Annals of emergency medicine
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Multicenter Study
Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis.
The Third International Consensus Definitions (Sepsis-3) Task Force recommended the use of the quick Sequential [Sepsis-related] Organ Failure Assessment (qSOFA) score to screen patients for sepsis outside of the ICU. However, subsequent studies raise concerns about the sensitivity of qSOFA as a screening tool. We aim to use machine learning to develop a new sepsis screening tool, the Risk of Sepsis (RoS) score, and compare it with a slate of benchmark sepsis-screening tools, including the Systemic Inflammatory Response Syndrome, Sequential Organ Failure Assessment (SOFA), qSOFA, Modified Early Warning Score, and National Early Warning Score. ⋯ In this retrospective study, RoS was more timely and discriminant than benchmark screening tools, including those recommend by the Sepsis-3 Task Force. Further study is needed to validate the RoS score at independent sites.
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Multicenter Study Observational Study
Identification of Clinical Characteristics Associated With High-Level Care Among Patients With Skin and Soft Tissue Infections.
Serious adverse outcomes associated with skin and soft tissue infections are uncommon, and current hospitalization rates appear excessive. It would be advantageous to be able to differentiate between patients who require high-level inpatient services and those who receive little benefit from hospitalization. We sought to identify characteristics associated with the need for high-level inpatient care among emergency department patients presenting with skin and soft tissue infections. ⋯ A limited number of simple clinical characteristics appear to be able to identify skin and soft tissue infection patients who require high-level inpatient services. Further research is needed to determine whether patients who do not exhibit these criteria can be safely discharged from the ED.