Background: Illness severity scoring systems are commonly used in critical care. When applied to the populations for whom they were developed and validated, these tools can facilitate mortality prediction and risk stratification, optimize resource use, and improve patient outcomes.
Objective: To describe the characteristics and applications of the scoring systems most frequently applied to critically ill patients.
Methods: A literature search was performed using MEDLINE to identify original articles on intensive care unit scoring systems published in the English language from 1980 to 2020. Search terms associated with critical care scoring systems were used alone or in combination to find relevant publications.
Results: Two types of scoring systems are most frequently applied to critically ill patients: those that predict risk of in-hospital mortality at the time of intensive care unit admission (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score, and Mortality Probability Models) and those that assess and characterize current degree of organ dysfunction (Multiple Organ Dysfunction Score, Sequential Organ Failure Assessment, and Logistic Organ Dysfunction System). This article details these systems' differing features and timing of use, score calculation, patient populations, and comparative performance data.
Conclusion: Critical care nurses must be aware of the strengths, limitations, and specific characteristics of severity scoring systems commonly used in intensive care unit patients to effectively employ these tools in clinical practice and critically appraise research findings based on their use.
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http://dx.doi.org/10.4037/ccn2021613 | DOI Listing |
Am J Manag Care
January 2025
Department of Orthopedic Surgery, Duke University School of Medicine, 311 Trent Dr, Durham, NC 27710. Email:
Objectives: Patients are often discharged to a skilled nursing facility (SNF) for postacute rehabilitation. Functional outcomes achieved in SNFs are variable, and costs are high. Especially for accountable care organizations (ACOs), home-based postacute rehabilitation offers a high-value option if outcomes are not compromised.
View Article and Find Full Text PDFJMIR Form Res
January 2025
Department of Medical Informatics, Amsterdam UMC - University of Amsterdam, Amsterdam, Netherlands.
Background: The prognosis for patients with several types of cancer has substantially improved following the introduction of immune checkpoint inhibitors, a novel type of immunotherapy. However, patients may experience symptoms both from the cancer itself and from the medication. A prototype of the eHealth tool Cancer Patients Better Life Experience (CAPABLE) was developed to facilitate symptom management, aimed at patients with melanoma and renal cell carcinoma treated with immunotherapy.
View Article and Find Full Text PDFN Z Med J
January 2025
Associate Professor, University of Otago, Christchurch.
Aim: Electronic cigarette use (vaping) has increased rapidly among adolescents globally. Most electronic cigarettes (e-cigarettes) contain nicotine, which is addictive and can cause behaviour problems and mood dysregulation. We sought to assess whether an educational intervention increased knowledge about vaping-related health risks and desire to quit among high school students.
View Article and Find Full Text PDFShock
January 2025
Department of Industrial and Systems Engineering, University of Florida, P.O. Box 116595, Gainesville, FL, 32611, USA.
Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to ICUs of Mayo Clinic Hospitals over eight-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status.
View Article and Find Full Text PDFEnviron Health Perspect
January 2025
Centre for Environment, Fisheries and Aquaculture Science (CEFAS), Weymouth, UK.
Background: Environmental change in coastal areas can drive marine bacteria and resulting infections, such as those caused by , with both foodborne and nonfoodborne exposure routes and high mortality. Although ecological drivers of in the environment have been well-characterized, fewer models have been able to apply this to human infection risk due to limited surveillance.
Objectives: The Cholera and Other Illness Surveillance (COVIS) system database has reported infections in the United States since 1988, offering a unique opportunity to both explore the forecasting capabilities machine learning could provide and to characterize complex environmental drivers of infections.
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