Detecting changes in the performance of a clinical machine learning tool over time

Background: Excessive use of blood cultures (BCs) in Emergency Departments (EDs) results in low yields and high contamination rates, associated with increased antibiotic use and unnecessary diagnostics. Our team previously developed and validated a machine learning model to predict BC outcomes and enhance diagnostic stewardship. While the model showed promising initial results, concerns over performance drift due to evolving patient demographics, clinical practices, and outcome rates warrant continual monitoring and evaluation of such models. Methods: A real-time evaluation of the model's performance was conducted between October 2021 and September 2022. The model was integrated into Amsterdam UMC's Electronic Health Record system, predicting BC outcomes for all adult patients with BC draws in real time. The model's performance was assessed monthly using metrics including the Area Under the Curve (AUC), Area Under the Precision-Recall Curve (AUPRC), and Brier scores. Statistical Process Control (SPC) charts were used to monitor variation over time. Findings: Across 3.035 unique adult patient visits, the model achieved an average AUC of 0.78, AUPRC of 0.41, and a Brier score of 0.10 for predicting the outcome of BCs drawn in the ED. While specific population characteristics changed over time, no statistical points outside the statistical control range were detected in the AUC, AUPRC, and Brier scores, indicating stable model performance. The average BC positivity rate during the study period was 13.4%. Interpretation: Despite significant changes in clinical practice, our BC stewardship tool exhibited stable performance, suggesting its robustness to changing environments. Using SPC charts for various metrics enables simple and effective monitoring of potential performance drift. The assessment of the variation of outcome rates and population changes may guide the specific interventions, such as intercept correction or recalibration, that may be needed to maintain a stable model performance over time. This study suggested no need to recalibrate or correct our BC stewardship tool. Funding: No funding to disclose.