Background/Aims: Straight leg raise (SLR) can be utilized to evaluate the integrity of the esophagogastric junction during high-resolution manometry (HRM). We aim to assess the value of transient hiatal separation during SLR in symptomatic reflux patients. Methods: Consecutive reflux patients undergoing esophageal HRM and pH monitoring were included. Transient hiatal separation was defined by a ≥ 1 cm separation between the lower esophageal sphincter and crural diaphragm during SLR. We compared esophageal motor patterns and reflux monitoring parameters between patients with normal, transiently abnormal and consistently abnormal esophagogastric junction morphology during SLR. Results: Of 85 (56.3% female, mean age: 46.7 ± 12.3 years) completed SLR, esophagogastric junction morphology was normal in 31 (36.5%), transient hiatal separation in 19 (22.3%), and consistently hiatal hernia in 35 (41.2%). The values of total acid exposure time (P= 0.016), longest acid reflux episodes (P = 0.024), and DeMeester scores (P = 0.016) were higher in hiatal hernia compared to patients with non-transient hiatal separation, but there were no differences between those with and without transient hiatal separation. Within ineffective esophageal motility, the presence of transient hiatal separation during SLR significantly associated with a higher total acid exposure time (P = 0.014), higher DeMeester scores (P = 0.019), higher total acid reflux events (P = 0.037), and higher longest acid reflux episodes (P = 0.006). Conclusion Our work suggests that SLR may have value as a provocative test during HRM, and future outcome studies are warranted to elucidate the clinical relevance of motor abnormalities depicted from SLR.

Background/Aims: Intrabolus pressures are important for esophageal bolus transport and may detect obstructed bolus flow. This study measured the effect esophageal outflow obstruction experimentally induce by a leg-lift protocol. Methods: Twenty-five gastroesophageal reflux disease patients referred for esophageal manometry and a normal motility diagnosis were included. Supine liquid swallows were tested. Leg-lift protocol generated esophageal outflow obstruction by increasing abdominal pressure. Esophageal pressure topography and intrabolus pressure metrics were calculated. These included, (1) mid-domain bolus distension pressure during esophageal emptying (DPE, mmHg) and (2) ramp pressure (mmHg/sec), generated by compression of the bolus between the peristaltic contraction and esophagogastric junction (EGJ). Results: EGJ relaxation pressure was increased by leg-lift from 13 (11-17) to 19 (14-30) mmHg (P< 0.005) and distal contractile integral also increased from 1077 (883-1349) to 1620 (1268-2072) mmHg · cm · sec (P < 0.001) as a physiological response to obstruction. All bolus pressures were increased by leg lift; DPE increased from 17 (15-20) to 27 (19-32) mmHg (P< 0.001), and ramp pressure increased from 3 (1-4) to 5 (2-9) mmHg/sec (P < 0.05). Conclusion Measuring pressures within the intrabolus domain can quantify changes related to obstruction to outflow and may serve as adjunct measures for confirming a diagnosis EGJ outflow obstruction.

Background/Aims: This study aims to evaluate the effects of acute codeine administration on primary and secondary esophageal peristalsis in patients with ineffective esophageal motility (IEM). Methods: Eighteen IEM patients (8 women; mean age 37.8 years, range 23-64 years) were enrolled in the study. The patients underwent highresolution manometry exams, consisting of 10 single wet swallows, multiple rapid swallows, and ten 20 mL rapid air injections to trigger secondary peristalsis. All participants completed 2 separate sessions, including acute administration of codeine (60 mg) and placebo, in a randomized order. Results: Codeine significantly increased the distal contractile integral (566 ± 81 mmHg · s · cm vs 247 ± 36 mmHg · s · cm, P = 0.001) andshortened distal latency (5.7 ± 0.2 seconds vs 6.5 ± 0.1 seconds, P < 0.001) for primary peristalsis compared with these parameters after placebo treatment. The mean total break length decreased significantly after codeine treatment compared with the length after placebo (P= 0.003). Codeine significantly increased esophagogastric junction-contractile integral (P= 0.028) but did not change the 4-second integrated relaxation pressure (P= 0.794). Codeine significantly decreased the frequency of weak (P= 0.039) and failed contractions (P= 0.009), resulting in increased frequency of normal primary peristalsis (P < 0.136). No significant differences in the ratio of impaired multiple rapid swallows inhibition and parameters of secondary peristalsis were detected. Conclusions In IEM patients, acute administration of codeine increases contraction vigor and reduces distal latency of primary esophageal peristalsis, but has no effect on secondary peristalsis. Future studies are required to further elucidate clinical relevance of these findings, especially in the setting of gastroesophageal reflux disease with IEM.

Background/Aims: Ineffective esophageal motility (IEM) is common in patients with gastroesophageal reflux disease (GERD) and can be associated with poor esophageal contraction reserve on multiple rapid swallows. Alterations in the esophageal microbiome have been reported in GERD, but the relationship to presence or absence of contraction reserve in IEM patients has not been evaluated. We aim to investigate whether contraction reserve influences esophageal microbiome alterations in patients with GERD and IEM. Methods: We prospectively enrolled GERD patients with normal endoscopy and evaluated esophageal motility and contraction reserve with multiple rapid swallows during high-resolution manometry. The esophageal mucosa was biopsied for DNA extraction and 16S ribosomal RNA gene V3-V4 (Illumina)/full-length (Pacbio) amplicon sequencing analysis. Results: Among the 56 recruited patients, 20 had normal motility (NM), 19 had IEM with contraction reserve (IEM-R), and 17 had IEM without contraction reserve (IEM-NR). Esophageal microbiome analysis showed a significant decrease in microbial richness in patients with IEM-NR when compared to NM. The beta diversity revealed different microbiome profiles between patients with NM or IEM-R and IEM-NR (P = 0.037). Several esophageal bacterial taxa were characteristic in patients with IEM-NR, including reduced Prevotella spp.and Veillonella dispar, and enriched Fusobacterium nucleatum. In a microbiome-based random forest model for predicting IEM-NR, an area under the receiver operating characteristic curve of 0.81 was yielded. Conclusions In symptomatic GERD patients with normal endoscopic findings, the esophageal microbiome differs based on contraction reserve among IEM. Absent contraction reserve appears to alter the physiology and microbiota of the esophagus.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.