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

TL;DR

This study develops a machine learning model using time-series data and comorbidity patterns to predict delirium risk in patients with mild cognitive impairment, demonstrating high predictive accuracy and emphasizing the importance of comorbidities.

Contribution

It introduces a novel LSTM-based predictive model leveraging longitudinal data and comorbidity analysis to assess delirium risk in MCI patients.

Findings

LSTM model achieved AUROC of 0.93 and AUPRC of 0.92.

Distinct comorbidity risk profiles identified for MCI patients.

Patients with MCI show significantly reduced survival when developing delirium.

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.