title: Feature construction can improve diagnostic criteria for high-dimensional metabolic data in newborn screening for medium-chain acyl-CoA dehydrogenase deficiency.
abstract: BACKGROUND
  In newborn screening with tandem mass spectrometry, multiple intermediary metabolites are quantified in a single analytical run for the diagnosis of fatty-acid oxidation disorders, organic acidurias, and aminoacidurias. Published diagnostic criteria for these disorders normally incorporate a primary metabolic marker combined with secondary markers, often analyte ratios, for which the markers have been chosen to reflect metabolic pathway deviations.
  
  
  METHODS
  We applied a procedure to extract new markers and diagnostic criteria for newborn screening to the data of newborns with confirmed medium-chain acyl-CoA dehydrogenase deficiency (MCADD) and a control group from the newborn screening program, Heidelberg, Germany. We validated the results with external data of the screening center in Hamburg, Germany. We extracted new markers by performing a systematic search for analyte combinations (features) with high discriminatory performance for MCADD. To select feature thresholds, we applied automated procedures to separate controls and cases on the basis of the feature values. Finally, we built classifiers from these new markers to serve as diagnostic criteria in screening for MCADD.
  
  
  RESULTS
  On the basis of chi(2) scores, we identified approximately 800 of >628,000 new analyte combinations with superior discriminatory performance compared with the best published combinations. Classifiers built with the new features achieved diagnostic sensitivities and specificities approaching 100%.
  
  
  CONCLUSIONS
  Feature construction methods provide ways to disclose information hidden in the set of measured analytes. Other diagnostic tasks based on high-dimensional metabolic data might also profit from this approach.
authors.affiliation: Division of Metabolic Diseases, Department of General Pediatrics, University Children's Hospital, Heidelberg, Germany. Sirikit.Ho@med.uni-heidelberg.de
authors.forename: Sirikit
authors.initials: S
authors.lastname: Ho
authors.forename: Zoltan
authors.initials: Z
authors.lastname: Lukacs
authors.forename: Georg F
authors.initials: GF
authors.lastname: Hoffmann
authors.forename: Martin
authors.initials: M
authors.lastname: Lindner
authors.forename: Thomas
authors.initials: T
authors.lastname: Wetter
mesh_terms: D042964:Acyl-CoA Dehydrogenase / Q000172:deficiency*; D000465:Algorithms; D001185:Artificial Intelligence; D015415:Biomarkers / Q000097:blood; D002331:Carnitine / Q000037:antagonists & inhibitors / Q000097:blood; D006801:Humans; D007231:Infant, Newborn; D008661:Metabolism, Inborn Errors / Q000145:classification / Q000175:diagnosis*; D015997:Neonatal Screening / Q000379:methods*; D021241:Spectrometry, Mass, Electrospray Ionization; D053719:Tandem Mass Spectrometry
chemical_list: D015415:Biomarkers; D042964:Acyl-CoA Dehydrogenase; D002331:Carnitine