ABSTRACT
Combined therapy using Interferon alfa (IFN) and Ribavirin (RIB) represents the standard treatment in patients with chronic hepatitis C. However, the percentage of responders to this regimen is still low, while its cost and side effects are elevated. Therefore, the possibility to predict patient's response to the above treatment is of paramount importance. The progress in the field of informatics and its large use for decision making has led to the development of novel techniques related to the so-called Artificial Intelligence, even including artificial neural networks (ANNs). In chronic viral hepatitis data are lacking. By means of an artificial neural network (ANN), 300 patients treated with IFN plus RIB were retrospectively analyzed with the aim to predict the response to the treatment. One hundred patients resulted responders and 200 non-responders at the end of treatment and during the follow up. For evaluating the prediction of treatment response, six ANNs with 16 neurons of input, an hidden layer with 7 neurons and an output layer with one neuron were utilized. The ANN model generated a positive predictive value (i.e. posterior probability of treatment response) ranging from 57% to 75% while the negative one (i.e. posterior probability of no response to treatment) was comprised between 52% and 71%. The highest level of diagnostic accuracy was 70%. In conclusion, ANNs appear to be a promising tool in the prediction of treatment response in patients with chronic hepatitis C. However, additional prospective studies are necessary to ultimately validate this predictive method.
Subject(s)
Antiviral Agents/therapeutic use , Hepatitis C, Chronic/drug therapy , Interferon-alpha/therapeutic use , Neural Networks, Computer , Ribavirin/therapeutic use , Adolescent , Adult , Aged , Antiviral Agents/pharmacology , Databases, Factual , Drug Therapy, Combination , Female , Humans , Interferon alpha-2 , Interferon-alpha/pharmacology , Male , Middle Aged , Recombinant Proteins , Ribavirin/pharmacology , Sensitivity and Specificity , Treatment OutcomeABSTRACT
BACKGROUND: To analyse the type of users that access an online Consultancy service. To understand what patient expectations are and how such a service can modify the physician/ patient relationship. EXPERIMENTAL DESIGN: retrospective investigation carried out by questionnaire which was sent out from 10/04/2001 to 05/06/2001. PARTICIPANTS: 65 users who had contacted the unit's online consultancy service. Of these 33 filled out the questionnaire. QUESTIONS: Each user was asked for anagraphic data, in full respect for his/her privacy, and an evaluation of the service. The data obtained, stored in a database, were then analysed and the percentages of each variable calculated. RESULTS: The typical user is resident in the centre-north, is aged an average of 32 with an average level of education. He chose online consultancy largely to obtain a medical opinion in a short time without leaving home and without the long waiting times usually necessary to obtain a further specialist opinion. He was generally fairly pleased with the replies received and therefore judged the system useful. CONCLUSIONS: The system is popular with users who access it to receive specific indications for treatment and to have more information about their condition. Given user expectations, it is necessary to guarantee the quality and reliability of the service and educate users in the correct use of these new technologies.
