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Use of a Combination Biomarker Algorithm To Identify Medical Intensive Care Unit Patients with Suspected Sepsis at Very Low Likelihood of Bacterial Infection.
Han, Jennifer H; Nachamkin, Irving; Coffin, Susan E; Gerber, Jeffrey S; Fuchs, Barry; Garrigan, Charles; Han, Xiaoyan; Bilker, Warren B; Wise, Jacqueleen; Tolomeo, Pam; Lautenbach, Ebbing.
Afiliação
  • Han JH; Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA Department of
  • Nachamkin I; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Coffin SE; Division of Infectious Diseases, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA Center for Pediatric Clinical Effectiveness, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
  • Gerber JS; Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA Division of Infectious D
  • Fuchs B; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Garrigan C; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Han X; Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Bilker WB; Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Wise J; Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Tolomeo P; Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
  • Lautenbach E; Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA Department of
Antimicrob Agents Chemother ; 59(10): 6494-500, 2015 Oct.
Article em En | MEDLINE | ID: mdl-26239984
ABSTRACT
Sepsis remains a diagnostic challenge in the intensive care unit (ICU), and the use of biomarkers may help in differentiating bacterial sepsis from other causes of systemic inflammatory syndrome (SIRS). The goal of this study was to assess test characteristics of a number of biomarkers for identifying ICU patients with a very low likelihood of bacterial sepsis. A prospective cohort study was conducted in a medical ICU of a university hospital. Immunocompetent patients with presumed bacterial sepsis were consecutively enrolled from January 2012 to May 2013. Concentrations of nine biomarkers (α-2 macroglobulin, C-reactive protein [CRP], ferritin, fibrinogen, haptoglobin, procalcitonin [PCT], serum amyloid A, serum amyloid P, and tissue plasminogen activator) were determined at baseline and at 24 h, 48 h, and 72 h after enrollment. Performance characteristics were calculated for various combinations of biomarkers for discrimination of bacterial sepsis from other causes of SIRS. Seventy patients were included during the study period; 31 (44%) had bacterial sepsis, and 39 (56%) had other causes of SIRS. PCT and CRP values were significantly higher at all measured time points in patients with bacterial sepsis. A number of combinations of PCT and CRP, using various cutoff values and measurement time points, demonstrated high negative predictive values (81.1% to 85.7%) and specificities (63.2% to 79.5%) for diagnosing bacterial sepsis. Combinations of PCT and CRP demonstrated a high ability to discriminate bacterial sepsis from other causes of SIRS in medical ICU patients. Future studies should focus on the use of these algorithms to improve antibiotic use in the ICU setting.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Precursores de Proteínas / Infecções Bacterianas / Proteína C-Reativa / Calcitonina / Síndrome de Resposta Inflamatória Sistêmica / Sepse Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2015 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Precursores de Proteínas / Infecções Bacterianas / Proteína C-Reativa / Calcitonina / Síndrome de Resposta Inflamatória Sistêmica / Sepse Tipo de estudo: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2015 Tipo de documento: Article