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Related Concept Videos

SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

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SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
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Hyperpolarized 13C Metabolic Magnetic Resonance Spectroscopy and Imaging
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Predicting hyperlactatemia in the MIMIC II database.

Max Dunitz, George Verghese, Thomas Heldt

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    PubMed
    Summary
    This summary is machine-generated.

    Sepsis is a major cause of hospital deaths. Researchers developed a real-time algorithm using patient data to predict septic shock risk, achieving over 0.8 accuracy in identifying critical lactate levels.

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    Area of Science:

    • Critical Care Medicine
    • Biomedical Informatics
    • Cardiovascular Physiology

    Background:

    • Sepsis significantly contributes to hospital mortality in the US, with 1 in 2 to 3 deaths attributed to it.
    • Early identification of patients at risk for septic shock is crucial for timely intervention and improved outcomes.

    Purpose of the Study:

    • To characterize sepsis progression within intensive care units (ICUs) at Beth Israel Deaconess Medical Center (BIDMC).
    • To develop and validate a real-time predictive algorithm for stratifying patients with infectious complaints based on their risk of progressing to septic shock.

    Main Methods:

    • Utilized the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) database, comprising electronic medical records from BIDMC ICUs.
    • Extracted time-series data including heart rate, arterial blood pressure, cardiac output, and total peripheral resistance from 146 selected patient records.
    • Developed and evaluated various classifiers to predict high serum lactate levels, a key indicator of hypoperfusion and impending circulatory shock.

    Main Results:

    • The developed classifiers demonstrated effectiveness in distinguishing patients with serum lactate levels below 2.5 mmol/L from those exceeding this threshold.
    • The best-performing classifier achieved an area under the receiver operating characteristic curve (AUC) greater than 0.8 on test data, indicating strong predictive performance.
    • The algorithm successfully stratified patients into different risk categories for septic shock progression.

    Conclusions:

    • The study successfully characterized sepsis within a specific ICU population and developed a promising real-time predictive tool.
    • The algorithm shows potential for early identification of patients at high risk for septic shock, enabling proactive clinical management.
    • Predictive modeling using readily available physiological data can enhance the early detection of critical conditions like sepsis and septic shock.