Haylett, K.R.; Vales, P.; McCloy, R.F.
April 2003
Gut;Apr2003 Supplement 1, Vol. 52, pA48
Academic Journal
Background and Aims: Ambulatory pH studies are widely accepted as the "Gold Standard" for detecting acid reflux within the oesophagus. However, many difficulties with analyses still exist. Patients frequently have evidence of reflux, on the day of the test, but no correlation with symptoms can be clinically found. This has led some investigators to suggest that more than one pH study would have to be done to identify the reflux pattern for each individual. The aim of this study was to carry out cluster-based analyses on a large database of ambulatory pH investigations and to compare the results of those patients with a clinical correlation of symptoms with acid reflux with those where no such correlation was found. Methods: A self-organising neural network was used to partition the data from 900 clinical investigations into clusters. First, similar clusters between the two groups were found, eg investigations with reflux with or without correlating symptoms. Then the numbers of cases within the similar clusters were compared to give an estimate of the probability of correlating reflux with symptoms. Results: The results show that investigations with reflux can be divided into four classes. Those patients with the highest values of reflux fell into a class indicating that there is up to a 78% probability of reflux correlating with symptoms on the day of the investigation. While those patients with the lowest levels of reflux have a 49% probability of symptoms correlating with reflux. Discussion: The developed neural network allows the data from any study to be placed into the most appropriate class. This enables the probability of the symptoms being related to the observed reflux to be estimated despite no correlation being found on the day. This study suggests that, by using historical data to form the basis of the network and applying these techniques, a series of repeat tests are not necessary to assess the individual patient.


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