Predicting time to death in controlled donation after circulatory death (cDCD) donors following withdrawal of life‐sustaining treatment (WLST) is important but poses a major challenge. The aim of this study is to determine factors predicting time to circulatory death within 60 minutes after WSLT and validate previously developed prediction models. In a single‐center retrospective study, we used the data of 92 potential cDCD donors. Multivariable regression analysis demonstrated that absent cough‐, corneal reflex, lower morphine dosage, and midazolam use were significantly associated with death within 60 minutes (area under the curve [AUC] 0.89; 95% confidenence interval [CI] 0.87‐0.91). External validation of the logistic regression models of de Groot et al (AUC 0.86; 95% CI 0.77‐0.95), Wind et al (AUC 0.62; 95% CI 0.49‐0.76), Davila et al (AUC 0.80; 95% CI 0.708‐0.901) and the Cox regression model by Suntharalingam et al (Harrell's c‐index 0.63), exhibited good discrimination and could fairly identify which patients died within 60 minutes. Previous prediction models did not incorporate the process of WLST. We believe that future studies should also include the process of WLST as an important predictor.

Many patients with acute devastating brain injury die outside intensive care units and could go unrecognized as potential organ donors. We conducted a prospective observational study in seven hospitals in the Netherlands to define the number of unrecognized potential organ donors outside intensive care units, and to identify the effect that end-of-life care has on organ donor potential. Records of all patients who died between January 2013 and March 2014 were reviewed. Patients were included if they died within 72 h after hospital admission outside the intensive care unit due to devastating brain injury, and fulfilled the criteria for organ donation. Physicians of included patients were interviewed using a standardized questionnaire regarding logistics and medical decisions related to end-of-life care. Of the 5170 patients screened, we found 72 additional potential organ donors outside intensive care units. Initiation of end-of-life care in acute settings and lack of knowledge and experience in organ donation practices outside intensive care units can result in under-recognition of potential donors equivalent to 11-34% of the total pool of organ donors. Collaboration with the intensive care unit and adjusting the end-of-life path in these patients is required to increase the likelihood of organ donation.

Background. Donation after circulatory death (DCD) is a procedure in which after planned withdrawal of life-sustaining treatment (WLST), the dying process is monitored. A DCD procedure can only be continued if the potential organ donor dies shortly after WLST. This study performed an external validation of 2 existing prediction models to identify potentially DCD candidates, using one of the largest cohorts. Methods. This multicenter retrospective study analyzed all patients eligible for DCD donation from 2010 to 2015. The first model (DCD-N score) assigned points for absence of neurological reflexes and oxygenation index. The second model, a linear prediction model (LPDCD), yielded the probability of death within 60 min. This study determined discrimination (c-statistic) and calibration (Hosmer and Lemeshow test) for both models. Results. This study included 394 patients, 283 (72%) died within 60 min after WLST. The DCD-N score had a c-statistic of 0.77 (95% confidence intervals, 0.71-0.83) and the LPDCD model 0.75 (95% confidence intervals, 0.68-0.81). Calibration of the LPDCD 60-min model proved to be poor (Hosmer and Lemeshow test, P < 0.001). Conclusions. The DCD-N score and the LPDCD model showed good discrimination but poor calibration for predicting the probability of death within 60 min. Construction of a new prediction model on a large data set is needed to obtain better calibration.

Background. The aim of this study was to evaluate the implementation process of a multidisciplinary approach for potential organ donors in the emergency department (ED) in order to incorporate organ donation into their end-of-life care plans. Methods. A new multidisciplinary approach was implemented in 6 hospitals in The Netherlands between January 2016 and January 2018. The approach was introduced during staff meetings in the ED, intensive care unit (ICU), and neurology department. When patients with a devastating brain injury had a futile prognosis in the ED, without contraindications for organ donation, an ICU admission was considered. Every ICU admission to incorporate organ donation into end-of-life care was systematically evaluated with the involved physicians using a standardized questionnaire. Results. In total, 55 potential organ donors were admitted to the ICU to incorporate organ donation into end-of-life care. Twenty-seven families consented to donation and 20 successful organ donations were performed. Twenty-nine percent of the total pool of organ donors in these hospitals were admitted to the ICU for organ donation. Conclusions. Patients with a devastating brain injury and futile medical prognosis in the ED are an important proportion of the total number of donors. The implementation of a multidisciplinary approach is feasible and could lead to better identification of potential donors in the ED.

Background. Acceptance of organs from controlled donation after circulatory death (cDCD) donors depends on the time to circulatory death. Here we aimed to develop and externally validate prediction models for circulatory death within 1 or 2 h after withdrawal of life-sustaining treatment. Methods. In a multicenter, observational, prospective cohort study, we enrolled 409 potential cDCD donors. For model development, we applied the least absolute shrinkage and selection operator (LASSO) regression and machine learning–artificial intelligence analyses. Our LASSO models were validated using a previously published cDCD cohort. Additionally, we validated 3 existing prediction models using our data set. Results. For death within 1 and 2 h, the area under the curves (AUCs) of the LASSO models were 0.77 and 0.79, respectively, whereas for the artificial intelligence models, these were 0.79 and 0.81, respectively. We were able to identify 4% to 16% of the patients who would not die within these time frames with 100% accuracy. External validation showed that the discrimination of our models was good (AUCs 0.80 and 0.82, respectively), but they were not able to identify a subgroup with certain death after 1 to 2 h. Using our cohort to validate 3 previously published models showed AUCs ranging between 0.63 and 0.74. Calibration demonstrated that the models over- and underestimated the predicted probability of death. Conclusions. Our models showed a reasonable ability to predict circulatory death. External validation of our and 3 existing models illustrated that their predictive ability remained relatively stable. We accurately predicted a subset of patients who died after 1 to 2 h, preventing starting unnecessary donation preparations, which, however, need external validation in a prospective cohort.