Amir Abbas Tahami Monfared,Yaakov Stern,Michael C Irizarry,Stephen Doogan,Quanwu Zhang,First published: 07 December 2020 https://doi.org/10.1002/alz.039448
Detection and diagnosis of Mild Cognitive Impairment due to Alzheimer’s Disease (MCI-AD) can be challenging due to the subtlety of changes in early stages of disease progression. As a result, opportunities to capture the patient burden in MCI-AD can be limited.
We used Natural Language Processing (NLP) to analyze challenges and issues that were the most frequently reported online by patients living with MCI-AD and caregivers. Patients’ self-reports and caregivers’ reports on patients were identified based on online narratives posted between January 1998 and December 2019 across 84 social media sources. The RLS Reveal NLP platform was used in combination with manual curation to codify verbatim symptoms and impairments against standardized medical taxonomies such as WHO-ICF and MedDRA, and further into the following categorizations: Social, Physical, Emotional, Cognitive, and Financial (SPEC-F).
63,933 narratives from 311 patients with MCI-AD and 1,454 caregivers were qualified into the sample for analysis. The most frequently reported issues varied between patients and caregivers. Cognitive issues (e.g. memory impairments and comprehension-related) were the most frequently reported by both groups, however, patients reported at a higher rate. This may suggest a deeper concern with losing cognitive ability felt by patients themselves at the disease stage of MCI. This is further supported by a higher reporting rate of anxiety and depressive disorders among patients relative to caregivers. In contrast, certain role activity, physical and social issues (e.g. driving, fatigue, walking and mobility, relationships with spouse or partner) are reported by caregivers at a higher rate than patients, suggesting that these issues may have a greater impact on the daily lives of caregivers or may be more readily recognizable than cognitive issues.
The SPEC-F framework helps identify, understand, and prioritize the most pressing concerns at the symptom and functional level by patients and caregivers. It can uncover areas of disease burden in MCI-AD that are less frequently associated with MCI, particularly in the non-cognitive domains, that may not be sufficiently captured by early diagnostic processes in the clinical setting.
