The Pain System in Oesophageal Disorders: Mechanisms, Clinical Characteristics, and Treatment

Pain is common in gastroenterology. This review aims at giving an overview of pain mechanisms, clinical features, and treatment options in oesophageal disorders. The oesophagus has sensory receptors specific for different stimuli. Painful stimuli are encoded by nociceptors and communicated via afferent nerves to the central nervous system. The pain stimulus is further processed and modulated in specific pain centres in the brain, which may undergo plastic alterations. Hence, tissue inflammation and long-term exposure to pain can cause sensitisation and hypersensitivity. Oesophageal sensitivity can be evaluated ,for example, with the oesophageal multimodal probe. Treatment should target the cause of the patient's symptoms. In gastro-oesophageal reflux diseases, proton pump inhibitors are the primary treatment option, surgery being reserved for patients with severe disease resistant to drug therapy. Functional oesophageal disorders are treated with analgesics, antidepressants, and psychological therapy. Lifestyle changes are another option with less documentation

Cervical transcutaneous vagal neuromodulation in chronic pancreatitis patients with chronic pain:A randomised sham controlled clinical trial

Background & aimsChronic abdominal pain is the primary symptom of chronic pancreatitis, but unfortunately it is difficult to treat. Vagal nerve stimulation studies have provided evidence of anti-nociceptive effect in several chronic pain conditions. We investigated the pain-relieving effects of transcutaneous vagal nerve stimulation in comparison to sham treatment in chronic pancreatitis patients.MethodsWe conducted a randomised double-blinded, sham-controlled, crossover trial in patients with chronic pancreatitis. Patients were randomly assigned to receive a two-week period of cervical transcutaneous vagal nerve stimulation using the gammaCore device followed by a two-week sham stimulation, or vice versa. We measured clinical and experimental endpoints before and after each treatment. The primary clinical endpoint was pain relief, documented in a pain diary using a visual analogue scale. Secondary clinical endpoints included Patients' Global Impression of Change score, quality of life and Brief Pain Inventory questionnaire. Secondary experimental endpoints included cardiac vagal tone and heart rate.ResultsNo differences in pain scores were seen in response to two weeks transcutaneous vagal nerve stimulation as compared to sham treatment (difference in average pain score (visual analogue scale): 0.17, 95%CI (-0.86;1.20), P = 0.7). Similarly, no differences were seen for secondary clinical endpoints, except from an increase in the appetite loss score (13.9, 95%CI (0.5:27.3), P = 0.04). However, improvements in maximum pain scores were seen for transcutaneous vagal nerve stimulation and sham treatments as compared to their respective baselines: vagal nerve stimulation (-1.3±1.7, 95%CI (-2.21:-0.42), P = 0.007), sham (-1.3±1.9, 95%CI (-2.28:-0.25), P = 0.018). Finally, heart rate was decreased after two weeks transcutaneous vagal nerve stimulation in comparison to sham treatment (-3.7 beats/min, 95%CI (-6.7:-0.6), P = 0.02).ConclusionIn this sham-controlled crossover study, we found no evidence that two weeks transcutaneous vagal nerve stimulation induces pain relief in patients with chronic pancreatitis.Trial registration numberThe study is registered at NCT03357029; www.clinicaltrials.gov

Prediction of pancreatic cancer risk in patients with new-onset diabetes using a machine learning approach based on routine biochemical parameters; Prediction of Pancreatic Cancer Risk in New Onset Diabetes

ObjectiveTo develop a machine-learning model that can predict the risk of pancreatic ductal adenocarcinoma (PDAC) in people with new-onset diabetes (NOD).MethodsFrom a population-based sample of individuals with NOD aged >50 years, patients with pancreatic cancer-related diabetes (PCRD), defined as NOD followed by a PDAC diagnosis within 3 years, were included (n = 716). These PCRD patients were randomly matched in a 1:1 ratio with individuals having NOD. Data from Danish national health registries were used to develop a random forest model to distinguish PCRD from Type 2 diabetes. The model was based on age, gender, and parameters derived from feature engineering on trajectories of routine biochemical variables. Model performance was evaluated using receiver operating characteristic curves (ROC) and relative risk scores.ResultsThe most discriminative model included 20 features and achieved a ROC-AUC of 0.78 (CI:0.75–0.83). Compared to the general NOD population, the relative risk for PCRD was 20-fold increase for the 1 % of patients predicted by the model to have the highest cancer risk (3-year cancer risk of 12 % and sensitivity of 20 %). Age was the most discriminative single feature, followed by the rate of change in haemoglobin A1c and the latest plasma triglyceride level. When the prediction model was restricted to patients with PDAC diagnosed six months after diabetes diagnosis, the ROC-AUC was 0.74 (CI:0.69–0.79).ConclusionIn a population-based setting, a machine-learning model utilising information on age, sex and trajectories of routine biochemical variables demonstrated good discriminative ability between PCRD and Type 2 diabetes

Is Cambridge scoring in chronic pancreatitis the same using ERCP and MRCP?: A need for revision of standards

Purpose Grading of chronic pancreatitis (CP) is a clinical and radiologic challenge. Retrograde cholangiopancreatography (ERCP) and magnetic resonance cholangiopancreatography (MRCP) use a version of the Cambridge criteria for ductal evaluation and CP staging, but interchangeability between the modalities lacks validation. This work compares ERCP and MRCP Cambridge scores and evaluates diagnostic performance of MRCP in a large cohort of patients with CP. Methods A large radiology database was searched for CP patients who underwent MRCP between 2003 and 2013. Next, patients who also had an ERCP within 90 days of their MRCP were selected. These were categorized into mild, moderate, and severe CP using the standardized Cambridge classification for ERCP. Radiologists blinded to ERCP findings then rated MRCP with modified Cambridge scores. Results The cohort comprised 325 patients (mean age 51 years; 56% female). By ERCP Cambridge classification, 122 had mild CP, 109 moderate CP, and 94 severe CP. MRCP and ERCP showed total agreement of Cambridge score in only 43% of cases. With ERCP as reference, the sensitivity and specificity of MRCP in detecting Cambridge scores 4 + 5 (main-duct predominant) were 75.9% and 64.3%, and for Cambridge score 3 (side-branch predominant) it was 60.0% and 76.9%, respectively. Conclusions There is a lack of strong concordance between ERCP- and MRCP-based grading of CP using the Cambridge criteria. MRCP had moderate to good performance in diagnosing side-branch predominant versus main-duct predominant CP. This suggests an inherent challenge in comparing literature and calls for a revision of the standards