title: Identifying Patients with Bacteremia in Community-Hospital Emergency Rooms: A Retrospective Cohort Study.
abstract: OBJECTIVE
  (1) To develop a clinical prediction rule to identify patients with bacteremia, using only information that is readily available in the emergency room (ER) of community hospitals, and (2) to test the validity of that rule with a separate, independent set of data.
  
  
  METHODS
  Multicenter retrospective cohort study.
  
  
  METHODS
  To derive the clinical prediction rule we used data from 3 community hospitals in Japan (derivation). We tested the rule using data from one other community hospital (validation), which was not among the three "derivation" hospitals.
  
  
  METHODS
  Adults (age ≥ 16 years old) who had undergone blood-culture testing while in the ER between April 2011 and March 2012. For the derivation data, n = 1515 (randomly sampled from 7026 patients), and for the validation data n = 467 (from 823 patients).
  
  
  METHODS
  We analyzed 28 candidate predictors of bacteremia, including demographic data, signs and symptoms, comorbid conditions, and basic laboratory data. Chi-square tests and multiple logistic regression were used to derive an integer risk score (the "ID-BactER" score). Sensitivity, specificity, likelihood ratios, and the area under the receiver operating characteristic curve (i.e., the AUC) were computed.
  
  
  RESULTS
  There were 241 cases of bacteremia in the derivation data. Eleven candidate predictors were used in the ID-BactER score: age, chills, vomiting, mental status, temperature, systolic blood pressure, abdominal sign, white blood-cell count, platelets, blood urea nitrogen, and C-reactive protein. The AUCs was 0.80 (derivation) and 0.74 (validation). For ID-BactER scores ≥ 2, the sensitivities for derivation and validation data were 98% and 97%, and specificities were 20% and 14%, respectively.
  
  
  CONCLUSIONS
  The ID-BactER score can be computed from information that is readily available in the ERs of community hospitals. Future studies should focus on developing a score with a higher specificity while maintaining the desired sensitivity.
authors.affiliation: Department of Healthcare Epidemiology, School of Public Health in the Graduate School of Medicine, Kyoto University, Kyoto, Japan.|Division of Community and Family Medicine, Center for Community Medicine, Jichi Medical University, Tochigi, Japan.
authors.forename: Taro
authors.initials: T
authors.lastname: Takeshima
authors.affiliation: Department of Healthcare Epidemiology, School of Public Health in the Graduate School of Medicine, Kyoto University, Kyoto, Japan.|Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital, Kyoto, Japan.
authors.forename: Yosuke
authors.initials: Y
authors.lastname: Yamamoto
authors.affiliation: Department of General Internal Medicine, Japanese Red Cross Nagoya Daini Hospital, Aichi, Japan.
authors.forename: Yoshinori
authors.initials: Y
authors.lastname: Noguchi
authors.affiliation: Department of Emergency Medicine, Shizuoka General Hospital, Shizuoka, Japan.
authors.forename: Nobuyuki
authors.initials: N
authors.lastname: Maki
authors.affiliation: Biostatistics Center, Kurume University, Kurume, Fukuoka, Japan.
authors.forename: Koichiro
authors.initials: K
authors.lastname: Gibo
authors.affiliation: Department of Home Care Medicine, Tenri Hospital, Nara, Japan, Tenri Hospital, Nara, Japan.
authors.forename: Yukio
authors.initials: Y
authors.lastname: Tsugihashi
authors.affiliation: Department of General Internal Medicine and Infectious Diseases, Kobe City Medical Center General Hospital, Hyogo, Japan.
authors.forename: Asako
authors.initials: A
authors.lastname: Doi
authors.affiliation: Department of Healthcare Epidemiology, School of Public Health in the Graduate School of Medicine, Kyoto University, Kyoto, Japan.|Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital, Kyoto, Japan.
authors.forename: Shingo
authors.initials: S
authors.lastname: Fukuma
authors.affiliation: Center for Environmental Health Sciences, National Institute for Environmental Studies, Ibaraki, Japan.
authors.forename: Shin
authors.initials: S
authors.lastname: Yamazaki
authors.affiliation: Division of Community and Family Medicine, Center for Community Medicine, Jichi Medical University, Tochigi, Japan.
authors.forename: Eiji
authors.initials: E
authors.lastname: Kajii
authors.affiliation: Department of Healthcare Epidemiology, School of Public Health in the Graduate School of Medicine, Kyoto University, Kyoto, Japan.|Center for Innovative Research for Communities and Clinical Excellence (CIRC2LE), Fukushima Medical University, Fukushima, Japan.
authors.forename: Shunichi
authors.initials: S
authors.lastname: Fukuhara
mesh_terms: D000368:Aged; D016470:Bacteremia / Q000175:diagnosis* / Q000453:epidemiology; D003710:Demography; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006764:Hospitals, Community / Q000706:statistics & numerical data*; D006801:Humans; D007564:Japan / Q000453:epidemiology; D008297:Male; D008962:Models, Theoretical; D015999:Multivariate Analysis; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies