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Updated: May 9, 2025
Author Spotlight: Unveiling Prognostic Indicators in Heart Failure - The Role of Phase Angle and Bioelectrical Impedance Analysis
Published on: June 30, 2023
Development and external validation of a prediction model for prolonged intensive care unit stay in heart failure
Erming Yang1, Xingyue He1, Linbo Li1
1School of Nursing, Shanxi Medical University, No. 56 Xinjian South Road, Taiyuan 030001, Shanxi, China.
Insights
A new XGBoost model accurately predicts prolonged intensive care unit (ICU) stays in heart failure patients. This tool aids in risk stratification, potentially improving patient outcomes and reducing healthcare costs.
Area of Science:
- Cardiology
- Intensive Care Medicine
- Machine Learning in Healthcare
Background:
- Prolonged intensive care unit (ICU) stays in heart failure patients are linked to adverse prognoses and significant financial burdens.
- Effective risk stratification is crucial for managing these patients and optimizing resource allocation.
Purpose of the Study:
- To develop and validate a machine learning-based predictive model for identifying heart failure patients at high risk of prolonged ICU stays.
- To assess the model's performance in terms of discrimination, calibration, and clinical utility.
Main Methods:
- A retrospective cohort study utilizing data from 5,744 heart failure patients (development) and 4,056 patients (external validation) from multiple US hospitals.
- Feature selection employed Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression.
- Nine machine learning algorithms were evaluated, with XGBoost selected as the optimal model.
Main Results:
- The XGBoost model demonstrated strong predictive performance, with an Area Under the Curve (AUC) of 0.861 in the internal validation set and 0.815 in the external validation set.
- Key predictors included malignant arrhythmia, acute kidney injury (AKI), Sequential Organ Failure Assessment (SOFA) score, breath sounds, valvular disease, systolic blood pressure, diuretics, blood urea nitrogen, SpO2, and cardiac catheterization.
- The model showed robust discrimination, calibration, and clinical utility.
Conclusions:
- The developed XGBoost model is a reliable tool for the risk stratification of prolonged ICU stays in heart failure patients.
- This predictive capability can inform clinical decision-making and resource management for improved patient care.
Aims:
Prolonged intensive care unit (ICU) stays in heart failure patients are associated with poor prognosis and result in high medical expenses. To develop and validate a predictive model for prolonged ICU stays in heart failure patients.
Methods And Results:
A retrospective cohort study was conducted involving ICU patients with a primary diagnosis of heart failure. The model development cohort comprised 5744 ICU heart failure patients from Beth Israel Deaconess Medical Center, while the external validation cohort included 4056 ICU heart failure patients from 208 hospitals across the USA. The primary outcome was prolonged ICU stay in heart failure patients. Feature selection was performed using Least Absolute Shrinkage and Selection Operator, univariate, and multivariate logistic regression. Nine machine learning algorithms were applied to develop the prediction models. Based on discrimination, calibration, and clinical utility metrics, the XGBoost emerged as the superior model. The top 10 predictive variables identified were malignant arrhythmia, acute kidney injury, Sequential Organ Failure Assessment (SOFA) score, breath sounds, valvular disease, systolic blood pressure, diuretics, blood urea nitrogen, SpO2, and cardiac catheterization. The area under the receiver operating characteristic curve for the XGBoost model was 0.861 [95% confidence interval (CI): 0.836-0.886] with a Kappa value of 0.518 in the internal validation set, and 0.815 (95% CI: 0.799-0.831) with a Kappa value of 0.437 in the external validation set.
Conclusion:
The XGBoost model exhibited robust discrimination, calibration, and clinical utility, making it a robust tool for risk stratification of prolonged ICU stays in heart failure patients.
