Abstract: Background: Understanding the relationship between distal contractile integral (DCI) and mean nocturnal baseline impedance (MNBI) could shed light on new diagnostic and treatment strategies, specifically concerning nocturnal reflux. This study aimed to assess this relationship to enhance our comprehension of the interplay between esophageal contractility and mucosal permeability.
Methods: We identified adult patients who had high resolution esophageal manometry and pH-impedance tests performed within a 30-day period between December 2018 and March 2022. A random forest model was used to identify significant predictors of MNBI, assisting with variable selection for a following regression analysis. Subsequently, both univariable and multivariable regression models were utilized to measure the association between predictors and MNBI.
Key Results: Our study included 188 patients, primarily referred for testing due to reflux. The most common motility diagnoses were normal (62%) followed by possible esophagogastric junction outflow obstruction (22%). The mean DCI was 2020 mmHg∙s∙cm and MNBI was 3.05 kΩ. The random forest model identified 12 significant predictors for MNBI, key variables being acid exposure time (AET), total proximal reflux events, intraabdominal lower esophageal sphincter length, hiatal hernia presence, and DCI. Subsequent multivariable regression analyses demonstrated log AET (β = -0.69, p = <0.001), total proximal reflux events (β = -0.16, p = 0.008), hiatal hernia presence (β = -0.82, p = 0.014), log DCI (β = 1.26, p < 0.001), and age (β = -0.13, p = 0.036) as being significantly associated with MNBI.
Conclusions and Inferences: DCI is a key manometric predictor of MNBI emphasizing the role of manometry in detecting reflux risk and the need for its consideration in reflux management.
(© 2024 John Wiley & Sons Ltd.)
References: Carlson DA, Pandolfino JE. High‐resolution Manometry in clinical practice. Gastroenterol Hepatol. 2015;11(6):374‐384.
Rengarajan A, Rogers BD, Wong Z, et al. High‐resolution Manometry thresholds and motor patterns among asymptomatic individuals. Clin Gastroenterol Hepatol. 2022;20(3):e398‐e406. doi:10.1016/j.cgh.2020.10.052.
Ghosh SK, Pandolfino JE, Rice J, Clarke JO, Kwiatek M, Kahrilas PJ. Impaired deglutitive EGJ relaxation in clinical esophageal manometry: a quantitative analysis of 400 patients and 75 controls. Am J Physiol Gastrointest Liver Physiol. 2007;293(4):G878‐G885. doi:10.1152/ajpgi.00252.2007.
Ghosh SK, Pandolfino JE, Zhang Q, Jarosz A, Shah N, Kahrilas PJ. Quantifying esophageal peristalsis with high‐resolution manometry: a study of 75 asymptomatic volunteers. Am J Physiol Gastrointest Liver Physiol. 2006;290(5):G988‐G997. doi:10.1152/ajpgi.00510.2005.
Yadlapati R, Kahrilas PJ, Fox MR, et al. Esophageal motility disorders on high‐resolution manometry: Chicago classification version 4.0©. Neurogastroenterol Motil. 2021;33(1):e14058. doi:10.1111/nmo.14058.
Tutuian R, Vela MF, Shay SS, Castell DO. Multichannel intraluminal impedance in esophageal function testing and gastroesophageal reflux monitoring. J Clin Gastroenterol. 2003;37(3):206‐215. doi:10.1097/00004836-200309000-00004.
Frazzoni M, Savarino E, de Bortoli N, et al. Analyses of the post‐reflux swallow‐induced peristaltic wave index and nocturnal baseline impedance parameters increase the diagnostic yield of impedance‐pH monitoring of patients with reflux disease. Clin Gastroenterol Hepatol. 2016;14(1):40‐46. doi:10.1016/j.cgh.2015.06.026.
Sifrim D, Roman S, Savarino E, et al. Normal values and regional differences in oesophageal impedance‐pH metrics: a consensus analysis of impedance‐pH studies from around the world. Gut. 2020.1441‐1449. doi:10.1136/gutjnl-2020-322627.
Gyawali CP, Yadlapati R, Fass R, et al. Updates to the modern diagnosis of GERD: Lyon consensus 2.0. Gut. 2024;73(2):361‐371. doi:10.1136/gutjnl-2023-330616.
Grolemund G, Wickham H. Dates and times made easy with lubridate. J Stat Softw. 2011;40(3):1‐25.
Rudis B, Muir C. Docxtractr: extract data tables and comments from “Microsoft” “word” documents. 2020. https://CRAN.R‐project.org/package=docxtractr.
Tierney N, Cook D. Expanding tidy data principles to facilitate missing data exploration, visualization and assessment of imputations. J Stat Softw. 2023;105(7):1‐31. doi:10.18637/jss.v105.i07.
Kahrilas PJ, Bredenoord AJ, Fox M, et al. The Chicago classification of esophageal motility disorders, v3.0. Neurogastroenterol Motil. 2015;27(2):160‐174. doi:10.1111/nmo.12477.
van der Ploeg T, Steyerberg EW. Feature selection and validated predictive performance in the domain of legionella pneumophila: a comparative study. BMC Res Notes. 2016;9:147. doi:10.1186/s13104-016-1945-2.
Hapfelmeier A, Ulm K. A new variable selection approach using random forests. Computational Statistics & Data Analysis. 2013;60:50‐69. doi:10.1016/j.csda.2012.09.020.
Breiman L. Random forests. Mach Learn. 2001;45(1):5‐32. doi:10.1023/A:1010933404324.
Sauerbrei W, Royston P, Binder H. Selection of important variables and determination of functional form for continuous predictors in multivariable model building. Stat Med. 2007;26(30):5512‐5528.
Altmann A, Toloşi L, Sander O, Lengauer T. Permutation importance: a corrected feature importance measure. Bioinformatics. 2010;26(10):1340‐1347. doi:10.1093/bioinformatics/btq134.
Genuer R, Poggi JM, Tuleau‐Malot C. Variable selection using random forests. Pattern Recogn Lett. 2010;31(14):2225‐2236.
Kuhn M, Johnson K. Applied Predictive Modeling. Springer; 2013. doi:10.1007/978-1-4614-6849-3.
R Core Team. R: A Language and Environment for Statistical Computing. 2023. https://www.R‐project.org/.
Rich B. Table1: Tables of Descriptive Statistics in HTML. 2023. https://CRAN.R‐project.org/package=table1.
Wickham H. Ggplot2: Elegant Graphics for Data Analysis. Springer‐Verlag; 2016. https://ggplot2.tidyverse.org.
Wright MN, Ziegler A. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. doi:10.18637/jss.v077.i01. arXiv.org.
Kuhn M, Wickham H. Tidymodels: A Collection of Packages for Modeling and Machine Learning Using Tidyverse Principles. 2020. https://www.tidymodels.org.
Lüdecke D. sjPlot: Data Visualization for Statistics in Social Science. 2023. https://CRAN.R‐project.org/package=sjPlot.
Patel A, Wang D, Sainani N, Sayuk GS, Gyawali CP. Distal mean nocturnal baseline impedance on pH‐impedance monitoring predicts reflux burden and symptomatic outcome in gastro‐oesophageal reflux disease. Aliment Pharmacol Ther. 2016;44(8):890‐898. doi:10.1111/apt.13777.
Zhong C, Duan L, Wang K, et al. Esophageal intraluminal baseline impedance is associated with severity of acid reflux and epithelial structural abnormalities in patients with gastroesophageal reflux disease. J Gastroenterol. 2013;48(5):601‐610. doi:10.1007/s00535-012-0689-6.
Ravi K, Geno DM, Vela MF, Crowell MD, Katzka DA. Baseline impedance measured during high‐resolution esophageal impedance manometry reliably discriminates GERD patients. Neurogastroenterol Motil. 2017;29(5): e12974. doi:10.1111/nmo.12974.
O'Sullivan GC, DeMeester TR, Joelsson BE, et al. Interaction of lower esophageal sphincter pressure and length of sphincter in the abdomen as determinants of gastroesophageal competence. Am J Surg. 1982;143(1):40‐47. doi:10.1016/0002-9610(82)90127-1.
Bonavina L, Evander A, DeMeester TR, et al. Length of the distal esophageal sphincter and competency of the cardia. Am J Surg. 1986;151(1):25‐34. doi:10.1016/0002-9610(86)90007-3.
Hershcovici T, Mashimo H, Fass R. The lower esophageal sphincter. Neurogastroenterol Motil. 2011;23(9):819‐830. doi:10.1111/j.1365-2982.2011.01738.x.
Rogers BD, Rengarajan A, Ribolsi M, et al. Postreflux swallow‐induced peristaltic wave index from pH‐impedance monitoring associates with esophageal body motility and esophageal acid burden. Neurogastroenterol Motil. 2021;33(2):e13973. doi:10.1111/nmo.13973.
Rogers BD, Rengarajan A, Mauro A, et al. Fragmented and failed swallows on esophageal high‐resolution manometry associate with abnormal reflux burden better than weak swallows. Neurogastroenterol Motil. 2020;32(2):e13736. doi:10.1111/nmo.13736.
Gyawali CP, Sifrim D, Carlson DA, et al. Ineffective esophageal motility: concepts, future directions, and conclusions from the Stanford 2018 symposium. Neurogastroenterol Motil. 2019;31(9):e13584. doi:10.1111/nmo.13584.
Kahrilas PJ, Dodds WJ, Hogan WJ. Effect of peristaltic dysfunction on esophageal volume clearance. Gastroenterology. 1988;94(1):73‐80. doi:10.1016/0016-5085(88)90612-9.
Anggiansah A, Taylor G, Bright N, et al. Primary peristalsis is the major acid clearance mechanism in reflux patients. Gut. 1994;35(11):1536‐1542. doi:10.1136/gut.35.11.1536.
Adkins C, Takakura W, Spiegel BMR, et al. Prevalence and characteristics of dysphagia based on a population‐based survey. Clin Gastroenterol Hepatol. 2020;18(9):1970‐1979.e2. doi:10.1016/j.cgh.2019.10.029.
El‐Serag HB, Sweet S, Winchester CC, Dent J. Update on the epidemiology of gastro‐oesophageal reflux disease: a systematic review. Gut. 2014;63(6):871‐880. doi:10.1136/gutjnl-2012-304269.
Processing Request
No Comments.