  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.
