A Visuoperceptual Measure for Videofluoroscopic Swallow Studies (VMV): A Pilot Study of Validity and Reliability in Adults with Dysphagia

Validity and reliability of the Group for Learning Useful and Performant Swallowing (GLUPS) tool

INTRODUCTION

To validate the Group for Learning Useful and Performant Swallowing (GLUPS), a clinical tool dedicated to videofluoroscopy swallowing study (VFSS).

METHODS

Forty-five individuals were recruited from January 2022 to March 2023 from the Department of Otolaryngology Head and Neck Surgery of University Hospital Saint-Pierre (Brussels, Belgium). Subjects underwent VFSS, which was rated with GLUPS tool by two blinded otolaryngologists and one speech-therapist. VFSS were rated twice with GLUPS within a 7-day period to assess test-retest reliability.

RESULTS

Twenty-four patients and twenty-one controls completed the evaluations. The internal consistency (α = 0.745) and the test-retest reliability (rs = 0.941; p = 0.001) were adequate. GLUPS reported a high external validity regarding the significant correlation with the Penetration-Aspiration Scale (rs = 0.551; p = 0.001). Internal validity was adequate, because GLUPS score was significant higher in patients compared to controls (6.21 ± 4.42 versus 2.09 ± 2.00; p = 0.001). Interrater reliability did not report significant differences in the GLUPS sub- and total score among the independent judges. The mean GLUPS score of individuals without any evidence of VFSS abnormalities was 2.09/23 (95% CI  1.23-2.95), which supported that a GLUPS score ≥ 3.0 is suggestive of pathological VFSS.

CONCLUSIONS

GLUPS is a clinical instrument documenting the abnormal findings of oral and pharyngeal phases at the VFSS. GLUPS demonstrated high reliability and excellent criterion-based validity. GLUPS may be used in clinical practice for the swallowing evaluation at the VFSS.

Measuring what matters in healthcare: a practical guide to psychometric principles and instrument development

The provision of quality healthcare relies on scales and measures with robust evidence of their psychometric properties. Using measurement instruments with poor reliability, validity, or feasibility, or those that are not appropriate for the target diagnostic group or construct/dimension under consideration, may be unfavorable for patients, unproductive, and hinder empirical advancement. Resources from the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) group can assist in identifying and developing psychometrically sound measures. The COSMIN initiative is the only international, research-based practice taxonomy and methodological guidelines for measurement in healthcare. This manuscript aims to provide an accessible introduction to theories, principles and practices of psychometrics, instrument properties, and scale development, with applied examples from the COSMIN recommendations. It describes why measurement in healthcare is critical to good practice, explains the concepts of the latent variable and hypothetical construct and their importance in healthcare assessments, explores issues of flawed measurement and briefly explains key theories relevant to psychometrics. The paper also outlines a ten-step process to develop and validate a new measurement instrument, with examples drawn from a recently developed visuoperceptual measure for analysis of disordered swallowing to demonstrate key concepts and provides a guide for understanding properties of and terminology related to measurement instruments. This manuscript serves as a resource for healthcare clinicians, educators, and researchers who seek to develop and validate new measurement instruments or improve the properties of existing ones. It highlights the importance of using psychometrically sound measurement instruments to ensure high-quality healthcare assessments.

Inference of core needle biopsy whole slide images requiring definitive therapy for prostate cancer

BACKGROUND

Prostate cancer is often a slowly progressive indolent disease. Unnecessary treatments from overdiagnosis are a significant concern, particularly low-grade disease. Active surveillance has being considered as a risk management strategy to avoid potential side effects by unnecessary radical treatment. In 2016, American Society of Clinical Oncology (ASCO) endorsed the Cancer Care Ontario (CCO) Clinical Practice Guideline on active surveillance for the management of localized prostate cancer.

METHODS

Based on this guideline, we developed a deep learning model to classify prostate adenocarcinoma into indolent (applicable for active surveillance) and aggressive (necessary for definitive therapy) on core needle biopsy whole slide images (WSIs). In this study, we trained deep learning models using a combination of transfer, weakly supervised, and fully supervised learning approaches using a dataset of core needle biopsy WSIs (n=1300). In addition, we performed an inter-rater reliability evaluation on the WSI classification.

RESULTS

We evaluated the models on a test set (n=645), achieving ROC-AUCs of 0.846 for indolent and 0.980 for aggressive. The inter-rater reliability evaluation showed s-scores in the range of 0.10 to 0.95, with the lowest being on the WSIs with both indolent and aggressive classification by the model, and the highest on benign WSIs.

CONCLUSION

The results demonstrate the promising potential of deployment in a practical prostate adenocarcinoma histopathological diagnostic workflow system.