Nelin Sarah, Karam Simon, Foglia Elizabeth, Turk Philip, Peddireddy Venu, Desai Jagdish
Department of Pediatrics, University of Mississippi Medical Center, Jackson, MS 39216, USA.
Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
Children (Basel). 2024 Sep 19;11(9):1137. doi: 10.3390/children11091137.
Neonatal resuscitation is guided by Neonatal Resuscitation Program (NRP) algorithms; however, human factors affect resuscitation. Video recordings demonstrate that deviations are common. Additionally, code documentation is prone to inaccuracies. Our long-term hypothesis is that the use of an automated resuscitation recorder (ARR) app will improve adherence to NRP and code documentation; the purpose of this study was to determine its feasibility.
We performed a simulation-based feasibility study using simulated code events mimicking NRP scenarios. Teams used the app during resuscitation events. We collected data via an initial demographics survey, video recording, ARR-generated code summary and a post-resuscitation survey. We utilized standardized grading tools to assess NRP adherence and the accuracy of code documentation through resuscitation data point (RDP) scoring. We evaluated provider comfort with the ARR via post-resuscitation survey ordinal ratings and open-ended question text mining.
Summary statistics for each grading tool were computed. For NRP adherence, the median was 68% (range 60-76%). For code documentation accuracy and completeness, the median was 77.5% (range 55-90%). When ordinal ratings assessing provider comfort with the app were reviewed, 47% chose "agree" (237/500) and 36% chose "strongly agree" (180/500), with only 0.6% (3/500) answering "strongly disagree". A word cloud compared frequencies of words from the open-ended text question.
We demonstrated the feasibility of ARR use during neonatal resuscitation. The median scores for each grading tool were consistent with passing scores. Post-resuscitation survey data showed that participants felt comfortable with the ARR while highlighting areas for improvement. A pilot study comparing ARR with standard of care is the next step.
新生儿复苏由新生儿复苏项目（NRP）算法指导；然而，人为因素会影响复苏过程。视频记录显示，偏差很常见。此外，抢救记录容易出现不准确的情况。我们的长期假设是，使用自动复苏记录器（ARR）应用程序将提高对NRP的依从性和抢救记录的质量；本研究的目的是确定其可行性。
我们使用模拟NRP场景的模拟抢救事件进行了一项基于模拟的可行性研究。团队在复苏事件中使用该应用程序。我们通过初始人口统计学调查、视频记录、ARR生成的抢救总结和复苏后调查收集数据。我们利用标准化评分工具，通过复苏数据点（RDP）评分来评估对NRP的依从性和抢救记录的准确性。我们通过复苏后调查的有序评分和开放式问题文本挖掘来评估提供者对ARR的舒适度。
计算了每个评分工具的汇总统计数据。对于NRP依从性，中位数为68%（范围为60 - 76%）。对于抢救记录的准确性和完整性，中位数为77.5%（范围为55 - 90%）。当查看评估提供者对该应用程序舒适度的有序评分时，47%的人选择“同意”（237/500），36%的人选择“强烈同意”（180/500），只有0.6%（3/500）的人回答“强烈不同意”。词云比较了开放式文本问题中单词的频率。
我们证明了在新生儿复苏期间使用ARR的可行性。每个评分工具的中位数分数与及格分数一致。复苏后调查数据显示，参与者对ARR感到满意，同时也突出了需要改进的方面。下一步是进行一项将ARR与护理标准进行比较的试点研究。
