A simplified acute physiology score for ICU patients

By J. L. Gall, P. Loirat, A. Alpérovitch, P. Glaser, C. Granthil, D. Mathieu, P. Mercier, Rémi Thomas, D. Villers. 1984

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Citations in the corpus

Learning Credible Models (2017, methodology): “In addition, for each of the 15 time-varying variables used in the Simplified Acute Physiology Score (SAPS-I) [18] we extract the most abnormal value observed within the first 24 hours,based on the SAPS scoring system.”
Supersparse linear integer models for optimized medical scoring systems (2015, background): “Medical Scoring Systems Some popular medical scoring systems include: • SAPS I, II and III, to assess ICU mortality risk (Le Gall et al., 1984, 1993, Moreno et al., 2005); • APACHE I, II and III, to assess ICU mortality risk (Knaus et al., 1981, 1985, 1991); • CHADS2, to assess the risk of stroke…”
Supersparse Linear Integer Models for Predictive Scoring Systems (2013, methodology): “See, for instance, these commonly used scoring systems: (Gage et al. 2001; Le Gall et al. 1984; Le Gall, Lemeshow, and Saulnier 1993; Knaus et al. 1985).”
Transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. (2015, unknown): “Well-known prediction models include the Framingham Risk Score [7], Ottawa Ankle Rules [8], EuroScore [9], Nottingham Prognostic Index [10], and the Simplified Acute Physiology Score [11].”
Effect of Ebola Progression in Liberia (2015, unknown): “Well-known prediction models include the Framingham Risk Score,(7) Ottawa Ankle Rules,(8) EuroScore,(9) Nottingham Prognostic Index,(10) and the Simplified Acute Physiology Score.(11)”
Multitask learning and benchmarking with clinical time series data (2017, methodology): “While traditional models, such as the Simpli ed Acute Physiology Score (SAPS) [35] include only the rst 24 hours, we use a wider 48-hour window to enable the detection of changes and trends that may indicate changes in patient acuity.”
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement (2015, unknown): “Prediction models (also commonly called “prognostic models,” “risk scores,” or “prediction rules”6) are tools that combine multiple predictors by assigning relative weights to each predictor to obtain a risk or probability.1 2 Well-known prediction models include the Framingham Risk Score,7 Ottawa A”
Supersparse Linear Integer Models for Interpretable Classification (2013, background): “1 Related Work in Medicine Some popular medical scoring systems include: – SAPS I, II and III, to assess the mortality of patients in intensive care [40, 39, 49, 52]; – APACHE I, II and III, to assess the mortality of patients in intensive care [36, 34, 35]; – CHADS2, to assess the risk of stroke in”
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement (2015, unknown): “Ankle Rules [8]; EuroScore [9]; Nottingham Prognostic Index [10]; and the Simplified Acute Physiology Score [11].”
A Multivariate Timeseries Modeling Approach to Severity of Illness Assessment and Forecasting in ICU with Sparse, Heterogeneous Clinical Data (2015, methodology): “Gold-standard clinical models typically use populationbased acuity scores, such as SAPS-I (Le Gall et al. 1984), based on snapshots of the patient’s status during their stay in the ICU.”
Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012 (2012, methodology): “The descriptors were: SAPS-1 score [2], SOFA score [3], length of stay in days (LOS), length of survival following ICU admission in days (up to 2 years), and in-hospital death (0 = survivor, 1 = died in the hospital).”
From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system (2014, methodology): “…scoring systems such as the SOFA, 9 the Acute Physiology and Chronic Health Evaluation (APACHE), 17 and Simpli ﬁ ed Acute Physiology Score (SAPS) 18 have shown reasonable discrimination when classifying patients with sepsis admitted to the ICU into groups with high-risk and low-risk mortality.”
A Bayesian Approach to Learning Scoring Systems (2015, methodology): “Some popular medical scoring systems include SAPS (Le Gall et al., 1984) and APACHE (Knaus et al., 1981) to assess ICU mortality risk, CHADS2 (Gage et al., 2001) to assess the risk of stroke in patients with atrial fibrillation and TIMI (Antman et al., 2000) to assess the risk of death and ischemic…”
Effect of Ebola Progression in Liberia (2015, unknown): “Well-known prediction models include the Framingham Risk Score (7), Ottawa Ankle Rules (8), EuroScore (9), Nottingham Prognostic Index (10), and the Simplified Acute Physiology Score (11).”