Early prediction model of brain death in out-of-hospital cardiac arrest patients: a single-center retrospective and internal validation analysis
Dublin Core
Title
Early prediction model of brain death in out-of-hospital cardiac arrest patients: a single-center retrospective and internal validation analysis
Subject
Brain death, Out-of-hospital cardiac arrest, Cardiopulmonary resuscitation, Organ donation, Prediction
model
model
Description
Background: A shortage of donor organs amid 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 patients with BD may facilitate the process of organ procurement.
Therefore, we developed a model for the early prediction of BD in patients who survived the initial phase of out-ofhospital cardiac arrest (OHCA).
Methods: We retrospectively analyzed data of patients aged<80 years who experienced OHCA with a return of
spontaneous circulation (ROSC) and were admitted to our hospital between 2006 and 2018. We categorized patients
into either a non-BD or BD group. Demographic and laboratory data on ED admission were used for stepwise logistic
regression analysis. Prediction scores of BD after OHCA were based on β-coefcients of prognostic factors identifed in
the multivariable logistic model.
Results: Overall, 419 OHCA patients with ROSC were admitted to our hospital during the study period. Seventyseven patients showed BD (18.3%). Age and etiology of OHCA were signifcantly diferent between the groups. Logistic regression analysis confrmed that age, low-fow time, pH, and etiology were independent predictors of BD. The
area under the receiver operating characteristic curve for this model was 0.831 (95% confdence interval, 0.786–0.876).
Conclusions: We developed and internally validated a new prediction model for BD after OHCA, which could aid in
the early identifcation of potential organ donors for early donor organ procurement.
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 patients with BD may facilitate the process of organ procurement.
Therefore, we developed a model for the early prediction of BD in patients who survived the initial phase of out-ofhospital cardiac arrest (OHCA).
Methods: We retrospectively analyzed data of patients aged<80 years who experienced OHCA with a return of
spontaneous circulation (ROSC) and were admitted to our hospital between 2006 and 2018. We categorized patients
into either a non-BD or BD group. Demographic and laboratory data on ED admission were used for stepwise logistic
regression analysis. Prediction scores of BD after OHCA were based on β-coefcients of prognostic factors identifed in
the multivariable logistic model.
Results: Overall, 419 OHCA patients with ROSC were admitted to our hospital during the study period. Seventyseven patients showed BD (18.3%). Age and etiology of OHCA were signifcantly diferent between the groups. Logistic regression analysis confrmed that age, low-fow time, pH, and etiology were independent predictors of BD. The
area under the receiver operating characteristic curve for this model was 0.831 (95% confdence interval, 0.786–0.876).
Conclusions: We developed and internally validated a new prediction model for BD after OHCA, which could aid in
the early identifcation of potential organ donors for early donor organ procurement.
Creator
Yuki Itagaki , Mineji Hayakawa, Kunihiko Maekawa, Akira Kodate, Koyo Moriki, Yuki Takahashi and Hisako Sageshima
Publisher
BMC Emergency Medicine
Date
(2022) 22:177
Contributor
Fajar Bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Yuki Itagaki , Mineji Hayakawa, Kunihiko Maekawa, Akira Kodate, Koyo Moriki, Yuki Takahashi and Hisako Sageshima, “Early prediction model of brain death in out-of-hospital cardiac arrest patients: a single-center retrospective and internal validation analysis,” Repository Horizon University Indonesia, accessed November 21, 2024, https://repository.horizon.ac.id/items/show/4245.