2 research outputs found
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Hypotension in ICU Patients Receiving Vasopressor Therapy
Vasopressor infusion (VPI) is used to treat hypotension in an ICU. We studied compliance with blood pressure (BP) goals during VPI and whether a statistical model might be efficacious for advance warning of impending hypotension, compared with a basic hypotension threshold alert. Retrospective data were obtained from a public database. Studying adult ICU patients receiving VPI at submaximal dosages, we analyzed characteristics of sustained hypotension episodes (>15 min) and then developed a logistic regression model to predict hypotension episodes using input features related to BP trends. The model was then validated with prospective data. In the retrospective dataset, 102-of-215 ICU stays experienced >1 hypotension episode (median of 2.5 episodes per day in this subgroup). When trained with 75% of retrospective dataset, testing with the remaining 25% of the dataset showed that the model and the threshold alert detected 99.6% and 100% of the episodes, respectively, with median advance forecast times (AFT) of 12 and 0 min. In a second, prospective dataset, the model detected 100% of 26 episodes with a median AFT of 22 min. In conclusion, episodes of hypotension were common during VPI in the ICU. A logistic regression model using BP temporal trend features predicted the episodes before their onset
Studying the Efficacy of and Developing Data-Driven Real-Time Clinical Decision Support Systems for Hypotension Detection
Critically ill patients admitted into intensive care units are prone to reoccurring episodes of sustained hypotension. Prolonged durations of hypotension are correlated to, and potentially cause, permanent body-wide damage to patients if not properly treated, which may result in death. Currently, typical care for the management of hypotension in the critically ill is reactive and delayed, perhaps due to clinical inertia. The purpose of this study is to describe the current problem that is faced in critical care through a retrospective analysis and introduce candidate models that may be used as clinical informatics systems for preemptive hypotension detection to aid clinicians and nurses providing care in the fast-paced clinical environment. The clinical performance of the models is quantified and the efficacy of implementation of these models is discussed