Profiling Delirium Progression in Elderly Patients via Continuous-Time Markov Multi-State Transition Models

• Main Authors: Honoria Ocagli, Danila Azzolina, Rozita Soltanmohammadi, Roqaye Aliyari, Daniele Bottigliengo, Aslihan Senturk Acar, Lucia Stivanello, Mario Degan, Ileana Baldi, Giulia Lorenzoni, Dario Gregori
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Journal of Personalized Medicine
• Subjects: Cox model; continuous-time Markov multi-state transition model; 4AT scale; delirium
• Online Access: https://www.mdpi.com/2075-4426/11/6/445

Poor recognition of delirium among hospitalized elderlies is a typical challenge for health care professionals. Considering methodological insufficiency for assessing time-varying diseases, a continuous-time Markov multi-state transition model (CTMMTM) was used to investigate delirium evolution in elderly patients. This is a longitudinal observational study performed in September 2016 in an Italian hospital. Change of delirium states was modeled according to the 4AT score. A Cox model (CM) and a CTMMTM were used for identifying factors affecting delirium onset both with a two-state and three-state model. In this study, 78 patients were enrolled and evaluated for 5 days. Both the CM and the CTMMTM show that urine catheter (UC), aging, drugs, and invasive devices (ID) are risk factors for delirium onset. The CTMMTM model shows that transition from no-delirium/cognitive impairment to delirium was associated with aging (HR = 1.14; 95%CI, 1.05, 1.23) and neuroleptics (HR = 4.3; 1.57, 11.77), dopaminergic drugs (HR = 3.89; 1.2, 12.6), UC (HR = 2.92; 1.09, 7.79) and ID (HR = 1.67; 103, 2.71). These results are confirmed by the multivariable model. Aging, ID, antibiotics, drugs affecting the central nervous system, and absence of moving ability are identified as the significant predictors of delirium. Additionally, it seems that modeling with CTMMTM may show associations that are not directly detectable with the traditional CM.