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Machine learning algorithms for the early detection of bloodstream infection in children with osteoarticular
Yuwen Liu1, Yuhan Wu2, Tao Zhang3
1Department of Orthopaedic Surgery, Children's Hospital of Nanjing Medical University, Nanjing, China.
Insights
Machine learning accurately identifies bloodstream infections (BSI) in children with bone infections. This tool aids early diagnosis, improving patient outcomes and clinical decisions.
Area of Science:
- Pediatric Infectious Diseases
- Medical Machine Learning
- Computational Diagnostics
Background:
- Bloodstream infections (BSI) present a critical risk in pediatric osteoarticular infections.
- Early BSI detection is vital for effective treatment and improved patient outcomes.
- This study focuses on developing a machine learning (ML) model for early BSI identification in pediatric patients.
Purpose of the Study:
- To develop and validate a machine learning model for the early detection of bloodstream infections (BSI) in pediatric patients with osteoarticular infections.
- To identify key early indicators for BSI in this patient population.
- To enhance clinical decision-making processes for managing pediatric osteoarticular infections.
Main Methods:
- Retrospective analysis of pediatric patients with osteoarticular infections (2012-2023).
- Utilized blood and puncture fluid cultures, selecting sixteen early available variables.
- Applied and compared eight machine learning algorithms, evaluating performance using accuracy and AUC; SHAP values explained variable importance.
Main Results:
- The Random Forest model demonstrated superior performance with an AUC of 0.947 ± 0.016.
- Achieved high accuracy (0.895 ± 0.023), sensitivity (0.847 ± 0.071), and specificity (0.917 ± 0.007).
- Key predictive variables included procalcitonin (PCT), neutrophil count (N), leukocyte count (WBC), and fever duration.
Conclusions:
- The Random Forest model is effective for early and timely identification of BSI in pediatric osteoarticular infections.
- This ML model can support clinical decisions and mitigate risks from delayed/inaccurate blood culture results.
- Early BSI detection through ML can significantly improve management of pediatric osteoarticular infections.
Background:
Bloodstream infection (BSI) poses a significant life-threatening risk in pediatric patients with osteoarticular infections. Timely identification of BSI is crucial for effective management and improved patient outcomes. This study aimed to develop a machine learning (ML) model for the early identification of BSI in children with osteoarticular infections.
Materials And Methods:
A retrospective analysis was conducted on pediatric patients diagnosed with osteoarticular infections admitted to three hospitals in China between January 2012 and January 2023. All patients underwent blood and puncture fluid bacterial cultures. Sixteen early available variables were selected, and eight different ML algorithms were applied to construct the model by training on these data. The accuracy and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of these models. The Shapley Additive Explanation (SHAP) values were utilized to explain the predictive value of each variable on the output of the model.
Results:
The study comprised 181 patients in the BSI group and 420 in the non-BSI group. Random Forest exhibited the best performance, with an AUC of 0.947 ± 0.016. The model demonstrated an accuracy of 0.895 ± 0.023, a sensitivity of 0.847 ± 0.071, a specificity of 0.917 ± 0.007, a precision of 0.813 ± 0.023, and an F1 score of 0.828 ± 0.040. The four most significant variables in both the feature importance matrix plot of the Random Forest model and the SHAP summary plot were procalcitonin (PCT), neutrophil count (N), leukocyte count (WBC), and fever days.
Conclusions:
The Random Forest model proved to be effective in early and timely identification of BSI in children with osteoarticular infections. Its application could aid in clinical decision-making and potentially mitigate the risk associated with delayed or inaccurate blood culture results.
