title: A One-Nearest-Neighbor Approach to Identify the Original Time of Infection Using Censored Baboon Sepsis Data.
abstract: OBJECTIVE
  Sepsis therapies have proven to be elusive because of the difficulty of translating biologically sound and effective interventions in animal models to humans. A part of this problem originates from the fact that septic patients present at various times after the onset of sepsis, whereas the exact time of infection is controlled in animal models. We sought to determine whether data mining longitudinal physiologic data in a nonhuman primate model of Escherichia coli-induced sepsis could help inform the time of onset of infection.
  
  
  METHODS
  A nearest-neighbor approach was used to back cast the time of onset of infection in animal models of sepsis. Animal data were censored to simulate prospective monitoring at any moment along the septic infection. This was compared against an uncensored database to find the most similar animal in order to estimate the infection onset time. Leave-one-out cross-validation was used for validation. Biomarker selection was performed based on the criteria of estimation accuracy and/or ease of measurement.
  
  
  METHODS
  Computational experimental on existing experimental data.
  
  
  METHODS
  Retrospective data from 33 septic baboons (Papio ursinus) subjected to Escherichia coli infusion. Validation was performed using 14 pigs that were subjected to surgically induced fecal peritonitis and 22 pigs that were subjected to lipopolysaccharide infusion.
  
  
  RESULTS
  Longitudinal physiologic and serum markers, time of death. The presence of uniquely changing biomarkers during septic infection enabled the estimation of infection onset time in the datasets. Various combinations of temporal biomarkers, such as WBC, oxygen content, mean arterial pressure, and heart rate, yielded estimation accuracies of up to 97.8%. The use of temporal vital signs and a single measurement of serum biomarkers yielded highly accurate estimates without the need for invasive measurements. Validation in the pig data revealed similar results despite the heterogeneity of multiple experimental cohorts. This suggests that the method may be effective if sufficiently similar subjects are present in the database.
  
  
  CONCLUSIONS
  One nearest-neighbor analysis showed promise in accurately identifying the onset of infection given a database of known infection times and of sufficient breadth. We suggest that this approach is ready for evaluation within the clinical setting using human data.
authors.affiliation: 1Department of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA. 2Clinical Research, Investigation, and Systems Modeling of Acute Illness Laboratory (CRISMA), Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA. 3McGowan Institute for Regenerative Medicine, University of Pittsburgh and UPMC, Pittsburgh, PA. 4Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA. 5Department of Mathematics, University of Pittsburgh, Pittsburgh, PA. 6Ludwig Boltzmann Institute for Experimental and Clinical Traumatology, AUVA Research Center, Vienna, Austria.
authors.forename: Li Ang
authors.initials: LA
authors.lastname: Zhang
authors.forename: Robert S
authors.initials: RS
authors.lastname: Parker
authors.forename: David
authors.initials: D
authors.lastname: Swigon
authors.forename: Ipsita
authors.initials: I
authors.lastname: Banerjee
authors.forename: Soheyl
authors.initials: S
authors.lastname: Bahrami
authors.forename: Heinz
authors.initials: H
authors.lastname: Redl
authors.forename: Gilles
authors.initials: G
authors.lastname: Clermont
mesh_terms: D000465:Algorithms; D000818:Animals; D062186:Arterial Pressure; D015415:Biomarkers / Q000097:blood; D019295:Computational Biology; D057225:Data Mining; D004195:Disease Models, Animal; D004927:Escherichia coli Infections / Q000150:complications*; D006339:Heart Rate; D007958:Leukocyte Count; D010100:Oxygen / Q000097:blood; D010215:Papio; D010363:Pattern Recognition, Automated; D012189:Retrospective Studies; D018805:Sepsis / Q000382:microbiology / Q000503:physiopathology*; D013552:Swine; D013997:Time Factors
chemical_list: D015415:Biomarkers; D010100:Oxygen