SUMMARY BackgroundThere has been increasing interest in small intestinal bacterial overgrowth (SIBO) after reports of a link with irritable bowel syndrome (IBS), yet our understanding of this entity is limited.

Obstructive sleep apnoea (OSA) is a common disorder with numerous potential sequelae. Although the majority of these consequences can be reduced with appropriate treatment, only limited data exist regarding the natural progression ofthis disorder in untreated individuals. We hereby report a long-term follow-up of all untreated patients (n = 40) followed-up in the Technion Sleep Clinic, using both subjective and objective measurements. In addition, we report a long-term follow-up of 11 patients who attempted dietary weight loss. The average time interval between the first and second polysomnographies for the untreated group was 5.0 +/- 2.8 yrs, and 2.5 +/- 2.3 yrs for the weight reduction group. There was no significant change in Body Mass Index (BMI) or Respiratory Disturbance Index (RDI) between the two Polysomnographic (PSG) evaluations in the untreated patients. However, eight patients developed hypertension (n=5) or ischaemic heart disease (IHD) (n=3) between the two evaluations. RDI, age and BMI at the time ofthe initial evaluation were not predictive of changes in RDI, snoring intensity or minimal oxygen saturation. However, the patients who developed hypertension/IHD had significantly higher RDI than the patients who did not (46 +/- 27 vs. 23 +/- 17 h(-1), P < 0.005). In the weight-loss group, BMI decreased by a mean of 3.1 kg m(-2), and RDI decreased by 20events h(-1), P<0.05 for both. There was a significant correlation between the weight loss and improvement in RDI (R = 0.75, P = 0.005). We conclude that in untreated obstructive sleep apnoea patients RDI does not necessarily increase over time, but associated hypertension or ischaemic heart disease may develop. When weight loss is successfully achieved, sleep apnoea significantly improves with a high correlation between the extent of weight loss and the improvement in apnoea status.

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Background Pancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC in the blood and urine, but these cannot be used for population-based screening as this would be prohibitively expensive and potentially harmful. Methods We conducted a case-control study using prospectively-collected electronic health records from primary care individually-linked to cancer registrations. Our cases were comprised of 1,139 patients, aged 15–99 years, diagnosed with pancreatic cancer between January 1, 2005 and June 30, 2009. Each case was age-, sex- and diagnosis time-matched to four non-pancreatic (cancer patient) controls. Disease and prescription codes for the 24 months prior to diagnosis were used to identify 57 individual symptoms. Using a machine learning approach, we trained a logistic regression model on 75% of the data to predict patients who later developed PC and tested the model’s performance on the remaining 25%. Results We were able to identify 41.3% of patients < = 60 years at ‘high risk’ of developing pancreatic cancer up to 20 months prior to diagnosis with 72.5% sensitivity, 59% specificity and, 66% AUC. 43.2% of patients >60 years were similarly identified at 17 months, with 65% sensitivity, 57% specificity and, 61% AUC. We estimate that combining our algorithm with currently available biomarker tests could result in 30 older and 400 younger patients per cancer being identified as ‘potential patients’, and the earlier diagnosis of around 60% of tumours. Conclusion After further work this approach could be applied in the primary care setting and has the potential to be used alongside a non-invasive biomarker test to increase earlier diagnosis. This would result in a greater number of patients surviving this devastating disease.

BackgroundPancreatic cancer (PC) represents a substantial public health burden. Pancreatic cancer patients have very low survival due to the difficulty of identifying cancers early when the tumour is localised to the site of origin and treatable. Recent progress has been made in identifying biomarkers for PC in the blood and urine, but these cannot be used for population-based screening as this would be prohibitively expensive and potentially harmful. MethodsWe conducted a case-control study using prospectively-collected electronic health records from primary care individually-linked to cancer registrations. Our cases were comprised of 1,139 patients, aged 15-99 years, diagnosed with pancreatic cancer between January 1, 2005 and June 30, 2009. Each case was age-, sex-and diagnosis time-matched to four non-pancreatic (cancer patient) controls. Disease and prescription codes for the 24 months prior to diagnosis were used to identify 57 individual symptoms. Using a machine learning approach, we trained a logistic regression model on 75% of the data to predict patients who later developed PC and tested the model's performance on the remaining 25%. ResultsWe were able to identify 41.3% of patients < = 60 years at 'high risk' of developing pancreatic cancer up to 20 months prior to diagnosis with 72.5% sensitivity, 59% specificity and, 66% AUC. 43.2% of patients >60 years were similarly identified at 17 months, with 65% sensitivity, 57% specificity and, 61% AUC. We estimate that combining our algorithm with