Kamau Stephen, Kigo Joyce, Mwaniki Paul, Dunsmuir Dustin, Pillay Yashodani, Zhang Cherri, Nyamwaya Brian, Kimutai David, Ouma Mary, Mohammed Ismael, Gachuhi Keziah, Chege Mary, Thuranira Lydia, Ansermino J Mark, Akech Samuel
Health Service Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya.
Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, Canada.
PLOS Digit Health. 2024 Aug 1;3(8):e0000408. doi: 10.1371/journal.pdig.0000408. eCollection 2024 Aug.
Several triage systems have been developed, but little is known about their performance in low-resource settings. Evaluating and comparing novel triage systems to existing triage scales provides essential information about their added value, reliability, safety, and effectiveness before adoption. This study included children aged < 15 years who presented to the emergency departments of two public hospitals in Kenya between February and December 2021. We compared the performance of Emergency Triage Assessment and Treatment (ETAT) guidelines and Smart Triage (ST) models (ST model with independent triggers, and recalibrated ST model with independent triggers) in categorizing children into emergency, priority, and non-urgent triage categories. Sankey diagrams were used to visualize the distribution of children into similar or different triage categories by ETAT and ST models. Sensitivity, specificity, negative and positive predictive values for mortality and admission were calculated. 5618 children were enrolled, and the majority (3113, 55.4%) were aged between one and five years of age. Overall admission and mortality rates were 7% and 0.9%, respectively. ETAT classified 513 (9.2%) children into the emergency category compared to 1163 (20.8%) and 1161 (20.7%) by the ST model with independent triggers and recalibrated model with independent triggers, respectively. ETAT categorized 3089 (55.1%) children as non-urgent compared to 2097 (37.4%) and 2617 (46.7%) for the respective ST models. ETAT classified 191/395 (48.4%) admitted patients as emergencies compared to more than half by all the ST models. ETAT and ST models classified 25/49 (51%) and 39/49 (79.6%) deceased children as emergencies. Sensitivity for admission and mortality was 48.4% and 51% for ETAT and 74.9% and 79.6% for the ST models, respectively. Smart Triage shows potential for identifying critically ill children in low-resource settings, particularly when combined with independent triggers and performs comparably to ETAT. Evaluation of Smart Triage in other contexts and comparison to other triage systems is required.
已经开发了几种分诊系统，但对于它们在资源匮乏环境中的表现知之甚少。在采用新的分诊系统之前，将其与现有的分诊量表进行评估和比较，可以提供有关其附加值、可靠性、安全性和有效性的重要信息。本研究纳入了2021年2月至12月期间前往肯尼亚两家公立医院急诊科就诊的15岁以下儿童。我们比较了急诊分诊评估与治疗（ETAT）指南和智能分诊（ST）模型（具有独立触发因素的ST模型，以及重新校准的具有独立触发因素的ST模型）在将儿童分为急诊、优先和非紧急分诊类别的表现。使用桑基图来直观展示ETAT和ST模型将儿童分配到相似或不同分诊类别的分布情况。计算了死亡率和入院率的敏感性、特异性、阴性和阳性预测值。共纳入5618名儿童，其中大多数（3113名，55.4%）年龄在1至5岁之间。总体入院率和死亡率分别为7%和0.9%。ETAT将513名（9.2%）儿童分类为急诊类别，而具有独立触发因素的ST模型和重新校准的具有独立触发因素的ST模型分别将1163名（20.8%）和1161名（20.7%）儿童分类为急诊类别。ETAT将3089名（55.1%）儿童分类为非紧急，而相应的ST模型分别为2097名（37.4%）和2617名（46.7%）。ETAT将191/395名（48.4%）入院患者分类为急诊，而所有ST模型的这一比例超过一半。ETAT和ST模型分别将25/49名（51%）和39/49名（79.6%）死亡儿童分类为急诊。ETAT入院和死亡率的敏感性分别为48.4%和51%，ST模型分别为74.9%和79.6%。智能分诊在资源匮乏环境中识别重症儿童方面显示出潜力，特别是与独立触发因素相结合时，其表现与ETAT相当。需要在其他环境中对智能分诊进行评估，并与其他分诊系统进行比较。
