Symptom clustering of old adult dysphagic patient phenotypes by unsupervised machine learning using multidimensional characteristics: a cross-sectional study

Dysphagia severely affects the old adult population. Its signs and symptoms are variable, which poses challenges to diagnosis and treatment. Existing assessment tools fall short in considering the coordination of relevant muscle groups in the pathology of dysphagia and fail to characterize dysphagia in a comprehensive manner. The aim of this study was to identify different phenotypes of dysphagia based on multidimensional patient characteristics. This work was a cross-sectional study. Senior dysphagia patients (> 65 years old) diagnosed by Videofluoroscopic Swallow Study at seven tertiary hospitals in Suzhou were enrolled from June 2022 to October 2024. They were grouped into different phenotypes by the k-means clustering algorithm based on their respiratory, swallowing, and articulatory functions. The characteristics of demographics, medical history, and two dysphagia outcomes were compared across the different clustering results. The 1,301 patients were divided into four distinct phenotypes: minimal impairment (MI, n = 462), respiratory-dominant impairment (RD, n = 305), articulatory-dominant impairment (AD, n = 301), and multimodal impairment (MT, n = 233). Each phenotype exhibited a unique combination of respiratory, swallowing, and articulatory impairments, and the demographics (e.g., age, BMI, income, etc.) and medical history (e.g., head and neck tumors, masticatory disorders) differed significantly between phenotypes (P < 0.05). The eating ability and the incidence of swallowing-related complications both differed significantly (P < 0.001) between phenotypes. The degree of dysphagia fell in the order MT > RD > AD > MI and was consistent with the Penetration-Aspiration Scale score. This study identified different phenotypes of dysphagia based on multidimensional patient characteristics with unique clinical features and outcomes. The findings support the development of targeted therapeutic strategies to improve patient prognosis.