Abstract
Background A shortage of donor organs amid a high demand for transplantable organs is a worldwide problem, and an increase in organ donation would be welcomed by the global healthcare system. Patients with brain death (BD) are potential organ donors, and early prediction of BD patients may facilitate the process of organ procurement. Therefore, we developed a model for early prediction of BD in patients who survived the initial phase of out-of-hospital cardiac arrest (OHCA). Methods We retrospectively analysed data of patients aged, who were aged < 80 years, experienced OHCA with return of spontaneous circulation (ROSC), and were admitted to our hospital between 2006 and 2018. We categorised the patients into either a non-BD or BD group. Demographic and laboratory data on emergency department admission were used for stepwise logistic regression. Prediction scores of BD after OHCA were based on β-coefficients of prognostic factors identified in the multivariable logistic model. Results Overall, 419 OHCA patients with ROSC were admitted to our hospital during the study period. Seventy-seven patients showed BD (18.3%). Age and OHCA aetiology were significantly different between the groups. Logistic regression analysis confirmed that age, low-flow time, pH, and aetiology were independent predictors of BD. The area under the receiver operating characteristic curve for this model was 0.831 (95% CI, 0.786–0.876). Conclusion We developed and internally validated a new prediction model for BD after OHCA, which could aid in early identification of potential organ donors for early donor organ procurement.
Non-survivors of out-of-hospital cardiac arrest should always be␣considered potential non-heart-beating organ donors