1. Promoting the process of determining brain death through standardized training.
- Author
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Yingying Su, Yan Zhang, Hong Ye, Weibi Chen, Linlin Fan, Gang Liu, Huijin Huang, Daiquan Gao, and Yunzhou Zhang
- Subjects
BRAIN death ,PROOF & certification of death ,SOMATOSENSORY evoked potentials ,MULTIPLE regression analysis ,ERROR rates - Abstract
Objective: This study aims to explore the training mode for brain death determination to ensure the quality of subsequent brain death determination. Methods: A four-skill and four-step (FFT) training model was adopted, which included a clinical neurological examination, an electroencephalogram (EEG) examination, a short-latency somatosensory evoked potential (SLSEP) examination, and a transcranial Doppler (TCD) examination. Each skill is divided into four steps: multimedia theory teaching, bedside demonstration, one-on-one real or dummy simulation training, and assessment. The authors analyzed the training results of 1,577 professional and technical personnel who participated in the FFT training model from 2013 to 2020 (25 sessions), including error rate analysis of the written examination, knowledge gap analysis, and influencing factors analysis. Results: The total error rates for all four written examination topics were < 5%, at 4.13% for SLSEP, 4.11% for EEG, 3.71% for TCD, and 3.65% for clinical evaluation. The knowledge gap analysis of the four-skill test papers suggested that the trainees had different knowledge gaps. Based on the univariate analysis and the multiple linear regression analysis, among the six factors, specialty categories, professional and technical titles, and hospital level were the independent influencing factors of answer errors (p < 0.01). Conclusion: The FFT model is suitable for brain death (BD) determination training in China; however, the authors should pay attention to the professional characteristics of participants, strengthen the knowledge gap training, and strive to narrow the difference in training quality. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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