title: A novel scoring system for prognostic prediction in d-galactosamine/lipopolysaccharide-induced fulminant hepatic failure BALB/c mice.
abstract: BACKGROUND
  It is frequently important to identify the prognosis of fulminant hepatic failure (FHF) patients as this will influence patient management and candidacy for liver transplantation. Therefore, a novel scoring system based on metabonomics combining with multivariate logistic regression was developed to predict the prognosis of FHF mouse model.
  
  
  METHODS
  BALB/c mice were used to construct FHF model. Parts of plasma were collected at 4, 5, and 6-h time points after treatment, respectively, and detected using gas chromatography/time-of-flight mass spectrometry (GC/TOFMS). The acquired data were processed using partial least square discriminant analysis (PLS-DA). The metabolic markers identified were used to construct a scoring system by multivariate regression analysis.
  
  
  RESULTS
  28 mice of survival group and 28 of dead group were randomly selected and analyzed. PLS regression analysis showed that both the PLS models of 5 h and 6 h after d-galactosamine/lipopolysaccharide treatment demonstrated good performances. Loadings plot suggested that phosphate, beta-hydroxybutyrate (HB), urea, glucose and lactate concentrations in plasma had the highest weightings on the clustering differences at the three time points. By the multivariate logistic regression analysis, the death/survival index (DSI) was constructed based on relative concentrations of HB, urea and phosphate. It provided general accurate rate of prediction of 93.3% in the independent samples.
  
  
  CONCLUSIONS
  The novel scoring system based on metabonomics combining with multivariate logistic regression is accurate in predicting the prognosis of FHF mouse model and may be referred in clinical practice as a more useful prognostic tool with other available information.
authors.affiliation: Hepatology Institute, Peking University People's Hospital, No. 11 Xizhimen South Street, Beijing 100044, PR China. xyfyfb_1@sina.com
authors.forename: Bo
authors.initials: B
authors.lastname: Feng
authors.forename: Sheng Ming
authors.initials: SM
authors.lastname: Wu
authors.forename: Sa
authors.initials: S
authors.lastname: Lv
authors.forename: Feng
authors.initials: F
authors.lastname: Liu
authors.forename: Hong Song
authors.initials: HS
authors.lastname: Chen
authors.forename: Yan
authors.initials: Y
authors.lastname: Gao
authors.forename: Fang Ting
authors.initials: FT
authors.lastname: Dong
authors.forename: Lai
authors.initials: L
authors.lastname: Wei
mesh_terms: D020155:3-Hydroxybutyric Acid / Q000097:blood; D000818:Animals; D015415:Biomarkers / Q000097:blood; D004195:Disease Models, Animal; D005688:Galactosamine / Q000009:adverse effects; D008070:Lipopolysaccharides / Q000009:adverse effects; D017114:Liver Failure, Acute / Q000097:blood* / Q000139:chemically induced / Q000175:diagnosis*; D016015:Logistic Models; D008297:Male; D055432:Metabolomics / Q000379:methods*; D051379:Mice; D008807:Mice, Inbred BALB C; D015233:Models, Statistical; D010710:Phosphates / Q000097:blood; D011237:Predictive Value of Tests; D011379:Prognosis; D012255:Ribitol / Q000097:blood; D014508:Urea / Q000097:blood
chemical_list: D015415:Biomarkers; D008070:Lipopolysaccharides; D010710:Phosphates; D012255:Ribitol; D005688:Galactosamine; D014508:Urea; D020155:3-Hydroxybutyric Acid