Document Detail


An algorithm for differential diagnosis in jaundice and its applications.
MedLine Citation:
PMID:  3767199     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
During the recent years a broad spectrum of diagnostic methods have appeared for the differentiation of obstructive and nonobstructive jaundice: ultrasound examination, CT-scan, direct cholangiography, etc. These investigations are costly and not without risks. It is therefore essential to devise an optimal diagnostic strategy for each patient. Extensive clinical and clinical chemical information was collected from 1,002 jaundiced patients. By application of Bayes' theorem and logistic discriminant analysis a diagnostic algorithm was developed based upon 21 variables of the 107 variables collected. This algorithm permitted a probabilistic classification of jaundiced patients into four diagnostic categories: acute non-obstructive, chronic non-obstructive, benign obstructive and malignant obstructive jaundice. Adopting a probability limit of 0.80, 683 patients (69 p. 100) were correctly classified, 34 patients (3.5 p. 100) were wrongly so, and 268 patients (27 p. 100) could not be classified with a probability above 0.80 (doubtful cases). The algorithm was also tested in a further series of 110 jaundiced patients and found to perform equally well: 88 patients classified, 22 patients remaining doubtful. Patients with doubtful diagnoses should be referred to a non-invasive test such as ultrasound examination, whereas patients with definite diagnoses can be referred to invasive tests (liver biopsy, direct cholangiography) as appropriate. The diagnostic algorithm seems to be a reliable tool for the primary differential diagnosis of the jaundiced patient and can be used in the planning of further diagnostic tests for the individual patient.
Authors:
A Malchow-Møller
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Annales de médecine interne     Volume:  137     ISSN:  0003-410X     ISO Abbreviation:  Ann Med Interne (Paris)     Publication Date:  1986  
Date Detail:
Created Date:  1986-10-30     Completed Date:  1986-10-30     Revised Date:  2004-11-17    
Medline Journal Info:
Nlm Unique ID:  0171744     Medline TA:  Ann Med Interne (Paris)     Country:  FRANCE    
Other Details:
Languages:  eng     Pagination:  274-8     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Bayes Theorem
Data Collection
Diagnosis, Differential
Humans
Jaundice / classification,  diagnosis*
Probability

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