A biochemical prognostic model of outcome in paracetamol-induced acute liver injury

Konstantinos John Dabos, Philip Noel Newsome, John Andrew Parkinson, Janice Stewart Davidson, Ian Howard Sadler, John Nicholas Plevris, Peter Clive Hayes

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)


The aim of this study was to develop a prognostic model of outcome for patients with paracetamol induced acute liver injury based on admission parameters

We used a cohort of 97 patients admitted to the Scottish Liver Transplant Unit between 1997 and 1998 to identify biochemical prognostic markers of outcome and thus create a prognostic model. Blood samples were taken on admission for analysis. The model was subsequently validated by testing it on a second cohort of 86 patients admitted between 1999 and 2000.

The following were identified as independent variables of poor prognosis (death/ transplant); phenylalanine, pyruvate, alanine, acetate, calcium, haemoglobin and lactate. A prognostic model was then constructed by stepwise forward logistic regression analysis: (400 × Pyruvate mmols/L) + (50 × Phenylalanine (mmols/L) – (4 × Hemoglobin (g/dL). A value of <16 had an accuracy of 93% in predicting death correctly. When applied to the validation cohort this model had a positive predictive value of 91%, a negative predictive value of 94%, a sensitivity of 91%, and a specificity of 94%. On the same population overall, the positive and negative predictive value of the King's criteria were 94% and 93% respectively, whereas their sensitivity and specificity were 88% and 96% respectively.

Using admission characteristics our model is able to identify patients who die from paracetamol overdose fulminant hepatic failure as accurately as King's College criteria, but at a much earlier stage in their condition.
Original languageEnglish
Pages (from-to)1712-1717
Number of pages6
Issue number12
Publication statusPublished - 27 Dec 2005


  • hemoglobin
  • pyruvate
  • phenylalanine
  • transplantation
  • liver


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