Introduction: Prognostication is a significant challenge early in the post-cardiac arrest period. Common prognostic factors for neurological survival are unreliable (high false positive rates) until 72 hours post-cardiac arrest. It is not known whether there are a combination of factors that can be utilized earlier in the post-cardiac arrest period to accurately predict patient outcome. Our objective was to predict neurological outcome utilizing a novel combination of patient factors early in the post-cardiac arrest period. Methods: We conducted a retrospective cohort study using data from our local cardiac arrest registry. We included adult patients who obtained a return of spontaneous circulation (ROSC) after out-of-hospital cardiac arrest (OHCA). We excluded patients who did not survive for at least 24 hours post-ROSC and those who had a do not resuscitate (DNR) order within 2 hours of ROSC. We performed an ordinal regression analysis using the proportional odds model to predict neurological outcome (modified rankin score (mRS)). We included a good neurological outcome (mRS 0-2), poor neurological outcome (mRS 3-5), and dead (mRS 6) as an ordinal outcome. We included a number of patient demographics, intra- and post-cardiac arrest factors as covariates in our model. The predictive performance of our model was analyzed using receiver operating characteristic (ROC) curves for discrimination and Brier statistic for calibration. Results: We included 3448 patients in our analysis. We found that an initial shockable rhythm (odds ratio (OR) 4.1; 95% confidence interval (CI) 3.6, 5.4), the absence of pupillary reflexes (OR 3.5; 95% CI 2.4,4.8) and maximum motor score on the Glasgow Coma Scale (GCS) (OR 1.5; 95% CI 1.4,1.6) had the greatest association with improved neurologic outcome. Longer duration of resuscitation was associate with worse outcomes (OR 0.84, 95% CI 0.82,0.87). The overall performance of our model was excellent with an area under the ROC curve of 0.89 and a Brier statistic of 0.13. Conclusion: Our model predicted good neurological outcome with a high rate of accuracy, however external validation of the model is required. This model may be useful in providing initial risk stratification of patients in clinical practice and future research on post-cardiac arrest care.