title: Distinguishing infected from noninfected abdominal fluid collections after surgery: an imaging, clinical, and laboratory-based scoring system.
abstract: OBJECTIVE
  Mortality from abdominal abscesses ranges from 30% in treated cases up to 80% to 100% in patients with undrained or nonoperated abscesses. Various computed tomographic (CT) imaging features have been suggested to indicate infection of postoperative abdominal fluid collections; however, features are nonspecific and substantial overlap between infected and noninfected collections exists. The purpose of this study was to develop and validate a scoring system on the basis of CT imaging findings as well as laboratory and clinical parameters for distinguishing infected from noninfected abdominal fluid collections after surgery.
  
  
  METHODS
  The score developmental cohort included 100 consecutive patients (69 men, 31 women; mean age, 58 ± 17 years) who underwent portal-venous phase CT within 24 hours before CT-guided intervention of postoperative abdominal fluid collections. Imaging features included attenuation (Hounsfield unit [HU]), volume, wall enhancement and thickness, fat stranding, as well as entrapped gas of fluid collections. Laboratory and clinical parameters included diabetes, intake of immunosuppressive drugs, body temperature, C-reactive protein, and leukocyte blood cell count. The score was validated in a separate cohort of 30 consecutive patients (17 men, 13 women; mean age, 51 ± 15 years) with postoperative abdominal fluid collections. Microbiologic analysis from fluid samples served as the standard of reference.
  
  
  RESULTS
  Diabetes, body temperature, C-reactive protein, attenuation of the fluid collection (in HUs), wall enhancement and thickness of the wall, adjacent fat stranding, as well as entrapped gas within the fluid collection were significantly different between infected and noninfected collections (P < 0.001). Multiple logistic regression analysis revealed diabetes, C-reactive protein, attenuation of the fluid collection (in HUs), as well as entrapped gas as significant independent predictors of infection (P < 0.001) and thus was selected for constructing a scoring system from 0 to 10 (diabetes: 2 points; C-reactive protein, ≥ 100 mg/L: 1 point; attenuation of fluid collection, ≥ 20 HU: 4 points; entrapped gas: 3 points). The model was well calibrated (Hosmer-Lemeshow test, P = 0.36). In the validation cohort, scores of 2 or lower had a 90% (95% confidence interval [CI], 56%-100%) negative predictive value, scores of 3 or higher had an 80% (95% CI, 56%-94%) positive predictive value, and scores of 6 or higher a 100% (95% CI, 74%-100%) positive predictive value for diagnosing infected fluid collections. Receiver operating characteristic analysis revealed an area under the curve of 0.96 (95% CI, 0.88-1.00) for the score.
  
  
  CONCLUSIONS
  We introduce an accurate scoring system including quantitative radiologic, laboratory, and clinical parameters for distinguishing infected from noninfected fluid collections after abdominal surgery.
authors.affiliation: From the *Institute of Diagnostic and Interventional Radiology, and †Department of Surgery, Swiss Hepato-Pancreato-Biliary and Transplantation Center, University Hospital Zurich; and ‡Division of Biostatistics, Institute of Social and Preventive Medicine, University of Zurich, Zurich, Switzerland.
authors.forename: Ralph
authors.initials: R
authors.lastname: Gnannt
authors.forename: Michael A
authors.initials: MA
authors.lastname: Fischer
authors.forename: Thomas
authors.initials: T
authors.lastname: Baechler
authors.forename: Pierre-Alain
authors.initials: PA
authors.lastname: Clavien
authors.forename: Christoph
authors.initials: C
authors.lastname: Karlo
authors.forename: Burkhardt
authors.initials: B
authors.lastname: Seifert
authors.forename: Mickael
authors.initials: M
authors.lastname: Lesurtel
authors.forename: Hatem
authors.initials: H
authors.lastname: Alkadhi
mesh_terms: D000005:Abdomen; D018784:Abdominal Abscess / Q000000981:diagnostic imaging* / Q000382:microbiology*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001202:Ascitic Fluid / Q000382:microbiology*; D001831:Body Temperature; D002097:C-Reactive Protein; D003287:Contrast Media; D003937:Diagnosis, Differential; D004322:Drainage / Q000379:methods; D005122:Exudates and Transudates / Q000000981:diagnostic imaging* / Q000382:microbiology; D005260:Female; D006801:Humans; D007472:Iohexol / Q000031:analogs & derivatives; D007958:Leukocyte Count / Q000379:methods; D008297:Male; D008875:Middle Aged; D011183:Postoperative Complications / Q000000981:diagnostic imaging* / Q000382:microbiology*; D012372:ROC Curve; D011856:Radiographic Image Enhancement / Q000379:methods; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012307:Risk Factors; D014057:Tomography, X-Ray Computed / Q000379:methods; D055815:Young Adult
chemical_list: D003287:Contrast Media; D007472:Iohexol; C038192:iopromide; D002097:C-Reactive Protein