Literature DB >> 18821035

Medical complications drive length of stay after brain hemorrhage: a cohort study.

Andrew M Naidech1, Bernard R Bendok, Paul Tamul, Sarice L Bassin, Charles M Watts, H Hunt Batjer, Thomas P Bleck.   

Abstract

INTRODUCTION: Longer length of stay (LOS) is associated with higher complications and costs in ICU patients, while hospital protocols may decrease complications and LOS. We hypothesized that medical complications would increase LOS after spontaneous subarachnoid (SAH) and intracerebral (ICH) hemorrhage after accounting for severity of neurologic injury in a cohort of consecutively admitted patients.
METHODS: We prospectively recorded admission characteristics, hospital complications, and LOS for 122 patients with SAH and 56 patients with ICH from February 2006 through March 2008. A multidisciplinary Neuro-ICU team included a dedicated pharmacist and intensivist on daily rounds. Hospital protocols set glucose control with intravenous insulin, ventilator bundles, pharmacist involvement, and hand hygiene. Associations were explored with univariate statistics (t-tests, ANOVA, or non-parametric statistics as appropriate) and linear regression (repeated after log transformation of ICU and hospital LOS).
RESULTS: Factors associated with longer LOS after SAH and ICH were similar. In both SAH and ICH the strongest drivers of LOS were infection, fever, and acute lung injury. For SAH, vasospasm and Glasgow Coma Scale were also significant in some models, while in patients with ICH the volume of the initial bleed was significant in some models.
CONCLUSION: LOS after spontaneous brain hemorrhage is driven by medical complications even after the adoption of dedicated intensive care medical staff, pharmacist involvement, and evidence-based protocols for ICU care. Further alterations in care will be necessary to eliminate "preventable" complications and minimize LOS after brain hemorrhage.

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Year:  2008        PMID: 18821035     DOI: 10.1007/s12028-008-9148-x

Source DB:  PubMed          Journal:  Neurocrit Care        ISSN: 1541-6933            Impact factor:   3.210


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10.  Improving the Accuracy of Scores to Predict Gastrostomy after Intracerebral Hemorrhage with Machine Learning.

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