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Abstract Introduction: We tested the ability of our natural language processing (NLP) algorithm to identify delirium episodes in a large-scale study using real-world clinical notes. Methods: We used the Rochester Epidemiology Project to identify persons ≥ 65 years who were hospitalized between 2011 and 2017. We identified all persons with an International Classification of Diseases code for delirium within ±14 days of a hospitalization. We independently applied our NLP algorithm to all clinical notes for this same population. We calculated rates using number of delirium episodes as the numerator and number of hospitalizations as the denominator. Rates were estimated overall, by demographic characteristics, and by year of episode, and differences were tested using Poisson regression. Results: In total, 14,255 persons had 37,554 hospitalizations between 2011 and 2017. The code-based delirium rate was 3.02 per 100 hospitalizations (95% CI: 2.85, 3.20). The NLP-based rate was 7.36 per 100 (95% CI: 7.09, 7.64). Rates increased with age (both p < 0.0001). Code-based rates were higher in men compared to women (p = 0.03), but NLP-based rates were similar by sex (p = 0.89). Code-based rates were similar by race and ethnicity, but NLP-based rates were higher in the White population compared to the Black and Asian populations (p = 0.001). Both types of rates increased significantly over time (both p values < 0.001). Conclusions: The NLP algorithm identified more delirium episodes compared to the ICD code method. However, NLP may still underestimate delirium cases because of limitations in real-world clinical notes, including incomplete documentation, practice changes over time, and missing clinical notes in some time periods.

Objectives To study the association between multi-morbidity percentiles, which is a measure of clinical aging, and interleukin (IL)-6, IL-10, and tumor necrosis factor (TNF)-α. Methods Participants 50 to 95 years of age from the Mayo Clinic Study of Aging were assigned age- and sex-specific multi-morbidity percentiles using look-up tables that were reported previously (n = 1646). Percentiles were divided into quintiles for analysis. Plasma IL-6, IL-10, and TNF-α levels were measured in 1595 participants. Median inflammatory marker levels were compared across multi-morbidity quintiles using nonparametric tests. Results People with higher multi-morbidity percentiles had significantly higher IL-6 and TNF-α levels compared with those with lower multi-morbidity percentiles. Tests for trend across five multi-morbidity quintiles were significant among women for IL-6 and among participants 70 years of age or older for IL-6 and TNF-α. IL-10 was not associated with multi-morbidity percentiles. Conclusions Multi-morbidity percentiles may be a useful clinical index of biological age for future studies, particularly in women and people 70 years of age and older.