SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission

By R. Moreno, P. Metnitz, E. Almeida, B. Jordan, P. Bauer, R. A. Campos, G. Iapichino, D. Edbrooke, M. Capuzzo, J. le Gall. 2005

1,001 citations recorded by the source corpus.

ObjectiveTo develop a model to assess severity of illness and predict vital status at hospital discharge based on ICU admission data.DesignProspective multicentre, multinational cohort study.Patients and settingA total of 16,784 patients consecutively admitted to 303 intensive care units from 14 October to 15 December 2002.Measurements and resultsICU admission data (recorded within ±1 h) were used, describing: prior chronic conditions and diseases; circumstances related to and physiologic derangement at ICU admission. Selection of variables for inclusion into the model used different complementary strategies. For cross-validation, the model-building procedure was run five times, using randomly selected four fifths of the sample as a development- and the remaining fifth as validation-set. Logistic regression methods were then used to reduce complexity of the model. Final estimates of regression coefficients were determined by use of multilevel logistic regression. Variables selection and weighting were further checked by bootstraping (at patient level and at ICU level). Twenty variables were selected for the final model, which exhibited good discrimination (aROC curve 0.848), without major differences across patient typologies. Calibration was also satisfactory (Hosmer-Lemeshow goodness-of-fit test Ĥ=10.56, p=0.39, Ĉ=14.29, p=0.16). Customised equations for major areas of the world were computed and demonstrate a good overall goodness-of-fit.ConclusionsThe SAPS 3 admission score is able to predict vital status at hospital discharge with use of data recorded at ICU admission. Furthermore, SAPS 3 conceptually dissociates evaluation of the individual patient from evaluation of the ICU and thus allows them to be assessed at their respective reference levels.

Subfields: Cardiology and Cardiovascular Medicine, Epidemiology

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Citations in the corpus

Learning Optimized Risk Scores (2016, methodology): “These models are widely used to support decision-making in domains such as: • Medicine: to assess the risk of mortality in intensive care (e.g., Moreno et al., 2005), critical physical conditions (e.g., adverse cardiac events, Six et al., 2008; Than et al., 2014) and mental illnesses (e.g., adult…”
Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension (2013, methodology): “Commonly used outcome prediction scores include the Acute Physiology and Chronic Health Evaluation (APACHE) scores (1), Simplified Acute Physiology Scores (SAPS) (2), and the Mortality Probability Objectives: To determine if a prediction rule for hospital mortality using dynamic variables in respons”
Machine Learning and Decision Support in Critical Care (2016, methodology): “…progression of the APACHE system towards increasing reliance on data for each subsequent generation has been echoed by other mortality prediction systems, including the Simplified Acute Severity Score (SAPS) [72], [105], [133], [134] and the Mortality Prediction Model (MPM) models [135]–[137].”
Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. (2020, neutral): “15 External validation studies have revealed AUROCs of 0⋅69 (95% CI 0⋅63–0⋅75) and 0·81 (0⋅79–0⋅93) in Denmark and Norway, respectively.”
Variation in critical care services across North America and Western Europe* (2008, methodology): “We also explored whether there was an association between ICU beds and the reported hospital mortality for intensive care patients using available published data from large international studies on intensive care (10, 17).”
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”
A Provable Algorithm for Learning Interpretable Scoring Systems (2018, methodology): “Some other widely used medical scores are SAPS I, II, and III [10, 21] and APACHE I, II, III to assess intensive care units mortality risks [14], CHADS2 to assess the risk of stroke [9]; TIMI to estimate the risk of death of ischemic events [2].”