Predicting Blood Pressure After Nitroglycerin Infusion Dose Titration in Critical Care Units

Abstract

Critical care nurses use physiological indicators, such as blood pressure, to guide their decision-making regarding the titration of nitroglycerin infusions. A retrospective study was conducted to determine the accuracy of systolic blood pressure predictions during nitroglycerin infusions. Data were extracted from the publicly accessible eICU program database. The accuracy of a linear model, least absolute shrinkage and selection operator, ridge regression, and a stacked ensemble model trained using the AutoGluon-Tabular framework were investigated. A persistence model, where the future value in a time series is predicted as equal to its preceding value, was used as the baseline comparison for model accuracy. Internal-external validation was used to examine if heterogeneity among hospitals could contribute to model performance. The sample consisted of 827 patients and 2541 nitroglycerin dose titrations with corresponding systolic blood pressure measurements. The root-mean-square error on the test set for the stacked ensemble model developed using the AutoGluon-Tabular framework was the lowest of all models at 15.3 mm Hg, equating to a 22% improvement against the baseline. Internal-external validation revealed consistent accuracy across hospitals. Further studies are needed to determine the impact of using systolic blood pressure predictions to inform nurses' clinical decision-making regarding nitroglycerin infusion titration in critical care.

Computer-aided diagnostic system with automated deep learning method based on the AutoGluon framework improved the diagnostic accuracy of early esophageal cancer

“…However, the aforementioned DL models are associated with drawbacks such as intricate operations, high complexity, steep learning curves, manual debugging, and low accuracy. In response to this predicament, automatic learning amalgamates automation and machine learning disciplines to minimize human intervention and achieve automated machine learning; it has garnered increasing attention ( 22 , 23 ). Amazon has developed AutoGluon, an open-source code library for automatic machine learning that enables developers to build machine learning applications incorporating image, text, or tabular datasets.…”