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Esophageal Dysphagia in Children: State of the Art and Proposal for a Symptom-Based Diagnostic Approach.

ABSTRACT: Pediatric esophageal dysphagia (PED) is an infrequent condition that can be determined by a large number of disorders. The etiologic diagnosis is challenging due to overlapping clinical phenotypes and to the absence of pediatric diagnostic guidelines. This review aims to summarize the most relevant causes of ED during childhood, highlight the clinical scenarios of PED presentation and discuss the indications of available diagnostic tools. Available information supports that PED should always be investigated as it can underlie life-threatening conditions (e.g., foreign body ingestion, mediastinal tumors), represent the complication of benign disorders (e.g., peptic stenosis) or constitute the manifestation of organic diseases (e.g., eosinophilic esophagitis, achalasia). Therefore, the diagnosis of functional PED should be made only after excluding mucosal, structural, or motility esophageal abnormalities. Several clinical features may contribute to the diagnosis of PED. Among the latter, we identified several clinical key elements, relevant complementary-symptoms and predisposing factors, and organized them in a multi-level, hierarchical, circle diagram able to guide the clinician through the diagnostic work-up of PED. The most appropriate investigational method(s) should be chosen based on the diagnostic hypothesis: esophagogastroduodenoscopy has highest diagnostic yield for mucosal disorders, barium swallow has greater sensitivity in detecting achalasia and structural abnormalities, chest CT/MR inform on the mediastinum, manometry is most sensitive in detecting motility disorders, while pH-MII measures gastroesophageal reflux. Further studies are needed to define the epidemiology of PED, determine the prevalence of individual underlying etiologies, and assess the diagnostic value of investigational methods as to develop a reliable diagnostic algorithm.

SUBMITTER: Lanzoni G

PROVIDER: S-EPMC9263077 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The aim of this study was to develop a diagnostic strategy for esophageal squamous cell carcinoma (ESCC) that combines plasma metabolomics with machine learning algorithms. Plasma-based untargeted metabolomics analysis was performed with samples derived from 88 ESCC patients and 52 healthy controls. The dataset was split into a training set and a test set. After identification of differential metabolites in training set, single-metabolite-based receiver operating characteristic (ROC) curves and multiple-metabolite-based machine learning models were used to distinguish between ESCC patients and healthy controls. Kaplan-Meier survival analysis and Cox proportional hazards regression analysis were performed to investigate the prognostic significance of the plasma metabolites. Finally, twelve differential plasma metabolites (six up-regulated and six down-regulated) were annotated. The predictive performance of the six most prevalent diagnostic metabolites through the diagnostic models in the test set were as follows: arachidonic acid (accuracy: 0.887), sebacic acid (accuracy: 0.867), indoxyl sulfate (accuracy: 0.850), phosphatidylcholine (PC) (14:0/0:0) (accuracy: 0.825), deoxycholic acid (accuracy: 0.773), and trimethylamine N-oxide (accuracy: 0.653). The prediction accuracies of the machine learning models in the test set were partial least-square (accuracy: 0.947), random forest (accuracy: 0.947), gradient boosting machine (accuracy: 0.960), and support vector machine (accuracy: 0.980). Additionally, survival analysis demonstrated that acetoacetic acid was an unfavorable prognostic factor (hazard ratio (HR): 1.752), while PC (14:0/0:0) (HR: 0.577) was a favorable prognostic factor for ESCC. This study devised an innovative strategy for ESCC diagnosis by combining plasma metabolomics with machine learning algorithms and revealed its potential to become a novel screening test for ESCC. Highlights • Six most prevalent diagnostic plasma metabolites were identified in ESCC.• Plasma-metabolite-based machine learning models (PLS, RF, GBM, and SVM) for ESCC diagnosis.• Acetoacetic acid was an unfavorable prognostic factor, while PC (14:0/0:0) was a favorable prognostic factor for ESCC.

Dataset Information

Esophageal Dysphagia in Children: State of the Art and Proposal for a Symptom-Based Diagnostic Approach.

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