title: Clinical validation of an artificial neural network trained to identify acute allograft rejection in liver transplant recipients.
abstract: Artificial neural networks (ANNs) are techniques of nonlinear data modeling that have been studied in a wide variety of medical applications. An ANN was developed to assist in the diagnosis of acute rejection in liver transplant recipients. We investigated the diagnostic accuracy of this ANN on a new data set of patients from the same hospital. In addition, we compared the diagnostic accuracy of the ANN with that of the individual input variables (alanine aminotransferase [ALT] and bilirubin levels and day posttransplantation). Clinical and biochemical data were collected retrospectively for 124 consecutive liver transplantations (117 patients) over the first 3 months after transplantation. Diagnostic accuracy was calculated using receiver operating characteristic (ROC) curve analysis. The ANN differentiated rejection from rejection-free episodes in the new data set over the first 3 months posttransplantation with an area under the ROC curve of 0.902 and sensitivity and specificity of 80.0% and 90.1% at the optimum decision threshold, respectively. The ANN was significantly more specific than ALT or bilirubin level or day posttransplantation at their corresponding optimum decision thresholds (P <.0001). Peak ANN output occurred 1 day earlier than peak values for either ALT or bilirubin (P <.005). The diagnostic accuracy of the ANN was greater than that of any of the individual variables that had been used as inputs. It would be a useful adjunct to conventional liver function tests for monitoring liver transplant recipients in the early postoperative period.
authors.affiliation: Department of Clinical Biochemistry, Addenbrooke's Hospital, Cambridge, England. vhughes@netscape.net
authors.forename: V F
authors.initials: VF
authors.lastname: Hughes
authors.forename: D G
authors.initials: DG
authors.lastname: Melvin
authors.forename: M
authors.initials: M
authors.lastname: Niranjan
authors.forename: G A
authors.initials: GA
authors.lastname: Alexander
authors.forename: A K
authors.initials: AK
authors.lastname: Trull
mesh_terms: D000208:Acute Disease; D000293:Adolescent; D000328:Adult; D000368:Aged; D000410:Alanine Transaminase / Q000097:blood; D001663:Bilirubin / Q000097:blood; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006084:Graft Rejection / Q000097:blood / Q000175:diagnosis* / Q000201:enzymology; D006801:Humans; D016031:Liver Transplantation / Q000009:adverse effects*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D012189:Retrospective Studies; D013997:Time Factors
chemical_list: D000410:Alanine Transaminase; D001663:Bilirubin