ICU Delirium Prediction Models: A Systematic Review

TL;DR

This systematic review analyzes recent ICU delirium prediction models, highlighting their performance, limitations in clinical applicability, and the need for dynamic, real-time prediction tools to improve patient care.

Contribution

The review provides a comprehensive summary of recent prediction models, emphasizing their static nature and the necessity for models that adapt over time for better clinical utility.

Findings

Model AUROC ranged from 0.68 to 0.94

Most models used data from a single time point

Limited models provided actionable predictions

Abstract

Purpose: Summarize ICU delirium prediction models published within the past five years. Methods: Electronic searches were conducted in April 2019 using PubMed, Embase, Cochrane Central, Web of Science, and CINAHL to identify peer reviewed studies published in English during the past five years that specifically addressed the development, validation, or recalibration of delirium prediction models in adult ICU populations. Data were extracted using CHARMS checklist elements for systematic reviews of prediction studies, including the following characteristics: study design, participant descriptions and recruitment methods, predicted outcomes, a priori candidate predictors, sample size, model development, model performance, study results, interpretation of those results, and whether the study included missing data. Results: Twenty studies featuring 26 distinct prediction models were included. Model performance varied greatly, as assessed by AUROC (0.68-0.94), specificity (56.5%-92.5%), and sensitivity (59%-90.9%). Most models used data collected from a single time point or window to predict the occurrence of delirium at any point during hospital or ICU admission, and lacked mechanisms for providing pragmatic, actionable predictions to clinicians. Conclusions: Although most ICU delirium prediction models have relatively good performance, they have limited applicability to clinical practice. Most models were static, making predictions based on data collected at a single time-point, failing to account for fluctuating conditions during ICU admission. Further research is needed to create clinically relevant dynamic delirium prediction models that can adapt to changes in individual patient physiology over time and deliver actionable predictions to clinicians.