Early prediction of septic shock in hospitalized patients.

BACKGROUND Hospitalized patients who develop severe sepsis have significant morbidity and mortality. Early goal-directed therapy has been shown to decrease mortality in severe sepsis and septic shock, though a delay in recognizing impending sepsis often precludes this intervention. OBJECTIVE To identify early predictors of septic shock among hospitalized non-intensive care unit (ICU) medical patients. DESIGN Retrospective cohort analysis. SETTING A 1200-bed academic medical center. PATIENTS Derivation cohort consisted of 13,785 patients hospitalized during 2005. The validation cohorts consisted of 13,737 patients during 2006 and 13,937 patients from 2007. INTERVENTION Development and prospective validation of a prediction model using Recursive Partitioning And Regression Tree (RPART) analysis. METHODS RPART analysis of routine laboratory and hemodynamic variables from the derivation cohort to identify predictors prior to the occurrence of shock. Two models were generated, 1 including arterial blood gas (ABG) data and 1 without. RESULTS When applied to the 2006 cohort, 347 (54.7%) and 121 (19.1%) of the 635 patients developing septic shock were correctly identified by the 2 models, respectively. For the 2007 patients, the 2 models correctly identified 367 (55.0%) and 102 (15.3%) of the 667 patients developing septic shock, respectively. CONCLUSIONS Readily available data can be employed to predict non-ICU patients who develop septic shock several hours prior to ICU admission.

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