Characterizing Physiologic Swallowing Impairment Profiles: A Large-Scale Exploratory Study of Head and Neck Cancer, Stroke, Chronic Obstructive Pulmonary Disease, Dementia, and Parkinson's Disease.

Purpose: The purpose of the present study was to use a large swallowing database to explore and compare the swallow-physiology impairment profiles of five dysphagia-associated diagnoses: chronic obstructive pulmonary disease (COPD), dementia, head and neck cancer (HNC), Parkinson's disease (PD), and stroke.

Method: A total of 8,190 patients across five diagnoses were extracted from a de-identified swallowing database, that is, the Modified Barium Swallow Impairment Profile Swallowing Data Registry, for the present exploratory cross-sectional analysis. To identify the impairment profiles of the five diagnoses, we fit 18 partial proportional odds models, one for each of the 17 Modified Barium Swallow Impairment Profile components and the Penetration-Aspiration Scale, with impairment score as the dependent variable and diagnoses, age, sex, and race as the independent variables with interactions between age and diagnoses and between PD and dementia (in effect creating a PD with dementia [PDwDem] group). For components with > 5% missingness, we applied inverse probability weighting to correct for bias.

Results: PD and COPD did not significantly differ on 13 of the 18 outcome variables (all s > .02). Dementia, stroke, and PDwDem all showed worse impairments than COPD or PD on five of six oral components (all s < .007). HNC had worse impairment than all diagnoses except PDwDem for nine of 10 pharyngeal components (all s < .006). Stroke and HNC had worse penetration/aspiration than all other diagnoses (all s < .003).

Conclusions: The present results show that there are both common and differing impairment profiles among these five diagnoses. These commonalities and differences in profiles provide a basis for the generation of hypotheses about the nature and severity of dysphagia in these populations. These results are also likely highly generalizable given the size and representativeness of the data set.

Supplemental Material: https://doi.org/10.23641/asha.27478245.