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Predicting adverse hemodynamic events in critically ill patients

Yoon, Joo, H.; Pinsky, Michael, R.

Current Opinion in Critical Care: June 2018 - Volume 24 - Issue 3 - p 196–203
doi: 10.1097/MCC.0000000000000496
CARDIOPULMONARY MONITORING: Edited by Jean-Louis Teboul
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Purpose of review The art of predicting future hemodynamic instability in the critically ill has rapidly become a science with the advent of advanced analytical processed based on computer-driven machine learning techniques. How these methods have progressed beyond severity scoring systems to interface with decision-support is summarized.

Recent findings Data mining of large multidimensional clinical time-series databases using a variety of machine learning tools has led to our ability to identify alert artifact and filter it from bedside alarms, display real-time risk stratification at the bedside to aid in clinical decision-making and predict the subsequent development of cardiorespiratory insufficiency hours before these events occur. This fast evolving filed is primarily limited by linkage of high-quality granular to physiologic rationale across heterogeneous clinical care domains.

Summary Using advanced analytic tools to glean knowledge from clinical data streams is rapidly becoming a reality whose clinical impact potential is great.

Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA

Correspondence to Michael R. Pinsky, MD, Department of Critical Care Medicine, University of Pittsburgh, 1215.4 Kaufmann Medical Building, 3471 Fifth Avenue, Pittsburgh, PA 15213, USA. Tel: +1 412 647 8109; e-mail: pinsky@pitt.edu

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