Prediction of Delirium Risk in Mild Cognitive Impairment Using Time-Series Data, Machine Learning and Comorbidity Patterns - A Retrospective Study.

Abstract

Delirium represents a significant clinical concern characterised by high morbidity and mortality rates, particularly in patients with mild cognitive impairment (MCI). This study investigates the associated risk factors for delirium by analysing the comorbidity patterns relevant to MCI and developing a longitudinal predictive model leveraging machine learning (ML) methodologies. A retrospective analysis utilising the MIMIC-IV v2.2 database was performed to evaluate comorbid conditions, survival probabilities, and predictive modelling 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 modelling, a Long Short-Term Memory (LSTM) model was implemented utilising time-series data, demographic variables, Charlson Comorbidity Index (CCI) scores, and an array of comorbid conditions. The model demonstrated robust predictive capabilities with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.92 and an Area Under the Precision-Recall Curve (AUPRC) of 0.91. 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.