The cardiac arrest survival score: A predictive algorithm for in-hospital mortality after out-of-hospital cardiac arrest. (November 2019) - Balan, Prakash; Hsi, Brian; Thangam, Manoj; Zhao, Yelin; Monlezun, Dominique; Arain, Salman; Charitakis, Konstantinos; Dhoble, Abhijeet; Johnson, Nils; Anderson, H. Vernon; Persse, David; Warner, Mark; et al.
Abstract: Background: Out-of-hospital cardiac arrest (OHCA) is associated with high mortality. Current methods for predicting mortality post-arrest require data unavailable at the time of initial medical contact. We created and validated a risk prediction model for patients experiencing OHCA who achieved return of spontaneous circulation (ROSC) which relies only on objective information routinely obtained at first medical contact. Methods: We performed a retrospective evaluation of 14, 892 OHCA patients in a large metropolitan cardiac arrest registry, of which 3952 patients had usable data. This population was divided into a derivation cohort (n = 2, 635) and a verification cohort (n = 1, 317) in a 2:1 ratio. Backward stepwise logistic regression was used to identify baseline factors independently associated with death after sustained ROSC in the derivation cohort. The cardiac arrest survival score (CASS) was created from the model and its association with in-hospital mortality was examined in both the derivation and verification cohorts. Results: Baseline characteristics of the derivation and verification cohorts were not different. The final CASS model included age >75 years (odds ratio [OR] = 1.61, confidence interval [CI][1.30–1.99], p < 0.001), unwitnessed arrest (OR = 1.95, CI[1.58–2.40], p < 0.001), home arrest (OR = 1.28, CI[1.07–1.53], p = 0.008), absence of bystander CPR (OR = 1.35, CI[1.12–1.64], p = 0.003), and non-shockable initial rhythm (OR = 3.81, CI[3.19–4.56], p < 0.001). The area under the curve for the model derivation and model verification cohorts were 0.7172 and 0.7081, respectively. Conclusion: CASS accurately predicts mortality in OHCA patients. The model uses only binary, objective clinical data routinely obtained at first medical contact. Early risk stratification may allow identification of more patients in whom timely and aggressive invasive management may improve outcomes.