27-Machine Learning Prediction of Intensive Care Unit Delirium
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Neuroscience, Quality and Patient Safety, 2021
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The Society of Critical Care Medicine's Critical Care Congress features internationally renowned faculty and content sessions highlighting the most up-to-date, evidence-based developments in critical care medicine. This is a presentation from the 2021 Critical Care Congress held virtually from January 31-February 12, 2021.

Kirby D. Gong, BS


Introduction/Hypothesis: ICU delirium is frequent, associated with unfavorable outcomes, increased health expenditures, and may be largely preventable. Accurate delirium prediction could allow early intervention strategies in high risk patients. The aim of this study was to leverage machine (ML) learning applied to early physiological and clinical data to predict delirium onset at any time during an ICU stay.

Methods: Data were obtained from the multicenter eICU dataset. Delirium labels were determined by clinical documentation of either the Confusion Assessment Method in the ICU or Intensive Care Delirium Screening Checklist scoring systems at any time during the ICU stay. Predictive features were extracted data recorded in the first 24h after ICU admission. A statistically pruned feature space of 116 derived variables was used to train three different ML algorithms (logistic regression [LR], random forest [RF], and gradient boosting [CatBoost]). Model performance was evaluated by area under the receiver operating characteristic curve (AUROC) discrimination analysis and compared with the PRE-DELIRIC score which has been previously validated for prediction of delirium in the ICU.

Results: A total of 24,695 patient stays associated with delirium were identified. The AUROC of logistic regression, random forest, and CatBoost were 0.780±0.005, 0.797±0.005, and 0.792±0.005, respectively. For comparison, AUROC of the PRE-DELIRIC model was 0.731±0.006. The ML models calibrated well, with Brier scores of 0.125±0.001, 0.127±0.003, and 0.152±0.002. The top ten predictive features according to the LR model included mean verbal GCS score, mean eyes GCS score, APACHE IV score, age, coma, and minimum mean corpuscular volume. Physiological time series data recorded in the first 24h did not add to prediction accuracy.

Conclusions: Machine learning applied to features from the first 24h after admission predicted ICU delirium onset with higher accuracy than the reference PRE-DELIRIC score. Results suggest that high-resolution data contain predictive information on delirium risk in the ICU which is overlooked in current prediction systems. Results warrant prospective validation and additional studies integrating other data modalities such as brain-specific molecular biomarkers and neuroimaging.


Meta Tag
Content Type Presentation
Knowledge Area Neuroscience
Knowledge Area Quality and Patient Safety
Knowledge Level Advanced
Membership Level Select
Tag Scoring Systems
Tag Delirium
Year 2021
Kirby Gong
machine learning
predicting delirium
intensive care units


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