Prediction of intradialytic hypotension based on heart rate variability and skin sympathetic nerve activity using
Yike Zhang1, Shuang Su2, Zhenye Chen1
1Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
Insights
A new model using early hemodialysis (HD) data accurately predicts intradialytic hypotension (IDH). This tool aids in managing HD complications by identifying patients at risk for IDH.
Area of Science:
- Nephrology
- Cardiovascular Physiology
- Biomedical Engineering
Background:
- Intradialytic hypotension (IDH) is a common and problematic complication during hemodialysis (HD).
- Existing predictive models for IDH are limited due to the complex causes of this condition.
Purpose of the Study:
- To develop and validate a predictive model for intradialytic hypotension (IDH) using early hemodialysis (HD) parameters.
- To identify key clinical and autonomic nervous system indicators for predicting IDH risk.
Main Methods:
- A predictive model was constructed using data from 201 maintenance HD patients across two centers.
- The model incorporated clinical data and autonomic parameters (skin sympathetic nerve activity and heart rate variability) from the initial 30 minutes of HD.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariable logistic regression were used for variable selection and model formulation.
Main Results:
- The final model included five key variables: baseline systolic blood pressure (SBP0), baseline and change in autonomic parameters (aSKNA0, △aSKNA0-30, SDNN0, △SDNN0-30).
- The nomogram demonstrated strong predictive performance with an Area Under the Curve (AUC) of 0.920 in the training cohort and 0.855 in the validation cohort.
- The model showed good concordance between predicted and observed IDH events.
Conclusions:
- A predictive model incorporating early HD clinical and autonomic data effectively predicts intradialytic hypotension (IDH).
- This LASSO-enabled nomogram offers a promising tool for the early identification and management of IDH in hemodialysis patients.
Background:
Intradialytic hypotension (IDH) is a prevalent complication during hemodialysis (HD). However, conventional predictive models are imperfect due to multifaceted etiologies underlying IDH.
Methods:
This study enrolled 201 patients undergoing maintenance HD across two centers. Seventy percent of the patient cohort was randomly allocated to the training cohort (n = 136), while the remaining 30% formed the validation cohort (n = 65). IDH was defined as a reduction in systolic blood pressure (SBP) ≥20 mmHg or mean arterial pressure (MAP) ≥10 mmHg. Clinical data and autonomic nervous parameters, including skin sympathetic nerve activity (SKNA) and heart rate variability (HRV) during the initial 30 min of HD, were employed to construct the model. The least absolute shrinkage and selection operator (LASSO) regression facilitated variable selection associated with IDH. Subsequently, a multivariable logistic regression model was formulated to predict the risk of IDH and establish the nomogram.
Results:
Sixty-six baseline features were included in the LASSO-regression model. In the final multivariable logistic regression model, 5 variables (SBP0, aSKNA0, △aSKNA0-30, SDNN0, △SDNN0-30) were incorporated into the nomogram. The AUC was 0.920 (95% CI, 0.878-0.962) in the training cohort and 0.855 (95% CI, 0.763-0.947) in the validation cohort, indicating concordance between the nomogram prediction and actual observation of IDH.
Conclusion:
The LASSO-enabled model, based on clinical characteristics and autonomic nervous system parameters from the first 30 min of HD, shows promise in accurately predicting IDH.
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