The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure

By Jean-Louis Vincent, R. Moreno, J. Takala, S. Willatts, A. Mendonça, H. Bruining, C. Reinhart, P. Suter, L. Thijs. 1996

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Citations in the corpus

AutoScore: A Machine Learning–Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records (2020, methodology): “Risk-scoring models have been traditionally developed in 1 of 2 ways: through expert opinions or consensus, such as the Sepsis-related Organ Failure Assessment [5] score and the National Early Warning Score [6], and through the analysis of conventional cohort studies, such as the History, Electrocar”
Clinical management of severe acute respiratory infection (SARI) when COVID-19 disease is suspected. Interim guidance (2020, background): “Assume the baseline score is 0 if data are not available (22).”
Learning (predictive) risk scores in the presence of censoring due to interventions (2015, methodology): “A number of additional widely used scoring systems have been designed in this way, including the Multiple Organ Dysfunction Score (Marshall et al, 1995) (MODS), the Sequential Organ Failure Assessment (Vincent et al, 1996) (SOFA), and Medsger’s scoring system (Medsger et al, 2003).”
Early Neuromuscular Blockade in the Acute Respiratory Distress Syndrome (2019, unknown): “§ Sequential Organ Failure Assessment (SOFA) scores were measured in 5 organ systems (respiratory, cardiovascular, hematologic, gastrointestinal, and renal; the neurologic system was not assessed), with each organ scored from 0 to 4, resulting in an aggregated score that ranges from 0 to 20, with hi”
Learning to Detect Sepsis with a Multitask Gaussian Process RNN Classifier (2017, background): “In addition, the SIRS score for systemic inﬂammatory response syndrome was part of the original clinical deﬁnition of sepsis (Bone et al., 1992), although other scores designed for sepsis such as SOFA (Vincent et al., 1996) and qSOFA (Singer et al., 2016) have been more popular in recent years.”
Critically Ill patients with 2009 influenza A(H1N1) in Mexico. (2009, unknown): “5 (6-24) [8-22]”
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use (2019, background): “Currently, many rule-based assistive tools, such as different early warning scores (Prytherch et al., 2010; Vincent et al., 1996; Duncan et al., 2006), have been deployed in clinical settings.”