Cardiac arrest & resuscitation
Developing an Electroencephalogram-based Model to Predict Awakening after Cardiac Arrest Using Partial Processing with the BIS Engine
Authors' conclusion
In patients comatose after cardiac arrest, four EEG features calculated internally by the BIS Engine were repurposed by a compact neural network to achieve a prognostic accuracy superior to the current clinical qualitative accepted standard, with high sensitivity for recovery.
Study context
- Population
- Comatose patients admitted after cardiac arrest to a single US academic medical center, with 48 h continuous EEGderivation 181, validation 134
- Participants
- 315 patients
- Exposure
- BIS model vs Modified Westhall
- Outcome
- Prediction of recovery of consciousness to the point of command following
Methodological detailssingle-centre, neural network prognostic model, derivation and validation cohorts
- Single-centre
- Neural network prognostic model
- Derivation and validation cohorts
Main finding
As reported in the abstract
Prediction of recovery of consciousness (validation cohort)
| Group | AUC | Accuracy | Sensitivity | Specificity | PPV | NPV |
|---|---|---|---|---|---|---|
| Model | 0.86 | 0.87 | 0.83 | 0.88 | 0.71 | 0.94 |
Model
AUC0.86
Accuracy0.87
Sensitivity0.83
Specificity0.88
PPV0.71
NPV0.94
Observational data: associations, not effects.
Further findings
Gamma band power
- Not previously reported as a correlate of recovery potential after cardiac arrest
Original publication
Developing an Electroencephalogram-based Model to Predict Awakening after Cardiac Arrest Using Partial Processing with the BIS Engine. Anesthesiology. Published 9 January 2025. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →