Looking for "indicants" in the differential diagnosis of jaundice

Reducing variability in treatment decision-making: effectiveness of educating clinicians about uncertainty

The objective of this study was to investigate whether or not education about the concept of uncertainty reduced variability in treatment decision-making. Three small groups of dentists in North York, Canada were asked to make restorative treatment decisions about simulated bitewing radiographs. They subsequently took part in a seminar about variations in perception and judgement and were given explanations of sensitivity, specificity and receiver operating characteristic (ROC) curve analysis. A repeat reading of the radiographs was then performed by both test and control groups. Results indicated that the intervention increased the accuracy, and decreased the variability of dentists' restorative treatment decisions. Kappa statistics were 0.33, 0.34 and 0.31 before the seminar, and 0.40, 0.43 and 0.41 after the seminar. Standard errors for kappas were 0.06, 0.05 and 0.05 before the seminar, and 0.02, 0.02 and 0.05 after the seminar. The area under the ROC curve was 0.7136 before the seminar and 0.7835 after the seminar. The data demonstrate that the dentists' decisions were less variable and more accurate following the educative intervention. This study suggests that there is potential for improving consistency and accuracy in clinical decision-making through education in probabilistic reasoning.

Computer-assisted diagnosis in gastroenterology

During the last 20 years, Danish gastroenterologists and biostatisticians have been involved in practical and theoretical research aimed at providing computer-assisted decision support in clinical practice. On the practical side, we summarize two clinical applications, one in the acute abdomen, the other in jaundice. The former project is part of a worldwide effort which appears able to reduce considerably the perforated appendix rate and the negative laparotomy rate. In the latter project, also entwined in international activities, three methods of early statistical discrimination of jaundice causes have been tested, and it has been shown how ultrasonographic data can be incorporated into the diagnostic assessment. On the theoretical side, a Dutch-Danish group has been looking into how one best designs a trial and analyses its data in the context of measuring the quality and impact of machine-made diagnostic advice. Having pointed out the international ramifications of these activities, we outline what may lie ahead: Gastroenterologists must be prepared to join large-scale field trials of clinical advice-giving software. In the future, however, such software will often become embedded in general hospital recordkeeping systems.

A new approach to differential diagnosis of diseases

The main goal of this paper is to show the usefulness of the logic-combinatory approach in pattern recognition theory for developing auxiliary criteria for differential medical diagnosis, based on the methodology presented by Heathfield et al. (J Biomed Eng 13 (1991) 51-57). Firstly, we propose a change in the characterization base, from disease characterization to patient characterization then, we suggest a k-valued treatment for variables which allows us to assign them values in a wider range in order to represent different degrees in symptom manifestations. Secondly, the methodology proposed is based on Testor Theory. This theory allows us to obtain the minimum combination of features (symptoms) and the set of features combination equally discriminant (typical testors) among the diseases considered. Then, applying some classification algorithm that uses typical testors, physicians will have more making differential flexibility diagnosis.

Computer-aided decision support in hepatology

The aim of this study was to describe and to evaluate the publications of the last 30 years devoted to computer-aided decision support in clinical hepatology. The search used Medlars and references of articles. Computer-aided decision support (CADS) was classified in two categories: statistical systems and knowledge-based systems. Two specific questionnaires were used for methodologic evaluation, one for statistical systems and one for knowledge-based systems. They were filled out independently by two observers. A total of 31 papers were selected among 55 identified between 1960 and 1991. The maximum possible for the two scores was 24. The methodologic quality ranged from 4 to 22 (median, 12) for statistical systems and from 8 to 12 (median, 9) for knowledge-based systems. The poor level of methodology could explain in part the lack of utilization of computer-aided decision support in the daily clinical practice of hepatologists.

'Solubile': decision-making in the diagnosis of jaundice

We have designed a computer program 'Solubile' to aid clinicians in the diagnosis of jaundice. Based on Bayes' theorem, 'Solubile' uses up to 47 items of information about the patient to produce the most probable diagnosis from 22 possible diseases. In a prospective analysis of 50 patients, 74% were correctly diagnosed in first place and 94% within the first three choices. The possibility of using 'Solubile' at differing locations was tested by prospectively diagnosing 100 cases at a second centre having a significantly different patient population. 75% of these patients were correctly diagnosed in first place and 89% within the first three choices. The diagnostic ability of 'Solubile' was compared with that of 20 clinicians of various grades. The clinicians correctly diagnosed 49.5% of cases (Solubile 74%) and placed 68.5% (Solubile 94%) within the first three choices. 'Solubile' will be of use to aid clinicians in aspects of the diagnosis and management of jaundice.

Expert systems and the pancreatic cancer problem: decision support in the pre-operative diagnosis

In this paper, after reviewing the main issue in artificial intelligence, decision support systems, medical decision-making, expert systems and some of their applications in medicine, we focus on the diagnostic aspect of pancreatic cancer. We briefly examine the most significant applications both from the oncological and from the diagnostic point of view. We discuss the medical problems mentioning incidence and mortality, aetiological factors and diagnosis, considering the roles of surgery and adjuvant therapies. Finally we justify the decision to develop an expert system in such a medical domain and discuss the SPES (Surgical Pancreatic Expert System) project, its parts dealing with the different medical phases of pancreatic cancer diagnosis and therapy: pre-operative, intra-operative and adjuvant therapies. In particular we discuss diagnostic aspects of pancreatic cancer disease, pointing out the aims of the project, methodologies, tools used and future developments.

Use of computers in diagnosis of jaundice

The development of computer-assisted diagnostic systems for the differential diagnosis of jaundice has been attempted in several studies during the last 25-30 years. All working systems have depended on numerical methods whereas expert systems have not yet become operational. The first step in the construction of a system is the collection of a data base from a series of jaundiced patients of clinical information (indicants) i.e. symptoms, signs, and the results from laboratory tests. The best discriminating indicants are selected and processed into a mathematical rule. The performance of this rule must be tested on an independent test sample of relevant patients. The performance may also be compared to that of clinicians. So far the computerized diagnoses have not been more accurate than those of clinicians. However, computer-assisted diagnostics may form an important first step in clinical decision making regarding the selection of a confirmatory diagnostic test (direct cholangiography, ultrasonography, liver biopsy, etc.) in the evaluation of the jaundiced patient.

Physicians' diagnoses compared with algorithmic differentiation of causes of jaundice

Clinical data were collected in 194 cases of jaundiced patients treated at the "Adolfo Lopez Mateos" ISSSTE Hospital in Mexico City from July 1985 to July 1986. A copy of the clinical history of each patient was given to each of four physicians--one recently graduated from medical school, another in his first year of gastroenterology, and two others who were experienced gastroenterologists. The same clinical data were processed by a computer set up to use a modified Danish COMIC algorithm. All physicians and the computer technician were blinded to the "gold standard" pathologic diagnoses, with which their diagnoses were compared. Accuracy rates of the physicians in distinguishing intrahepatic (medical) from extrahepatic (surgical) jaundice were 78%, 86%, 86%, and 91%, and the accuracy of computer-assisted diagnoses was 96%. Chi-squared analysis of the diagnoses of three of the physicians and those of the computer showed significant differences (p between 0.1 and 0.01). For the diagnoses of the remaining physician, however, no significant difference was found after chi-squared continuity correction.

Differential diagnosis of jaundice: junior staff experience with the Copenhagen pocket chart

Originally published in 1984, the Copenhagen Pocket Chart for early differentiation between causes of jaundice has been tested with success in centres outside Denmark. Using a logistic discrimination model, it estimates probabilities of obstruction and non-obstruction in each case (and provides a further subdivision if desired). Here we evaluate its performance in the hands of young clinicians on a consecutive series of 173 jaundiced patients from two Danish hospitals. The chart performed as well as in the original series: confident diagnoses (probability greater than or equal to 0.80) were assigned to 124 patients; of these 115 proved correct (93%). In 46 patients diagnostic probabilities were less than 0.80, and 3 patients had an unknown cause of jaundice. There were 108 cases in which physician and chart were in agreement, both with a confident diagnosis, and only one of these cases was wrong. In one hospital, contributing 107 cases, each patient was independently examined by a medical student in addition to the physician's examination. Student performance was equally good, practically speaking, in particular when taking the scores on the chart into consideration. As to observer disagreement, the student and the physician typically differed on 0-2 of the chart's 21 items. In no case, however, did this lead to a confident obstructive diagnosis being changed into a confident diagnosis of non-obstruction, or vice versa.

Algorithmic diagnosis of jaundice

Extensive clinical and clinical chemical information was collected from 1002 jaundiced patients. By applying Bayes' theorem and logistic discriminant analysis, a diagnostic algorithm was developed based upon 21 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. Of the 985 patients with a final diagnosis a correct probabilistic diagnosis (obstruction vs. non-obstruction) was suggested by the algorithm in 867 patients (88%). Adopting a probability limit of 0.80, 683 patients (69%) were correctly classified, 34 patients (3.5%) were wrongly so, and 268 patients (27%) 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 valuable aid for the preliminary differential diagnosis of the jaundiced patient and can be used in the planning of a diagnostic strategy for the individual patient.