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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
Open accessAbstract
OBJECTIVE: To develop a model to assess severity of illness and predict vital status at hospital discharge based on ICU admission data. DESIGN: Prospective multicentre, multinational cohort study. PATIENTS AND SETTING: A total of 16,784 patients consecutively admitted to 303 intensive care units from 14 October to 15 December 2002. MEASUREMENTS AND RESULTS: ICU 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 H=10.56, p=0.39, C=14.29, p=0.16). Customized equations for major areas of the world were computed and demonstrate a good overall goodness-of-fit. CONCLUSIONS: The 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.
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Cites
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
A REVIEW OF GOODNESS OF FIT STATISTICS FOR USE IN THE DEVELOPMENT OF LOGISTIC REGRESSION MODELS1
LEMESHOW, S., & HOSMER, D. W. (1982) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a113284 · Cited by 1,947
Cited by
Tomazini, B. M., Maia, I. S., Cavalcanti, A. B., Berwanger, O., Rosa, R. G., Veiga, V. C., Avezum, A., Lopes, R. D., Bueno, F. R., Silva, M. V. A. O., et al. (2020) · JAMA · doi:10.1001/jama.2020.17021 · Cited by 1,241
Interpretable machine learning: Fundamental principles and 10 grand challenges
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys · doi:10.1214/21-ss133 · Cited by 958
Kramer, A. A., & Zimmerman, J. E. (2007) · Critical Care Medicine · doi:10.1097/01.ccm.0000275267.64078.b0 · Cited by 886
Clinical review: Scoring systems in the critically ill
Vincent, J.-L., & Moreno, R. (2010) · Critical Care · doi:10.1186/cc8204 · Cited by 634
Self-citationLew, C. C. H., Yandell, R., Fraser, R. J. L., Chua, A. P., Chong, M. F. F., & Miller, M. (2017) · Journal of Parenteral and Enteral Nutrition · doi:10.1177/0148607115625638 · Cited by 539
Metadata
- Authors
- Moreno, R. P., Metnitz, P. G. H., Almeida, E., Jordan, B., Bauer, P., Campos, R. A., Iapichino, G., Edbrooke, D., Capuzzo, M., Le Gall, J.-R., & on behalf of the SAPS 3 Investigators
- Venue
- Intensive Care Medicine
- Published
- 2005-08-17
- Volume
- 31
- Issue
- 10
- Pages
- 1345-1355
- ISSN
- 0342-4642 (ISSN-L)
- 1432-1238
- OpenAlex
- W2110317531
- Cited by
- 1,413
- References
- 2
- FWCI
- 16.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- The paper
- Moreno, R. P., Metnitz, P. G. H., Almeida, E., Jordan, B., Bauer, P., Campos, R. A., Iapichino, G., Edbrooke, D., Capuzzo, M., Le Gall, J.-R., & on behalf of the SAPS 3 Investigators. (2005). 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. Intensive Care Medicine. https://doi.org/10.1007/s00134-005-2763-5
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