title: A simple scoring algorithm predicting vascular infections in adults with nontyphoid Salmonella bacteremia.
abstract: BACKGROUND
  Nontyphoid Salmonella (NTS) can cause fatal vascular infections. This study aims to establish a predictive scoring algorithm to identify adults aged ≥ 50 years with NTS bacteremia who are at risk for vascular infections.
  
  
  METHODS
  There were 358 adults aged ≥ 50 years with NTS bacteremia at 2 medical centers in southern Taiwan included in this study. Multiple logistic regression was used to identify risk factors for imaging-documented vascular infections. The prediction capability of the proposed scoring algorithm was indicated by a receiver operating characteristic curve and measures of sensitivity and specificity.
  
  
  RESULTS
  Sixty patients (16.8%) with vascular infections were noted. The 4 risk factors significantly associated with vascular infections-male sex, hypertension, coronary arterial disease, and serogroup C1 infections-were each assigned +1 point to form the NTS vascular infection (NTSVI) score. In contrast, malignancy and immunosuppressive therapy were each assigned -1 point, owing to their negative associations with vascular infections. Based on the proposed NTSVI scoring, the prevalence of vascular infections in patients with ≤ 0, 1, 2, 3, or 4 points was 2.2% (3 of 138 patients), 10.6% (13 of 123 patients), 39.4% (26 of 66 patients), 55.2% (16 of 29 patients), and 100% (2 of 2 patients), respectively (P< .0001). The scoring algorithm shows an area under the curve of 0.83 (95% confidence interval, .78-.89; P < .0001). A cutoff value of +1 represents a high sensitivity (95.0%) and an acceptable specificity (45.3%).
  
  
  CONCLUSIONS
  This simple scoring algorithm can be used to identify patients with NTS bacteremia with a high risk of vascular infections. The cost-effectiveness of this algorithm should be further studied.
authors.affiliation: Internal Medicine, National Cheng Kung University Hospital, Tainan, Taiwan.
authors.forename: Po-Lin
authors.initials: PL
authors.lastname: Chen
authors.forename: Ching-Chi
authors.initials: CC
authors.lastname: Lee
authors.forename: Chung-Yi
authors.initials: CY
authors.lastname: Li
authors.forename: Chia-Ming
authors.initials: CM
authors.lastname: Chang
authors.forename: Hsin-Chun
authors.initials: HC
authors.lastname: Lee
authors.forename: Nan-Yao
authors.initials: NY
authors.lastname: Lee
authors.forename: Chi-Jung
authors.initials: CJ
authors.lastname: Wu
authors.forename: Hsin-I
authors.initials: HI
authors.lastname: Shih
authors.forename: Hung-Jen
authors.initials: HJ
authors.lastname: Tang
authors.forename: Wen-Chien
authors.initials: WC
authors.lastname: Ko
mesh_terms: D000046:Academic Medical Centers; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016470:Bacteremia / Q000150:complications* / Q000382:microbiology*; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D012475:Salmonella / Q000302:isolation & purification; D012480:Salmonella Infections / Q000175:diagnosis* / Q000382:microbiology*; D013624:Taiwan; D014657:Vasculitis / Q000175:diagnosis* / Q000382:microbiology*; D055815:Young Adult