Clinical Report: Utilizing Explainable Machine Learning to Detect CVD in Kidney Stone Patients
Overview
This study establishes a significant association between kidney stone disease and cardiovascular disease (CVD), demonstrating a 47% increased risk of CVD in patients with kidney stones. It also introduces a logistic regression model that effectively identifies prevalent CVD in this population using machine learning techniques.
Background
Kidney stone disease is a prevalent condition that poses a substantial risk for cardiovascular disease, which remains a leading cause of mortality worldwide. Understanding the relationship between these two conditions is crucial for improving patient outcomes and guiding preventive strategies. Current CVD risk assessment tools are inadequate for patients with kidney stones, highlighting the need for tailored predictive models.
Data Highlights
Metric
Value
Odds Ratio for CVD in Kidney Stone Patients
1.47 (95% CI: 1.20–1.80)
Area Under ROC Curve
0.801
Sensitivity
0.721
Specificity
0.771
Accuracy
0.759
Brier Score
0.169
Key Findings
Kidney stones are associated with a 47% increased risk of CVD.
The logistic regression model achieved an area under the ROC curve of 0.801.
Sensitivity and specificity of the model were 72.1% and 77.1%, respectively.
SHAP analysis identified 15 important predictors for CVD in kidney stone patients.
Current CVD risk assessment tools are not tailored for kidney stone patients.
Machine learning can enhance CVD risk prediction in specific populations.
Clinical Implications
Healthcare professionals should consider the increased cardiovascular risk in patients with kidney stone disease when assessing overall health. Implementing machine learning models could improve early identification and management of CVD in this population, facilitating targeted interventions.
Conclusion
The findings underscore the importance of recognizing kidney stone disease as a significant risk factor for cardiovascular disease, advocating for the integration of machine learning tools in clinical practice to enhance risk assessment.
