scholarly journals Patient-Reported Outcomes and Blood-Based Parameters Identify Response to Treatment in Eosinophilic Esophagitis

2020 ◽  
Author(s):  
Christine Lingblom ◽  
Sofie Albinsson ◽  
Leif Johansson ◽  
Helen Larsson ◽  
Christine Wennerås
Keyword(s):  
Eosinophilic Esophagitis ◽  
Patient Reported Outcomes ◽  
Response To Treatment ◽  
Patient Reported

Qualitative Assessment of Patient-reported Outcomes in Adults With Eosinophilic Esophagitis

2011 ◽  
Vol 45 (9) ◽  
pp. 769-774 ◽  
Author(s):  
Tiffany H. Taft ◽  
Emily Kern ◽  
Laurie Keefer ◽  
David Burstein ◽  
Ikuo Hirano
Keyword(s):  
Eosinophilic Esophagitis ◽  
Patient Reported Outcomes ◽  
Qualitative Assessment ◽  
Patient Reported

scholarly journals Patient-Reported Outcomes in Eosinophilic Esophagitis and Achalasia

2016 ◽  
Vol 14 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Alain Schoepfer ◽  
Alex Straumann ◽  
Ekaterina Safroneeva
Keyword(s):  
Eosinophilic Esophagitis ◽  
Patient Reported Outcomes ◽  
Patient Reported

scholarly journals Systematic Review of Outcome Measures Used in Observational Studies of Adults with Eosinophilic Esophagitis

2021 ◽  
pp. 1-25
Author(s):  
Alain M. Schoepfer ◽  
Camilla Schürmann ◽  
Sven Trelle ◽  
Marcel Zwahlen ◽  
Christopher Ma ◽  
...  
Keyword(s):  
Systematic Review ◽  
Outcome Measures ◽  
Eosinophilic Esophagitis ◽  
Observational Studies ◽  
Patient Reported Outcomes ◽  
Case Series ◽  
Open Label ◽  
Topical Corticosteroids ◽  
Therapeutic Trials ◽  
Patient Reported

<b><i>Background:</i></b> Over the last 20 years, diverse outcome measures have been used to evaluate the effectiveness of therapies for eosinophilic esophagitis (EoE). This systematic review aims to identify the readouts used in observational studies of topical corticosteroids, diet, and dilation in adult EoE patients. <b><i>Methods:</i></b> We searched MEDLINE and Embase for prospective and retrospective studies (cohorts/case series, randomized open-label, and case-control) evaluating the use of diets, dilation, and topical corticosteroids in adults with EoE. Two authors independently assessed the articles and extracted information about histologic, endoscopic, and patient-reported outcomes and tools used to assess treatment effects. <b><i>Results:</i></b> We included 69 studies that met inclusion criteria. EoE-associated endoscopic findings (assessed either as absence/presence or using Endoscopic Reference Score) were evaluated in 24/35, 11/17, and 9/17 studies of topical corticosteroids, diet, and dilation, respectively. Esophageal eosinophil density was recorded in 32/35, 17/17, and 11/17 studies of topical corticosteroids, diet, and dilation, respectively. Patient-reported outcomes were not uniformly used (only in 14, 8, and 3 studies of topical corticosteroids, diet, and dilation, respectively), and most tools were not validated for use in adults with EoE. <b><i>Conclusions:</i></b> Despite the lack of an agreed set of core outcomes that should be recorded and reported in studies in adult EoE patients, endoscopic EoE-associated findings and esophageal eosinophil density are commonly used to assess disease activity in observational studies. Standardization of outcomes and data supporting the use of outcomes are needed to facilitate interpretation of evidence, its synthesis, and comparisons of interventions in meta-analyses of therapeutic trials in adults with EoE.

scholarly journals Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment

Medicina ◽  
2020 ◽  
Vol 56 (9) ◽  
pp. 455
Author(s):  
Hema Sekhar Reddy Rajula ◽  
Giuseppe Verlato ◽  
Mirko Manchia ◽  
Nadia Antonucci ◽  
Vassilios Fanos
Keyword(s):  
Machine Learning ◽  
Drug Development ◽  
Statistical Methods ◽  
Patient Reported Outcomes ◽  
A Priori ◽  
Response To Treatment ◽  
Patient Reported ◽  
Need To Evaluate ◽  
Diagnosis And Classification ◽  
Treatment Integration

Futurists have anticipated that novel autonomous technologies, embedded with machine learning (ML), will substantially influence healthcare. ML is focused on making predictions as accurate as possible, while traditional statistical models are aimed at inferring relationships between variables. The benefits of ML comprise flexibility and scalability compared with conventional statistical approaches, which makes it deployable for several tasks, such as diagnosis and classification, and survival predictions. However, much of ML-based analysis remains scattered, lacking a cohesive structure. There is a need to evaluate and compare the performance of well-developed conventional statistical methods and ML on patient outcomes, such as survival, response to treatment, and patient-reported outcomes (PROs). In this article, we compare the usefulness and limitations of traditional statistical methods and ML, when applied to the medical field. Traditional statistical methods seem to be more useful when the number of cases largely exceeds the number of variables under study and a priori knowledge on the topic under study is substantial such as in public health. ML could be more suited in highly innovative fields with a huge bulk of data, such as omics, radiodiagnostics, drug development, and personalized treatment. Integration of the two approaches should be preferred over a unidirectional choice of either approach.

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