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Journal ArticleDOI

Prediction of potential for organ donation after circulatory death in neurocritical patients

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TLDR
The C-DCD-Nomogram is superior to the other 3 tools for predicting death within a limited duration after withdrawal of life-sustaining treatment in Chinese neurocritical patients and appears to be a reliable tool identifying potential donors after circulatory death.
Abstract
Background The success or failure of donation after circulatory death depends largely on the functional warm ischemia time , which is closely related to the duration between withdrawal of life-sustaining treatment and circulatory arrest However, a reliable predictive model for the duration is absent We aimed to compare the performance of the Chinese Donation after Circulatory Death Nomogram (C-DCD-Nomogram) and 3 other tools in a cohort of potential donors Methods In this prospective, multicenter, observational study , data were obtained from 219 consecutive neurocritical patients in China The patients were followed until circulatory death after withdrawal of life-sustaining treatment Results The C-DCD-Nomogram performed well in predicting patient death within 30, 60, 120 and 240 minutes after withdrawal of life-sustaining treatment with c-statistics of 087, 088, 086 and 095, respectively The DCD-N score was a poor predictor of death within 30, 60 and 240 minutes, with c-statistics of 063, 069 and 059, respectively, although it was able to predict patient death within 120 minutes, with a c-statistic of 073 Neither the University of Wisconsin DCD evaluation tool (UWDCD) nor the United Network for Organ Sharing (UNOS) criteria was able to predict patient death within 30, 60, 120 and 240 minutes after withdrawal of life-sustaining treatment (UWDCD tool: 048, 045, 049 and 057; UNOS criteria: 050, 053, 051 and 063) Conclusion The C-DCD-Nomogram is superior to the other 3 tools for predicting death within a limited duration after withdrawal of life-sustaining treatment in Chinese neurocritical patients Thus, it appears to be a reliable tool identifying potential donors after circulatory death

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Citations
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External Validation of the DCD-N Score and a Linear Prediction Model to Identify Potential Candidates for Organ Donation After Circulatory Death: A Nationwide Multicenter Cohort Study.

TL;DR: An external validation of 2 existing prediction models to identify potentially DCD candidates, using one of the largest cohorts, showed good discrimination but poor calibration for predicting the probability of death within 60 min.
Journal ArticleDOI

Controlled DCD lung transplantation: Circumventing imagined and real barriers-time for an international taskforce?

TL;DR: In this article , the potential benefits associated with increased DCD utilization as well as the perceived barriers to the expansion of DCD are examined, and solutions are offered as a means to expand DCD utilisation across centers and nations.
Journal ArticleDOI

The future of heart procurement with donation after circulatory death: Current practice and opportunities for advancement.

TL;DR: In this paper , the authors describe the current practice and outstanding questions related to fundamental aspects of DCD heart procurement, including donor selection, premortem donor intervention, ischemic definitions, confirmation of circulatory death, and techniques for heart procurement and preservation.
Journal ArticleDOI

Identification of separation phenomenon during brain death determination of potential organ donor and analysis of relative factors

TL;DR: The application of median nerve short latency evoked potential (SLSEP) in primary brain stem injury or children is limited, and it is hard to be understand by patients' families and non-neurological healthy workers.
Journal ArticleDOI

When circulatory death does not come in time in potential organ donors

TL;DR: Based on the analysis of nationwide data of all 143 patients who entered the cDCD program, but in whom organs were not procured due to delayed circulatory death, it is recommended that age and brainstem reflexes should at least be studied in future studies on multimodal prediction models on time to death.
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Journal ArticleDOI

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