OBJECTIVE: To assess the accuracy of newly developed software for detection of gastro-esophageal reflux episodes in ambulatory 24-h impedance tracings. MATERIAL AND METHODS. 24-h esophageal impedance recordings obtained from 60 consecutive patients with reflux symptoms were used in this study. The impedance tracings of the first 10 consecutive patients were analyzed manually by three investigators. Liquid-containing reflux episodes and their proximal extent were scored. A consensus between the three investigators was used as a gold standard. Computer analysis using dedicated software was performed, and the results were compared with the results of the consensus agreement. In addition, in order to assess the accuracy of symptom association analysis 24-h impedance tracings of all 60 patients were analyzed both manually by one investigator and using computer software. The number of reflux episodes and the results of symptom association analysis obtained by the human and computer analysis software were compared. RESULTS: The consensus meeting resulted in a total of 625 reflux episodes. The mean sensitivity and the percentage of true-positives of analysis by individual investigators was 89±1% and 94±1%, respectively. Automated analysis had a sensitivity of 73±4% and a proportion of true-positive reflux episodes of 62±8%. Symptom association analysis performed by the computer and a human observer showed concordant results in 83% of the patients. CONCLUSIONS: Although not as good as manual analysis by experts, computer analysis can be a helpful tool to identify reflux episodes and to assess the relationship between reflux episodes and symptoms.
OBJECTIVE: To assess the accuracy of newly developed software for detection of gastro-esophageal reflux episodes in ambulatory 24-h impedance tracings. MATERIAL AND METHODS. 24-h esophageal impedance recordings obtained from 60 consecutive patients with reflux symptoms were used in this study. The impedance tracings of the first 10 consecutive patients were analyzed manually by three investigators. Liquid-containing reflux episodes and their proximal extent were scored. A consensus between the three investigators was used as a gold standard. Computer analysis using dedicated software was performed, and the results were compared with the results of the consensus agreement. In addition, in order to assess the accuracy of symptom association analysis 24-h impedance tracings of all 60 patients were analyzed both manually by one investigator and using computer software. The number of reflux episodes and the results of symptom association analysis obtained by the human and computer analysis software were compared. RESULTS: The consensus meeting resulted in a total of 625 reflux episodes. The mean sensitivity and the percentage of true-positives of analysis by individual investigators was 89±1% and 94±1%, respectively. Automated analysis had a sensitivity of 73±4% and a proportion of true-positive reflux episodes of 62±8%. Symptom association analysis performed by the computer and a human observer showed concordant results in 83% of the patients. CONCLUSIONS: Although not as good as manual analysis by experts, computer analysis can be a helpful tool to identify reflux episodes and to assess the relationship between reflux episodes and symptoms.
Authors: Thomas Ciecierega; Benjamin L Gordon; Anna Aronova; Carl V Crawford; Rasa Zarnegar Journal: J Gastrointest Surg Date: 2015-04-16 Impact factor: 3.452
Authors: Nigel J Trudgill; Daniel Sifrim; Rami Sweis; Mark Fullard; Kumar Basu; Mimi McCord; Michael Booth; John Hayman; Guy Boeckxstaens; Brian T Johnston; Nicola Ager; John De Caestecker Journal: Gut Date: 2019-07-31 Impact factor: 23.059