Pathophysiologic Signatures of Bloodstream Infection in Critically Ill Adults.

Critical care explorations(2020)

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摘要
OBJECTIVES:Bloodstream infection is associated with high mortality rates in critically ill patients but is difficult to identify clinically. This results in frequent blood culture testing, exposing patients to additional costs as well as the potential harms of unnecessary antibiotics. The purpose of this study was to assess whether the analysis of bedside physiologic monitoring data could accurately describe a pathophysiologic signature of bloodstream infection in patients admitted to the ICU. DESIGN:Development of a statistical model using physiologic data from a retrospective observational cohort. SETTING:University of Virginia Medical Center (Charlottesville, VA), a tertiary-care academic medical center. PATIENTS:Critically ill patients consecutively admitted to either the medical or surgical/trauma ICUs with available physiologic monitoring data between February 2011 and June 2015. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We analyzed 9,954 ICU admissions with 144 patient-years of vital sign and electrocardiography waveform data, totaling 1.3 million hourly measurements. There were 15,577 blood culture instances, with 1,184 instances of bloodstream infection (8%). The multivariate pathophysiologic signature of bloodstream infection was characterized by abnormalities in 15 different physiologic features. The cross-validated area under the receiver operating characteristic curve was 0.78 (95% CI, 0.69-0.85). We also identified distinct signatures of Gram-negative and fungal bloodstream infections, but not Gram-positive bloodstream infection. CONCLUSIONS:Signatures of bloodstream infection can be identified in the routine physiologic monitoring data of critically ill adults. This may assist in identifying infected patients, maximizing diagnostic stewardship, and measuring the effect of new therapeutic modalities for sepsis.
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bacteremia, critical care, fungemia,physiologic monitoring, sepsis, statistical models
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