An algorithm for differential diagnosis in jaundice and its applications.
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.
D000465:Algorithms; D001499:Bayes Theorem; D003625:Data Collection; D003937:Diagnosis, Differential; D006801:Humans; D007565:Jaundice / Q000145:classification / Q000175:diagnosis*; D011336:Probability