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Review
. 2018 Jun;24(3):196-203.
doi: 10.1097/MCC.0000000000000496.

Predicting adverse hemodynamic events in critically ill patients

Joo H Yoon  1 , Michael R Pinsky
Affiliations
Review

Predicting adverse hemodynamic events in critically ill patients

Joo H Yoon et al. Curr Opin Crit Care. 2018 Jun.

Abstract

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.

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Figures

Figure 1
Figure 1. Comparison of data-driven predictive analytics with other conventional predictive modalities
APACHE IV: The Acute Physiology and Chronic Health Evaluation Score IV (4); SAPS II: Simplified Acute Physiology Score II (10); MEWS: The Modified Early Warning Score (11); NEWS: The National Early Warning Scores (14); ICU: Intensive Care Unit; SDU: Step-down Unit
Figure 2
Figure 2. Estimated trajectories of stratified relative risk groups.
Time index 0 corresponds to cardiorespiratory insufficiency (CRI) onset time for both plots. Groups are color coded. The solid lines reflect mean trajectories estimated from all raw trajectories for the representative groups, as determined by the maximum posterior probability from the group-based model. Shaded areas depict the associated 95% confidence intervals. Reprinted with permission of the American Thoracic Society. Copyright © 2018 American Thoracic Society. [16] Annals of the American Thoracic Society is an official journal of the American Thoracic Society
See this image and copyright information in PMC

References

    1. Harris CRS.Galen’s Pulse-Lore The Heart and the Vascular System in Ancient Greek Medicine, from Alcmaeon to Galen. Oxford, UK: Clarendon Press; 1973.
    1. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Critical care medicine 1985; 13: 818-829. - PubMed
    1. Moreno RP, Metnitz PG, Almeida E, Jordan B, Bauer P, Campos RA, Iapichino G, Edbrooke D, Capuzzo M, Le Gall JR. SAPS 3--From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensive care medicine 2005; 31: 1345-1355. - PMC - PubMed
    1. Zimmerman JE, Kramer AA, McNair DS, Malila FM. Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients. Critical care medicine 2006; 34: 1297-1310. - PubMed
    1. Higgins TL, Teres D, Copes WS, Nathanson BH, Stark M, Kramer AA. Assessing contemporary intensive care unit outcome: an updated Mortality Probability Admission Model (MPM0-III). Critical care medicine 2007; 35: 827-835. - PubMed

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