Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Moreno, RP; Metnitz, PG; Almeida, E; Jordan, B; Bauer, P; Campos, RA; Iapichino, G; Edbrooke, D; Capuzzo, M; Le Gall, JR; SAPS 3 Investigators.
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 Med. 2005; 31(10):1345-1355
Doi: 10.1007/s00134-005-2763-5
[OPEN ACCESS]
Web of Science
PubMed
FullText
FullText_MUG
- Co-Autor*innen der Med Uni Graz
- Metnitz Philipp
- Altmetrics:
- Dimensions Citations:
- Plum Analytics:
- Scite (citation analytics):
- Abstract:
- To develop a model to assess severity of illness and predict vital status at hospital discharge based on ICU admission data. Prospective multicentre, multinational cohort study. A total of 16,784 patients consecutively admitted to 303 intensive care units from 14 October to 15 December 2002. 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. 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.
- Find related publications in this database (using NLM MeSH Indexing)
- Adult -
- Comorbidity -
- Confidence Intervals -
- Hospital Mortality -
- Humans -
- Intensive Care Units - statistics & numerical data
- Logistic Models -
- Middle Aged -
- Predictive Value of Tests -
- Prognosis -
- Risk -
- Severity of Illness Index -
- Find related publications in this database (Keywords)
- intensive care unit
- severity of illness
- ICU mortality
- hospital mortality
- risk adjustment