Prolonged acid reflux monitoring using acid exposure time and DeMeester score: two days are not enough.

BACKGROUND: Wireless pH monitoring allows for a definitive GERD diagnosis, which is essential for optimal medical or surgical management of the patient. However, there is no guideline recommendation on whether prolonged pH testing (72 or 96 h) provides additional benefit when compared to the standard 48-h testing. We aimed to assess whether prolonged pH monitoring diagnoses more patients with GERD, as well as compare the DeMeester score to acid exposure time as diagnostic criteria for GERD. METHODS: This was a retrospective analysis of consecutive adult patients who underwent wireless esophageal pH monitoring between August 2018 and July 2021. The primary outcome was the additional diagnoses of GERD (predominant acid exposure pattern) in patients who underwent 48-h versus 96-h pH monitoring. Secondary outcomes included comparison of the DeMeester score to acid exposure time and internal agreement between the first and second 48-h blocks of a prolonged 96-h pH study. RESULTS: When comparing 48-h versus 96-h pH testing, the prolonged monitoring group was more likely to have a predominant reflux pattern and thus be diagnosed with definitive GERD by elevated DeMeester score (58.8% vs. 40.8%, p = 0.003) or acid exposure time > 6% (44.7% vs. 32.4%, p = 0.039). For patients who underwent prolonged testing, the results of monitoring beyond 48 h led to a clinically meaningful change in study interpretation in 24.8% of patients. The study data from Days 3 to 4 yielded only a 56.6% agreement with the first 2 days. CONCLUSIONS: In patients undergoing extended pH monitoring, almost half were found to have an abnormal pH study after a normal study on Day 1. An additional 25% of patients had a change in study interpretation by extending the study beyond 48 h. Our findings suggest only 48 h of pH monitoring will miss a diagnosis of GERD in a clinically important number of patients.

Esophagogastric Junction Opening Parameters Are Consistently Abnormal in Untreated Achalasia.

Achalasia is a disorder of impaired lower esophageal sphincter (LES) relaxation and failed peristalsis traditionally characterized by manometry.1 As impaired LES relaxation is a mechanism of reduced esophagogastric junction (EGJ) opening, abnormally reduced EGJ distensibility assessed with functional luminal imaging probe (FLIP) was reported among patients with untreated achalasia.2-5 Therefore, we aimed to describe the performance characteristics of EGJ opening parameters on FLIP panometry among a large cohort of treatment-naïve achalasia patients.

Achalasia subtypes can be identified with functional luminal imaging probe (FLIP) panometry using a supervised machine learning process.

BACKGROUND: Achalasia subtypes on high-resolution manometry (HRM) prognosticate treatment response and help direct management plan. We aimed to utilize parameters of distension-induced contractility and pressurization on functional luminal imaging probe (FLIP) panometry and machine learning to predict HRM achalasia subtypes. METHODS: One hundred eighty adult patients with treatment-naïve achalasia defined by HRM per Chicago Classification (40 type I, 99 type II, 41 type III achalasia) who underwent FLIP panometry were included: 140 patients were used as the training cohort and 40 patients as the test cohort. FLIP panometry studies performed with 16-cm FLIP assemblies were retrospectively analyzed to assess distensive pressure and distension-induced esophageal contractility. Correlation analysis, single tree, and random forest were adopted to develop classification trees to identify achalasia subtypes. KEY RESULTS: Intra-balloon pressure at 60 mL fill volume, and proportions of patients with absent contractile response, repetitive retrograde contractile pattern, occluding contractions, sustained occluding contractions (SOC), contraction-associated pressure changes >10 mm Hg all differed between HRM achalasia subtypes and were used to build the decision tree-based classification model. The model identified spastic (type III) vs non-spastic (types I and II) achalasia with 90% and 78% accuracy in the train and test cohorts, respectively. Achalasia subtypes I, II, and III were identified with 71% and 55% accuracy in the train and test cohorts, respectively. CONCLUSIONS AND INFERENCES: Using a supervised machine learning process, a preliminary model was developed that distinguished type III achalasia from non-spastic achalasia with FLIP panometry. Further refinement of the measurements and more experience (data) may improve its ability for clinically relevant application.

Accuracy of digital mRNA profiling of oesophageal biopsies as a novel diagnostic approach to eosinophilic oesophagitis.

BACKGROUND: Quantification of tissue eosinophils remains the golden standard in diagnosing eosinophilic oesophagitis (EoE), but this approach suffers from poor specificity. It has been recognized that histopathological changes that occur in patients with EoE are associated with a disease-specific tissue transcriptome. OBJECTIVE: We hypothesized that digital mRNA profiling targeted at a set of EoE-specific and Th2 inflammatory genes in oesophageal biopsies could help differentiate patients with EoE from those with reflux oesophagitis (RE) or normal tissue histology (NH). METHODS: The mRNA expression levels of 79 target genes were defined in both proximal and distal biopsies of 196 patients with nCounter® (Nanostring) technology. According to clinicopathological diagnosis, these patients were grouped in a training set (35 EoE, 30 RE, 30 NH) for building of a three-class prediction model using the random forest method, and a blinded predictive set (n = 47) for model validation. RESULTS: A diagnostic model built on ten differentially expressed genes was able to differentiate with 100% sensitivity and specificity between conditions in the training set. In a blinded predictive set, this model was able to correctly predict EoE in 14 of 18 patients in distal (sensitivity 78%, 95% CI 52-93%) and 16 of 18 patients in proximal biopsies (sensitivity 89%, 95% CI 64-98%), without false-positive diagnosis of EoE in RE or NH patients (specificity 100%, 95% CI 85-100%). Sensitivity was increased to 94% (95% CI 71-100%) when either the best predictive distal or proximal biopsy was used. CONCLUSION AND CLINICAL RELEVANCE: We conclude that mRNA profiling of oesophageal tissue is an accurate diagnostic strategy in detecting EoE.