Prediction of Delirium Risk in Mild Cognitive Impairment Using Time-Series data, Machine Learning and Comorbidity Patterns -- A Retrospective Study
Journal:
arXiv
Published Date:
May 5, 2025
Abstract
Delirium represents a significant clinical concern characterized by high
morbidity and mortality rates, particularly in patients with mild cognitive
impairment (MCI). This study investigates the associated risk factors for
delirium by analyzing the comorbidity patterns relevant to MCI and developing a
longitudinal predictive model leveraging machine learning methodologies. A
retrospective analysis utilizing the MIMIC-IV v2.2 database was performed to
evaluate comorbid conditions, survival probabilities, and predictive modeling
outcomes. The examination of comorbidity patterns identified distinct risk
profiles for the MCI population. Kaplan-Meier survival analysis demonstrated
that individuals with MCI exhibit markedly reduced survival probabilities when
developing delirium compared to their non-MCI counterparts, underscoring the
heightened vulnerability within this cohort. For predictive modeling, a Long
Short-Term Memory (LSTM) ML network was implemented utilizing time-series data,
demographic variables, Charlson Comorbidity Index (CCI) scores, and an array of
comorbid conditions. The model demonstrated robust predictive capabilities with
an AUROC of 0.93 and an AUPRC of 0.92. This study underscores the critical role
of comorbidities in evaluating delirium risk and highlights the efficacy of
time-series predictive modeling in pinpointing patients at elevated risk for
delirium development.
