title: [The use of a simple algorithm to improve proteinuria diagnosis in clinical laboratories].
abstract: OBJECTIVE
  When proteinuria appears, a differential diagnosis must determine its origin. The object of this work has been to evaluate the results after the laboratory implantation of an algorithm for the screening and diagnosis of proteinuria.
  
  
  METHODS
  From a total of 30,718 processed urines, a 30 mg/dl or higher protein concentration was obtained in 639, recommending a new sample to confirm and differentiate proteinuria. We received 207, to which total protein, creatinine, albumin and alpha-1-microglobulin were quantified, together with pseudoperoxidase and leukocyte esterase from dipstick. The results were introduced in an expert system (UPES and its application Protis), allowing differentiate hematuria, leukocyturia and proteinuria and suggesting the assessment of other parameters, like IgG, alpha-2-macroglobulin, light chain kappa/lambda, when necessary.
  
  
  RESULTS
  From 207 urinalysis assayed for selective proteinuria, 39 were normal, 96 were classified as primary glomerulopathy, 26 as secondary glomerulopathy and 5 as tubulo-interstitial nephropathy. A differential diagnosis of hematuria was made in 58 of these urines. Besides, kappa light chains were detected in a sample from a patient with a normal serum protein graph, which were confirmed by immune fixation.
  
  
  CONCLUSIONS
  With the proposed algorithm, the information obtained from a urine sample increases substantially, allowing detection and differentiation of proteinuria and providing suggestions for the clinical evaluation of the patient.
authors.affiliation: Servicio de Análisis Clínicos, Hospital Universitario Virgen de la Arrixaca, El Palmar, Murcia. mariad.albaladejo@carm.es
authors.forename: M D
authors.initials: MD
authors.lastname: Albaladejo Otón
authors.forename: M M
authors.initials: MM
authors.lastname: González Cueva
authors.forename: R
authors.initials: R
authors.lastname: Alvarez López
authors.forename: P
authors.initials: P
authors.lastname: Martínez Hernández
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D005103:Expert Systems; D006801:Humans; D011507:Proteinuria / Q000209:etiology*; D016482:Urinalysis