Continuous imputation of blood gas and metabolic panel laboratory values in the intensive care unit using machine learning

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Abstract
We developed machine learning (ML) models to perform continuous hourly prediction of arterial blood gas (ABG) and basic metabolic panel (BMP) laboratory values in critically ill patients. We evaluated its impact on prediction of the need for renal replacement therapy (RRT). We compared the performance of the deep learning models using laboratory variables imputed hourly by our ML estimators versus models using laboratory variables imputed by a previous-value baseline. Our ML model incorporated various predictors, including previous laboratory values, administered medications, clinical events, fluid input/output, hemodynamics, and ventilator settings. Accuracy of laboratory imputations were compared using mean absolute error (MAE) and root mean squared error (RMSE). We trained bi-directional long short-term memory models (Bi-LSTM) to predict need for renal replacement therapy (RRT) that differed only in how laboratory variables were imputed over time. We compared performance using area under the receiver operating characteristics curve (AUROC) and area under the precision-recall curve (AUPRC). XGBoost achieved an average error reduction of 33% in MAE and 32% in RMSE across ABG variables, and 19% and 21% across BMP variables, respectively. Bi-LSTM models using our ML-based hourly laboratory estimates (AUROC 0.951, 95% CI 0.942–0.960; AUPRC 0.419, 95% CI 0.375–0.465) outperformed models using previous-value laboratory imputation (AUROC 0.923, 95% CI 0.910–0.935; AUPRC 0.343, 95% CI 0.301–0.386). Our real-time imputation models led to significant error reduction of most ABG and BMP targets, improving deep learning model performance for the need of RRT.
Funding Information
  • National Institutes of Health

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