Early risk stratification for progression to death by neurological criteria following out-of-hospital cardiac arrest

Background: Some patients resuscitated from out-of-hospital cardiac arrest (OHCA) progress to death by neurological criteria (DNC). We hypothesized that initial brain imaging, electroencephalography (EEG), and arrest characteristics predict progression to DNC.Methods: We identified comatose OHCA patients from January 2010 to February 2020 treated at a single quaternary care facility in Western Pennsylvania. We abstracted demographics and arrest characteristics; Pittsburgh Cardiac Arrest Category, initial motor exam and pupillary light reflex; initial brain computed tomography (CT) grey-to-white ratio (GWR), sulcal or basal cistern effacement; initial EEG background and suppression ratio. We used two modeling approaches: fast and frugal tree (FFT) analysis to create an interpretable clinical risk stratification tool and ridge regression for comparison. We used bootstrapping to randomly partition cases into 80% training and 20% test sets and evaluated test set sensitivity and specificity.Results: We included 1,569 patients, of whom 147 (9%) had diagnosed DNC. Across bootstrap samples, >99% of FFTs included three predictors: sulcal effacement, and in cases without sulcal effacement, the combination of EEG background suppression and GWR < 1.23. This tree had mean sensitivity and specificity of 87% and 81%. Ridge regression with all available predictors had mean sensitivity 91% and mean specificity 83%. Subjects falsely predicted as likely to progress to DNC generally died of rearrest or withdrawal of life sustaining therapies due to poor neurological prognosis. Two of these cases awakened from coma during the index hospitalization.Conclusions: Sulcal effacement on presenting brain CT or EEG suppression with GWR < 1.23 predict progression to DNC after OHCA.

Automated summary

This study aimed to investigate the predictors of death by neurological criteria (DNC) in patients resuscitated from out-of-hospital cardiac arrest (OHCA). The study found that initial brain imaging and electroencephalography (EEG) results can predict the prognosis of OHCA patients. The predictors included sulcal effacement, EEG background, and GWR.