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Machine learning algorithms to predict the risk of hyperlipidemia in people with HIV after starting HAART for 6
Yi Ding1, Jialu Li, Chengyu Gao
1Clinical and Research Center of AIDS, Beijing Ditan Hospital, Capital Medical University, China.
Insights
Machine learning effectively predicts hyperlipidemia risk in people living with HIV (PLWHs) starting highly active antiretroviral therapy (HAART). This aids early intervention to prevent cardiovascular disease.
Area of Science:
- Medical Informatics
- Cardiovascular Disease Prevention
- HIV Medicine
Background:
- People living with HIV (PLWHs) are at increased risk of cardiovascular and cerebrovascular diseases.
- Highly active antiretroviral therapy (HAART) can influence lipid metabolism, potentially increasing hyperlipidemia risk.
- Early identification of hyperlipidemia risk in PLWHs is crucial for timely intervention.
Purpose of the Study:
- To develop and validate machine learning models for predicting hyperlipidemia risk in PLWHs within six months of initiating HAART.
- To enhance early intervention strategies and mitigate cardiovascular and cerebrovascular complications.
Main Methods:
- Utilized electronic medical records from HAART-naive individuals at Beijing Ditan Hospital (January 2015-January 2023).
- Developed and compared classification models including Random Forest, XGBoost, and LightGBM.
- Evaluated model performance using accuracy, positive/negative predictive values, sensitivity, specificity, ROC curves, precision-recall curves, and decision curve analysis.
Main Results:
- The LightGBM model demonstrated superior performance in both training and testing datasets.
- Key predictors identified by SHAP analysis included baseline HDL-C, TG, viral load, age, albumin, monocyte count, CD4 cell count, uric acid, lymphocyte count, and sex.
- The model showed good performance in decision curve analysis.
Conclusions:
- The LightGBM model effectively predicts hyperlipidemia risk in PLWHs initiating HAART.
- Physicians should closely monitor lipid levels and consider timely lipid-lowering drug interventions.
- Proactive management can help prevent cardiovascular diseases in this population.
Objective:
The purpose of this study was to use machine learning models to predict the risk of hyperlipidemia in people with HIV (PWH) for 6 months after starting HAART, to improve early intervention efforts and prevent further progression to cardiovascular and cerebrovascular diseases.
Methods:
This study enrolled HAART-naive individuals who visited the clinic at Beijing Ditan Hospital between January 2015 and January 2023. All clinical features were extracted from the electronic medical records. A classification prediction model was established based on various machine learning algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), to predict the risk of hyperlipidemia based on accuracy, positive-predictive value, negative-predictive value, sensitivity, and specificity. Receiver operating characteristic (ROC) curve, precision-recall curve, and decision curve analyses were used to visually evaluate the model.
Results:
A total of 2479 participants (median age, 33 years) were included, of which 2380 (96.01%) were male and 99 (3.99%) were female. The LightGBM model performed the best among all the models in both the training and testing sets. This model performed well in the decision curve analysis (DCA), and baseline high-density lipoprotein cholesterol (HDL-C), baseline triglycerides, baseline viral load, age, albumin, monocyte count, baseline CD4 + cell count, uric acid level, lymphocyte count, and sex were the top 10 predictive risk factors for hyperlipidemia in PWH who started HAART treatment for 6 months, based on SHAP analysis.
Conclusion:
This study demonstrated that the LightGBM model can effectively predict the risk of hyperlipidemia in PWH after starting HAART treatment for 6 months and reminded physicians closely to monitor serum lipid levels or the timely addition of lipid-lowering drugs, which helped prevent the occurrence of cardiovascular diseases among PWH.
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