The Patients' Voice in Clostridioides difficile Infection: Large Language Model-Assisted Thematic Analysis of Patient Testimonials.

Abstract

Background: Clostridioides difficile infection (CDI) imposes a burden that extends well beyond the gastrointestinal tract, yet existing outcome measures only partially capture the patient experience. We used frontier large language models (LLMs) on patient and caregiver narratives at scale to describe how burden shifts with disease course.

Methods: We analyzed 189 testimonials from the Peggy Lillis Foundation corpus, sorted into 4 cohorts with recurrence (r) and fulminant (f) severity as axes (rfCDI, fCDI, rCDI, non-rfCDI). Two independent LLMs coded 8 thematic domains, 4 fulminant flags, 13 emerging semantic fields, the dominant dimension, and narrative arcs. Two physicians independently coded a subset for inter-rater reliability (PABAK, Gwet's AC1).

Results: Treatment trajectory was the dominant theme in recurrent disease, whereas death and near-death dominated nonrecurrent fulminant narratives. Psychological burden was near-universal in fulminant disease (98.0% in rfCDI, 97.2% in fCDI). Caregiver and bereavement content concentrated in fCDI (66.7%). Diagnostic failure was frequent across recurrent cohorts (47.6-56.1%). Mentions of bacteriotherapy (fecal microbiota transplantation) concentrated in recurrent cohorts (60.2% rfCDI versus 5.6% fCDI). Financial, mental-health, and caregiver burdens were prominent and remain largely unaddressed by guidelines. Human-human reliability was substantial (PABAK 0.79 for semantic fields, 0.76 for domains); arc coding was least reliable.

Conclusions: Patient narratives reveal a course-dependent, multidimensional burden in CDI. Concrete gaps exist between what patients prioritize, what guidelines recommend, and what therapy access provides. Frontier-LLM coding, validated against clinicians, offers a reproducible route to translate these priorities into research, care, and policy.