Machine learning-based investigation of the relationship between immune status and left ventricular hypertrophy in
Min Yang1,2, Bo Peng1,2, Quan Zhuang1,2
1Transplantation Center, The Third Xiangya Hospital, Central South University, Changsha, China.
Insights
Patients with end-stage kidney disease (ESKD) and left ventricular hypertrophy (LVH) show impaired immune function. Machine learning models, particularly random forest, can predict LVH risk in ESKD patients, suggesting immunomodulation as a potential treatment.
Area of Science:
- Cardiology
- Nephrology
- Immunology
Background:
- Left ventricular hypertrophy (LVH) is a common cardiac complication in end-stage kidney disease (ESKD) patients, predicting poor outcomes.
- The immune system's role in cardiac hypertrophy development is increasingly recognized across various diseases.
- Investigating the immune status in ESKD patients is crucial for understanding LVH pathogenesis.
Purpose of the Study:
- To explore the relationship between immune status and left ventricular hypertrophy (LVH) in end-stage kidney disease (ESKD) patients.
- To develop and evaluate machine learning models for predicting LVH in ESKD.
- To identify specific immune parameters associated with LVH occurrence and severity.
Main Methods:
- Retrospective analysis of 506 ESKD patients with simultaneous immune status assessment and echocardiography.
- Univariate and multivariate logistic regression to assess associations between immune parameters and LVH.
- Machine learning models including Support Vector Machine (SVM), Logistic Regression (LR), Multi-layer Perceptron (MLP), and Random Forest (RF) were employed for prediction.
Main Results:
- ESKD patients with LVH exhibited significantly lower counts of CD3+ T cells, CD4+ T cells, CD8+ T cells, and B cells compared to the non-LVH group.
- Reduced CD3+ T cell count was an independent risk factor for LVH, while decreased NK cell count correlated with LVH severity.
- The Random Forest (RF) model achieved a high predictive performance with an Area Under the Curve (AUC) of 0.942.
Conclusions:
- A significant association exists between immune parameters and LVH in ESKD patients.
- Machine learning, specifically the RF model, demonstrates strong predictive capability for identifying ESKD patients at risk of LVH.
- Immunomodulation emerges as a potential therapeutic strategy for preventing and treating LVH in the ESKD population.
Background:
Left ventricular hypertrophy (LVH) is the most frequent cardiac complication among end-stage kidney disease (ESKD) patients, which has been identified as predictive of adverse outcomes. Emerging evidence has suggested that immune system is implicated in the development of cardiac hypertrophy in multiple diseases. We applied machine learning models to exploring the relation between immune status and LVH in ESKD patients.
Methods:
A cohort of 506 eligible patients undergoing immune status assessment and standard echocardiography simultaneously in our center were retrospectively analyzed. The association between immune parameters and the occurrence of LVH were evaluated through univariate and multivariate logistic analysis. To develop a predictive model, we utilized four distinct modeling approaches: support vector machine (SVM), logistic regression (LR), multi-layer perceptron (MLP), and random forest (RF).
Results:
In comparison to the non-LVH group, ESKD patients with LVH exhibited significantly impaired immune function, as indicated by lower cell counts of CD3+ T cells, CD4+ T cells, CD8+ T cells, and B cells. Additionally, multivariable Cox regression analysis revealed that a decrease in CD3+ T cell count was an independent risk factor for LVH, while a decrease in NK cell count was associated with the severity of LVH. The RF model demonstrated superior performance, with an average area under the curve (AUC) of 0.942.
Conclusion:
Our findings indicate a strong association between immune parameters and LVH in ESKD patients. Moreover, the RF model exhibits excellent predictive ability in identifying ESKD patients at risk of developing LVH. Based on these results, immunomodulation may represent a promising approach for preventing and treating this disease.
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