Phelps Kamal, Silos Christin, De La Torre Susan, Moreno Amee, Lapus Robert, Sanghani Nipa, Koenig Mary, Savitz Sean, Green Charles, Fraser Stuart
University of Texas McGovern Medical School, Houston, TX, United States.
School of Biomedical Informatics, The University of Texas Health Science Center Houston, Houston, TX, United States.
Front Neurol. 2023 May 18;14:1194990. doi: 10.3389/fneur.2023.1194990. eCollection 2023.
Pediatric stroke is among the top 10 causes of death in pediatrics. Rapid recognition and treatment can improve outcomes in select patients, as evidenced by recent retrospective studies in pediatric thrombectomy. We established a collaborative protocol involving the vascular neurology and pediatric neurology division in our institution to rapidly diagnose and treat pediatric suspected stroke. We also prospectively collected data to attempt to identify predictors of acute stroke in pediatric patients.
IRB approval was obtained to prospectively collect clinical data on pediatric code stroke activations based on timing metrics in resident-physician note templates. The protocol emphasized magnetic resonance imaging over computed tomography imaging when possible. We analyzed performance of the system with descriptive statistics. We then performed a Bayesian statistical analysis to search for predictors of pediatric stroke.
There were 40 pediatric code strokes over the 2.5-year study period with a median age of 10.8 years old. 12 (30%) of patients had stroke, and 28 (70%) of code stroke patients were diagnosed with a stroke mimic. Median time from code stroke activation to completion of imaging confirming or ruling out stroke was 1 h. In the Bayesian analysis, altered mental status, hemiparesis, and vasculopathy history were associated with increased odds of stroke, though credible intervals were wide due to the small sample size.
A trainee developed and initiated pediatric acute stroke protocol quickly implemented a hospital wide change in management that led to rapid diagnosis and triage of pediatric stroke and suspected stroke. No additional personnel or resources were needed for this change, and we encourage other hospitals and emergency departments to implement similar systems. Additionally, hemiparesis and altered mental status were predictors of stroke for pediatric acute stroke activation in our Bayesian statistical analysis. However credible intervals were wide due to the small sample size. Further multicenter data collection could more definitively analyze predictors of stroke, as well as the help in the creation of diagnostic tools for clinicians in the emergency setting.
小儿卒中是儿科十大死因之一。快速识别和治疗可改善部分患者的预后，小儿血栓切除术的近期回顾性研究证明了这一点。我们制定了一项合作方案，涉及本机构的血管神经科和儿科神经科，以快速诊断和治疗小儿疑似卒中。我们还前瞻性收集数据，试图确定小儿急性卒中的预测因素。
获得机构审查委员会（IRB）批准，基于住院医师记录模板中的时间指标前瞻性收集小儿卒中代码激活的临床数据。该方案尽可能强调磁共振成像而非计算机断层扫描成像。我们用描述性统计分析该系统的性能。然后进行贝叶斯统计分析以寻找小儿卒中的预测因素。
在2.5年的研究期内有40例小儿卒中代码激活，中位年龄为10.8岁。12例（30%）患者发生卒中，28例（70%）卒中代码激活患者被诊断为疑似卒中。从卒中代码激活到完成确认或排除卒中的成像的中位时间为1小时。在贝叶斯分析中，精神状态改变、偏瘫和血管病变史与卒中几率增加相关，不过由于样本量小，可信区间较宽。
一名实习生制定并启动了小儿急性卒中方案，迅速在全院实施了管理变革，从而实现了小儿卒中和疑似卒中的快速诊断和分诊。这一变革无需额外的人员或资源，我们鼓励其他医院和急诊科实施类似系统。此外，在我们的贝叶斯统计分析中，偏瘫和精神状态改变是小儿急性卒中激活的卒中预测因素。然而，由于样本量小，可信区间较宽。进一步的多中心数据收集可以更明确地分析卒中的预测因素，也有助于为急诊环境中的临床医生创建诊断工具。
