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Machine learning based on systemic inflammation response index and risk of cardiovascular disease in gout: a
Qiang Zhang1, Xuan-Hua Yu2, Wei-Zhen Zhang3
1Department of Rheumatology and Chinese Medicine, The 962nd Hospital of the PLA, Harbin, 150048, China.
Insights
The systemic inflammation response index (SIRI) is linearly associated with increased cardiovascular disease (CVD) risk in gout patients. Higher SIRI levels indicate a greater risk, suggesting SIRI as a valuable tool for CVD prediction in this population.
Area of Science:
- Rheumatology
- Cardiology
- Biostatistics
Background:
- Gout is a chronic inflammatory condition often associated with cardiovascular disease (CVD).
- Identifying reliable markers for CVD risk in gout patients is crucial for timely intervention.
Purpose of the Study:
- To investigate the association between the systemic inflammation response index (SIRI) and the risk of developing CVD in individuals with gout.
- To evaluate the predictive performance of a machine learning model incorporating SIRI for CVD risk in gout patients.
Main Methods:
- Analysis of six cycles of NHANES data.
- Machine learning algorithms and SHAP interpretation for covariate screening and variable importance.
- Logistic regression, Restricted Cubic Splines (RCS), ROC, DCA, and calibration curves to assess SIRI's association with CVD risk and model performance.
- Integration of SIRI with the Framingham risk score (FRS) model to quantify improvements in prediction.
Main Results:
- A significant positive linear association was found between SIRI and CVD risk in gout patients (OR=1.297 per unit increase).
- Individuals in the highest SIRI quartile (Q4) had a 2.060-fold increased risk of CVD compared to the lowest quartile (Q1).
- The developed model showed robust discrimination (AUC=0.755) and improved the FRS model's predictive accuracy.
Conclusions:
- SIRI demonstrates a positive linear relationship with CVD risk in patients diagnosed with gout.
- The machine learning-based model incorporating SIRI offers a robust approach for CVD risk prediction in gout patients.
- SIRI serves as a valuable complement to existing risk scores like FRS, aiding in early CVD identification and management.
Objective:
Gout is a chronic inflammatory disease, and cardiovascular disease (CVD) is regarded as one of its complications. The aim of our study was to explore the association between systemic inflammation response index (SIRI) and the risk of CVD in gout.
Methods:
Six cycles of NHANES data were analyzed. Machine learning algorithms were employed to screen covariates, followed by SHAP interpretation to assess variable importance. Participants with gout were stratified by SIRI quartiles, and logistic regression was performed to evaluate CVD risk. RCS were applied to assess nonlinear trends, while discrimination, calibration, and clinical utility were evaluated using ROC, DCA, and calibration curve. Additionally, the Framingham risk score (FRS) model was integrated with SIRI, and model improvement was quantified via net reclassification improvement and integrated discrimination improvement.
Results:
Among 1260 participants with gout, 436 (weighted 28.77%) had CVD comorbidities. A linear positive association was observed between SIRI and CVD risk (P for nonlinear = 0.824), with each 1-unit increase in SIRI corresponding to 29.7% higher CVD risk (OR = 1.297, 95% CI 1.073-1.568). Participants in the highest SIRI quartile Q4 (OR = 2.060, 95% CI 1.141-3.721) exhibited increased CVD risk compared to Q1. The final model demonstrated robust discrimination (AUC = 0.755, 95% CI 0.729-0.783). Incorporating SIRI into the NHANES and clinical datasets improved the discrimination of the FRS model by 5.2% and 1.9%.
Conclusion:
A positive linear association was identified between SIRI and CVD risk in gout patients. The model constructed based on machine learning demonstrated comparable robustness to the FRS model in predicting CVD. These findings provide a theoretical and empirical foundation for early CVD identification, prevention, and management in this population. Key Points • The positive linear association between the systemic inflammation response index and cardiovascular disease, as well as its subtypes in patients with gout. • SIRI can serve as a valuable complement to the Framingham risk score model.
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