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Predictive value of systemic inflammation response index for atherosclerotic cardiovascular disease risk in patients
Yu Chen1,2, Weikang Huang2, Shihan Zhao2
1Department of Pharmacy, Shanghai Children's Medical Center Guizhou Hospital, Shanghai Jiao tong University School of Medicine, Guiyang, China.
Insights
Systemic Inflammation Response Index (SIRI) is a significant predictor of atherosclerotic cardiovascular disease (ASCVD) in hypercholesterolemia patients. An interpretable machine learning model incorporating SIRI aids in predicting ASCVD risk, improving early identification.
Area of Science:
- Cardiovascular Medicine
- Biomarker Discovery
- Machine Learning in Healthcare
Background:
- Persistent cardiovascular risk in hypercholesterolemia patients despite treatment highlights the role of inflammation in atherosclerotic cardiovascular disease (ASCVD).
- The Systemic Inflammation Response Index (SIRI), a composite biomarker from neutrophil, monocyte, and lymphocyte counts, is explored for its association with ASCVD.
Purpose of the Study:
- To evaluate the relationship between SIRI and ASCVD in hypercholesterolemia patients.
- To develop an interpretable machine learning (ML) model for predicting ASCVD risk in this population.
Main Methods:
- Utilized National Health and Nutrition Examination Survey data (2001-2018) for 6,645 hypercholesterolemia patients and an external validation cohort.
- Employed ML to analyze SIRI's effect on ASCVD, establishing four risk prediction models.
- Evaluated model performance using Area Under the Receiver Operating Characteristic Curve (AUC) and interpreted using Shapley Additive Explanations (SHAP).
Main Results:
- SIRI demonstrated a consistent association with ASCVD in both internal and external cohorts.
- The XGBoost model, incorporating SIRI, achieved an AUC of 0.8001 (internal) and 0.7030 (external).
- SHAP analysis identified elevated SIRI as a key predictor, enhancing model discriminative capability.
Conclusions:
- High SIRI levels are an independent risk factor for ASCVD in hypercholesterolemia patients.
- An interpretable ML model combining SIRI for ASCVD prediction shows acceptable performance and moderate generalizability.
- This model can serve as a risk stratification tool for early identification of high-risk individuals.
Background:
Residual cardiovascular risk persists in patients with hypercholesterolemia despite lipid-lowering therapy, underscoring the importance of inflammation in ASCVD development. This study evaluated the relationship between Systemic Inflammation Response Index (SIRI) (a composite biomarker derived from neutrophil, monocyte, and lymphocyte counts) and ASCVD in patients with hypercholesterolemia. And to develop an interpretable machine learning (ML) model for predicting ASCVD risk in patients with hypercholesterolemia.
Methods:
This study utilized data from the National Health and Nutrition Examination Survey (2001-2018), including a total of 6,645 patients with hypercholesterolemia. Additionally, a independent external cohort of 357 patients from Guizhou Provincial People's Hospital served as the validation cohort. Then, we used ML to analyze the effect of SIRI on ASCVD in patients with hypercholesterolemia and established four risk prediction models. Area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. Shapley Additive Explanations (SHAP) were applied for model interpretation, and a web-based application was developed for clinical use.
Results:
Our findings indicated that SIRI is consistently associated with ASCVD in hypercholesterolemia patients in both cohorts. SIRI and other seven features were used to construct ML models. The XGBoost model achieved an AUC of 0.8001 in the internal validation cohort and 0.7030 in the external cohort. The model retained strong clinical relevance. SHAP analysis highlighted elevated SIRI levels as an important predictor of ASCVD risk in patients with hypercholesterolemia. The inclusion of novel inflammatory markers such as SIRI enhanced the model's discriminative capability.
Conclusions:
The study findings revealed that high SIRI levels were an independent risk factor for ASCVD in patients with hypercholesterolemia. In addition, this study constructed the first interpretable ML model combined with SIRI for ASCVD prediction in hypercholesterolemia patients. The model demonstrated acceptable performance and moderate generalizability. While its external specificity was limited, the model may still serve as a useful risk stratification aid to support early identification of high-risk individuals with hypercholesterolemia.
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