Centre for Urgent and Emergency Care, School of Health and Related Research, The University of Sheffield, Sheffield, United Kingdom.
Design, Trials and Statistics, School of Health and Related Research, The University of Sheffield, Sheffield, United Kingdom.
PLoS One. 2022 Nov 16;17(11):e0276515. doi: 10.1371/journal.pone.0276515. eCollection 2022.
One of the main problems currently facing the delivery of safe and effective emergency care is excess demand, which causes congestion at different time points in a patient's journey. The modern case-mix of prehospital patients is broad and complex, diverging from the traditional 'time critical accident and emergency' patients. It now includes many low-acuity patients and those with social care and mental health needs. In the ambulance service, transport decisions are the hardest to make and paramedics decide to take more patients to the ED than would have a clinical benefit. As such, this study asked the following research questions: In adult patients attending the ED by ambulance, can prehospital information predict an avoidable attendance? What is the simulated transportability of the model derived from the primary outcome? A linked dataset of 101,522 ambulance service and ED ambulance incidents linked to their respective ED care record from the whole of Yorkshire between 1st July 2019 and 29th February 2020 was used as the sample for this study. A machine learning method known as XGBoost was applied to the data in a novel way called Internal-External Cross Validation (IECV) to build the model. The results showed great discrimination with a C-statistic of 0.81 (95%CI 0.79-0.83) and excellent calibration with an O:E ratio was 0.995 (95% CI 0.97-1.03), with the most important variables being a patient's mobility, their physiological observations and clinical impression with psychiatric problems, allergic reactions, cardiac chest pain, head injury, non-traumatic back pain, and minor cuts and bruising being the most important. This study has successfully developed a decision-support model that can be transformed into a tool that could help paramedics make better transport decisions on scene, known as the SINEPOST model. It is accurate, and spatially validated across multiple geographies including rural, urban, and coastal. It is a fair algorithm that does not discriminate new patients based on their age, gender, ethnicity, or decile of deprivation. It can be embedded into an electronic Patient Care Record system and automatically calculate the probability that a patient will have an avoidable attendance at the ED, if they were transported. This manuscript complies with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement (Moons KGM, 2015).
目前，安全有效地提供紧急护理所面临的主要问题之一是需求过剩，这导致患者在就诊过程中的不同时间点出现拥堵。现在，院前患者的病例组合广泛而复杂，与传统的“时间关键型事故和急诊”患者不同。现在，它包括许多低危患者和有社会护理及心理健康需求的患者。在救护服务中，最难做出的是转运决定，护理人员决定将更多的患者送往急诊室，而这些患者并不会从中获得临床益处。因此，本研究提出了以下研究问题：在通过救护车前往急诊室的成年患者中，院前信息能否预测是否可避免就诊？从主要结局推导的模型的模拟可转运性如何？本研究的样本是一个从 2019 年 7 月 1 日至 2020 年 2 月 29 日期间整个约克郡的 101522 例救护车服务和 ED 救护车事件与其各自的 ED 护理记录相关联的数据集。一种名为 XGBoost 的机器学习方法以一种名为内部-外部交叉验证（IECV）的新方法应用于数据，以构建模型。结果显示，该模型具有出色的区分度，C 统计量为 0.81（95%CI 0.79-0.83），极好的校准度，O:E 比值为 0.995（95%CI 0.97-1.03），最重要的变量是患者的移动能力、生理观察和有精神问题、过敏反应、心前区疼痛、头部损伤、非外伤性背痛、轻度割伤和瘀伤的临床印象。本研究成功开发了一种决策支持模型，可以转化为一种工具，帮助护理人员在现场做出更好的转运决策，该模型被称为 SINEPOST 模型。该模型准确，并且在包括农村、城市和沿海地区在内的多个地理区域都经过了空间验证。它是一种公平的算法，不会根据患者的年龄、性别、种族或贫困程度的十分位数来歧视新患者。它可以嵌入到电子患者护理记录系统中，并自动计算患者如果被转运，是否会在 ED 避免就诊的概率。本手稿符合多变量预测个体预后或诊断模型的透明报告（TRIPOD）声明（Moons KGM，2015）。
