementation and reducing antimicrobial resistance risks.
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