Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Moreno, RP; Metnitz, PG; Metnitz, B; Bauer, P; Afonso de Carvalho, S; Hoechtl, A; SAPS 3 Investigators.
Modeling in-hospital patient survival during the first 28 days after intensive care unit admission: a prognostic model for clinical trials in general critically ill patients.
J Crit Care. 2008; 23(3):339-348
Doi: 10.1016/j.jcrc.2007.11.004
Web of Science
PubMed
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- Co-Autor*innen der Med Uni Graz
- Metnitz Philipp
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- Abstract:
- The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. The study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model. The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. None. The database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups. Both statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.
- Find related publications in this database (using NLM MeSH Indexing)
- Aged -
- Clinical Trials as Topic - statistics & numerical data
- Critical Illness - mortality
- Female -
- Hospital Mortality -
- Humans -
- Intensive Care Units - statistics & numerical data
- Male -
- Middle Aged -
- Models, Statistical -
- Prognosis -
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- Find related publications in this database (Keywords)
- intensive care
- critical care
- severity scores
- outcome
- 28-day survival