Artificial neural networks to predict presence of significant pathology in patients presenting to routine colorectal clinics.

AIM: Artificial neural networks (ANNs) are computer programs used to identify complex relations within data. Routine predictions of presence of colorectal pathology based on population statistics have little meaning for individual patient. This results in large number of unnecessary lower gastrointestinal endoscopies (LGEs - colonoscopies and flexible sigmoidoscopies). We aimed to develop a neural network algorithm that can accurately predict presence of significant pathology in patients attending routine outpatient clinics for gastrointestinal symptoms. METHOD: Ethics approval was obtained and the study was monitored according to International Committee on Harmonisation - Good Clinical Practice (ICH-GCP) standards. Three-hundred patients undergoing LGE prospectively completed a specifically developed questionnaire, which included 40 variables based on clinical symptoms, signs, past- and family history. Complete data sets of 100 patients were used to train the ANN; the remaining data was used for internal validation. The primary output used was positive finding on LGE, including polyps, cancer, diverticular disease or colitis. For external validation, the ANN was applied to data from 50 patients in primary care and also compared with the predictions of four clinicians. RESULTS: Clear correlation between actual data value and ANN predictions were found (r = 0.931; P = 0.0001). The predictive accuracy of ANN was 95% in training group and 90% (95% CI 84-96) in the internal validation set and this was significantly higher than the clinical accuracy (75%). ANN also showed high accuracy in the external validation group (89%). CONCLUSION: Artificial neural networks offer the possibility of personal prediction of outcome for individual patients presenting in clinics with colorectal symptoms, making it possible to make more appropriate requests for lower gastrointestinal endoscopy.

Results of the United Kingdoms first pilot study for nonmedical endoscopy practitioners.

OBJECTIVE: Spencer and Ready published the first description of nonmedically qualified endoscopists in 1977 [Dis Colon Rectum 1977; 20: 94]. Since then there has been an explosion of interest in this concept. Duthie et al. [Gut 1998; 43: 711] first described Nurse Endoscopy (NE) in the UK in 1998. In recent years, endoscopy services in England have been subject to review as a consequence of perceived inadequacies in service provision and delivery. METHOD: Increasing pressure on endoscopic services led the Modernisation Agency to commission a pilot study evaluating nontraditional individuals to practice flexible sigmoidoscopy (FS). A science graduate, basic grade nurse and nonclinical member of staff were successfully trained. As a result, a second pilot scheme was introduced, whereby nine nonmedically qualified individuals received education and training in FS. A curriculum was designed, developed and implemented to create an endoscopic practitioner (EP) who was both safe and competent to undertake FS. The student backgrounds included administration, health-care assistants, phlebotomist, junior doctor assistant and clinical physiologist. RESULTS: In the context of academic achievement, prescribed learning outcomes were achieved at a rate of 93% (101 from 108) of success in first attempt at assessment. In terms of practical achievement, each student demonstrated competency in FS. CONCLUSION: The curriculum designed provides a suitable framework to develop individuals with the necessary knowledge, skills and attitudes required for safe, competent practice within FS. This indicates that the curriculum provides a broad range of learning, teaching and assessment strategies befitting to a range of individuals.