Objective To review the current literature about the epidemiology, clinical presentation, diagnosis, and treatment of laryngopharyngeal reflux (LPR). Data Sources PubMed, Cochrane Library, and Scopus. Methods A comprehensive review of the literature on LPR epidemiology, clinical presentation, diagnosis, and treatment was conducted. Using the PRISMA statement, 3 authors selected relevant publications to provide a critical analysis of the literature. Conclusions The important heterogeneity across studies in LPR diagnosis continues to make it difficult to summarize a single body of thought. Controversies persist concerning epidemiology, clinical presentation, diagnosis, and treatment. No recent epidemiologic study exists regarding prevalence and incidence with the use of objective diagnostic tools. There is no survey that evaluates the prevalence of symptoms and signs on a large number of patients with confirmed LPR. Regarding diagnosis, an increasing number of authors used multichannel intraluminal impedance–pH monitoring, although there is no consensus regarding standardization of the diagnostic criteria. The efficiency of proton pump inhibitor (PPI) therapy remains poorly demonstrated and misevaluated by incomplete clinical tools that do not take into consideration many symptoms and extralaryngeal findings. Despite the recent advances in knowledge about nonacid LPR, treatment protocols based on PPIs do not seem to have evolved. Implications for Practice The development of multichannel intraluminal impedance–pH monitoring and pepsin and bile salt detection should be considered for the establishment of a multiparameter diagnostic approach. LPR treatment should evolve to a more personalized regimen, including diet, PPIs, alginate, and magaldrate according to individual patient characteristics. Multicenter international studies with a standardized protocol could improve scientific knowledge about LPR.

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BACKGROUNGFor a long time, laryngopharyngeal reflux disease (LPRD) has been treated by proton pump inhibitors (PPIs) with an uncertain success rate.AIMTo shed light the current therapeutic strategies used for LPRD in order to analysis the rationale in the LPRD treatment.METHODSThree authors conducted a PubMed search to identify papers published between January 1990 and February 2019 about the treatment of LPRD. Clinical prospective or retrospective studies had to explore the impact of medical treatment(s) on the clinical presentation of suspected or confirmed LPRD. The criteria for considering studies for the review were based on the population, intervention, comparison, and outcome framework.RESULTSThe search identified 1355 relevant papers, of which 76 studies met the inclusion criteria, accounting for 6457 patients. A total of 64 studies consisted of empirical therapeutic trials and 12 were studies where authors formally identified LPRD with pH-monitoring or multichannel intraluminal impedance-pH monitoring (MII-pH). The main therapeutic scheme consisted of once or twice daily PPIs for a duration ranged from 4 to 24 wk. The most used PPIs were omeprazole, esomeprazole, rabeprazole, lansoprazole and pantoprazole with a success rate ranging from 18% to 87%. Other composite treatments have been prescribed including PPIs, alginate, prokinetics, and H2 Receptor antagonists.CONCLUSIONRegarding the development of MII-pH and the identification of LPRD subtypes (acid, nonacid, mixed), future studies are needed to improve the LPRD treatment considering all subtypes of reflux.

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Objectives: To evaluate the role of clinical scores assessing the risk of disease severity in patients with clinical suspicion of obstructive sleep apnea syndrome (OSA). The hypothesis was tested by applying artificial intelligence (AI) to demonstrate its effectiveness in distinguishing between mild–moderate OSA and severe OSA risk. Methods: A support vector machine model (SVM) was developed from the samples included in the analysis (N = 498), and they were split into 75% for training (N = 373) with the remaining for testing (N = 125). Two diagnostic thresholds were selected for OSA severity: mild to moderate (apnea–hypopnea index (AHI) ≥ 5 events/h and AHI < 30 events/h) and severe (AHI ≥ 30 events/h). The algorithms were trained and tested to predict OSA patient severity. Results: The sensitivity and specificity for the SVM model were 0.93 and 0.80 with an accuracy of 0.86; instead, the logistic regression full mode reported a value of 0.74 and 0.63, respectively, with an accuracy of 0.68. After backward stepwise elimination for features selection, the reduced logistic regression model demonstrated a sensitivity and specificity of 0.79 and 0.56, respectively, and an accuracy of 0.67. Conclusion: Artificial intelligence could be applied to patients with symptoms related to OSA to identify individuals with a severe OSA risk with clinical-based algorithms in the OSA framework.