کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5885635 1150925 2015 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Electronic ArticlesEvolution and prognosis of long intensive care unit stay patients suffering a deterioration: A multicenter study
ترجمه فارسی عنوان
مقالات الکترونیکی مقیاس و پیش آگهی بیمارانی که در معرض طولانی مدت قرار دارند، بیماران را تحت تاثیر قرار می دهند: یک مطالعه چند محوری
کلمات کلیدی
موضوعات مرتبط
علوم پزشکی و سلامت پزشکی و دندانپزشکی بیهوشی و پزشکی درد
چکیده انگلیسی

PurposeThe prognosis of a patient who deteriorates during a prolonged intensive care unit (ICU) stay is difficult to predict. We analyze the prognostic value of the serialized Sequential Organ Failure Assessment (SOFA) score and other variables in the early days after a complication and to build a new predictive score.Materials and methodsEPIPUSE (Evolución y pronóstico de los pacientes con ingreso prolongado en UCI que sufren un empeoramiento, Evolution and prognosis of long intensive care unit stay patients suffering a deterioration) study is a prospective, observational study during a 3-month recruitment period in 75 Spanish ICUs. We focused on patients admitted in the ICU for 7 days or more with complications of adverse events that involve organ dysfunction impairment. Demographics, clinical variables, and serialized SOFA after a supervening clinical deterioration were recorded. Univariate and multivariate analyses were performed, and a predictive model was created with the most discriminating variables.ResultsWe included 589 patients who experienced 777 cases of severe complication or adverse event. The entire sample was randomly divided into 2 subsamples, one for development purposes (528 cases) and the other for validation (249 cases). The predictive model maximizing specificity is calculated by minimum SOFA + 2 * cardiovascular risk factors + 2 * history of any oncologic disease or immunosuppressive treatment + 3 * dependence for basic activities of daily living. The area under the receiver operating characteristic curve is 0.82. A 14-point cutoff has a positive predictive value of 100% (92.7%-100%) and negative predictive value of 51% (46.4%-55.5%) for death.ConclusionsEPIPUSE model can predict mortality with a specificity and positive predictive value of 99% in some groups of patients.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Critical Care - Volume 30, Issue 3, June 2015, Pages 654.e1-654.e7
نویسندگان
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