Publication
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
| dc.contributor.author | Moreno, R | |
| dc.contributor.author | Metnitz, P | |
| dc.contributor.author | Metnitz, B | |
| dc.contributor.author | Bauer, P | |
| dc.contributor.author | Afonso de Carvalho, S | |
| dc.contributor.author | Hoechtl, A | |
| dc.contributor.author | SAPS 3 Investigators | |
| dc.date.accessioned | 2013-08-06T16:37:25Z | |
| dc.date.available | 2013-08-06T16:37:25Z | |
| dc.date.issued | 2008 | |
| dc.description.abstract | OBJECTIVE: The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches. DESIGN: 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. SETTING: The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort. PATIENTS AND PARTICIPANTS: Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: 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. CONCLUSIONS: 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. | por |
| dc.identifier.citation | J Crit Care. 2008 Sep;23(3):339-48 | por |
| dc.identifier.uri | http://hdl.handle.net/10400.17/1429 | |
| dc.language.iso | eng | por |
| dc.peerreviewed | yes | por |
| dc.publisher | Elsevier | por |
| dc.subject | Ensaios Clínicos Como Assunto | por |
| dc.subject | Estado Terminal | por |
| dc.subject | Mortalidade Hospitalar | por |
| dc.subject | Unidades de Cuidados Intensivos | por |
| dc.subject | Modelos Estatísticos | por |
| dc.subject | Prognóstico | por |
| dc.subject | Avaliação de Risco | por |
| dc.subject | Índice de Gravidade da Doença | por |
| dc.subject | Factores de Tempo | por |
| dc.title | 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 | por |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 348 | por |
| oaire.citation.startPage | 339 | por |
| oaire.citation.title | Journal of Critical Care | por |
| rcaap.rights | openAccess | por |
| rcaap.type | article | por |