Cardiac arrest & resuscitation
Outcome prediction of cardiac arrest with automatically computed gray-white matter ratio on computed tomography images
Authors' conclusion
Automated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.
Study context
- Population
- Patients with out-of-hospital cardiac arrest (OHCA) who underwent brain computed tomography (CT) within 12 h of ROSCderivation 265, validation 178
- Participants
- 443 patients
- Comparison
- Automated vs manual GWR measurement on brain CT
- Outcome
- Favorable neurological outcome (cerebral performance category 1 or 2)
- Follow-up
- Hospital discharge
Methodological detailsderivation and validation sets
- Derivation and validation sets
Performance
As reported in the abstract
Prediction of favorable neurological outcome by GWR
| Test or model | AUC |
|---|---|
| Automated method | 0.79 |
| Manual method | 0.70 |
AUC
Automated method0.79
Manual method0.70
Further findings
Sensitivity, specificity, PPV and NPV (derivation-set cutoff)
- Superior performance of the automated method
ROI Hounsfield unit values
- Strong correlation between automated and manual methods
GWR in logistic regression
- GWR an independent predictor of outcomes
Prediction models with clinical and resuscitation variables
- Adding GWR significantly enhanced performance vs models without GWR
Original publication
Outcome prediction of cardiac arrest with automatically computed gray-white matter ratio on computed tomography images. Crit Care. Published 9 April 2024. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →