• algorithm;
  • differential diagnosis;
  • jaundice

ABSTRACT— This paper shows that an algorithm for differential diagnosis of jaundice developed in Denmark has been successfully transferred for use in a Swedish hospital. The algorithm, which is based on data from nearly 1000 patients, utilises 21 items of information from the medical history, physical examination and blood chemistry. The algorithm recognises four diagnostic groups: benign obstructive jaundice, malignant obstructive jaundice, acute non-obstructive jaundice, and chronic non-obstructive jaundice. To each item of information, a score is attached reflecting its weight of evidence. Summing the scores for the symptoms and signs that are present leads to a probabilistic statement about the diagnosis. Because of missing data in the Swedish patient material, three of the items were excluded from the original algorithm. Corrections were made for differences in the distribution of diseases. In reclassification of 985 Danish patients the modified algorithm's “best bid”, i.e. the diagnosis given the highest probability, was correct in 78% of cases. More important, 93% of the cases given a “confident” diagnosis (probability >0.80) were correct. The corresponding figures when the algorithm was applied to Swedish patients were 76% and 93%. respectively. In both series the predicted probabilities were matched by a corresponding proportion of actual diagnostic hits. It is concluded that the algorithm leads to reliable estimates of diagnostic probabilities in jaundice and that the algorithm seems to work well in Sweden also.