哪些因素可预测救护车向急诊科发出的预先警报?对英国 3 家救护车服务机构的常规数据进行分析。

Fiona C Sampson, Richard Pilbery, Esther Herbert, Steve Goodacre, Fiona Bell, Rob Spaight, Andy Rosser, Peter Webster, Mark Millins, Andy Pountney, Joanne Coster, Jacqui Long, Rachel O'Hara, Alexis Foster, Jamie Miles, Janette Turner, Aimee Boyd
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引用次数: 0

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目的救护车临床医生使用预先警报电话通知急诊科(ED)需要立即检查或干预的病人到达。预先警报做法的一致性对于确保急诊科做出适当反应非常重要。我们使用常规数据来描述预报警实践,并探索影响实践差异的因素。方法我们使用一个链接数据集开展了一项观察性研究,该数据集包含英国三家救护车服务机构 12 个月的救护车患者记录、救护车临床医生数据和紧急呼叫数据。我们使用 LASSO 回归来确定多变量逻辑回归模型的候选变量,以预测预警报使用的变化,分析了临床医生因素(角色、经验、资质、当班期间预警报的时间)、患者因素(NEWS2 评分、临床工作印象、年龄、性别)和医院因素(接收 ED、ED 移交延迟状态)。结果在 1,363,274 名转送至急诊室的患者数据集中,有 142,795 名患者(10.5%)接受了预警报,其中只有三分之一的患者的病情有明确的预警报路径(如败血症、STEMI、重大创伤)。病例组合(疾病敏锐度评分、临床诊断印象)是使用预警报的最强预测因素,但在对病例组合进行调整后,男性患者性别、临床医生角色、接诊医院和接诊医院周转延迟也是具有统计学意义的预测因素。结论 除病情严重程度和临床诊断印象外,预先警报的决定还取决于其他因素。需要进行研究,以确定我们的研究结果是否可在其他地方复制,以及非临床因素(如患者性别)为何会影响预警报做法。
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What factors predict ambulance pre-alerts to the emergency department? Analysis of routine data from 3 UK ambulance services.
Objective Ambulance clinicians use pre-alert calls to advise emergency departments (EDs) of the arrival of patients requiring immediate review or intervention. Consistency of pre-alert practice is important in ensuring appropriate EDs response. We used routine data to describe pre-alert practice and explore factors affecting variation in practice. Methods We undertook an observational study using a linked dataset incorporating 12 months' ambulance patient records, ambulance clinician data and emergency call data for three UK ambulance services. We used LASSO regression to identify candidate variables for multivariate logistic regression models to predict variation in pre-alert use, analysing clinician factors (role, experience, qualification, time of pre-alert during shift), patient factors (NEWS2 score, clinical working impression, age, sex) and hospital factors (receiving ED, ED handover delay status). Results From the dataset of 1,363,274 patients conveyed to ED, 142,795 (10.5%) were pre-alerted, of whom only a third were for conditions with clear pre-alert pathways (e.g. sepsis, STEMI, major trauma). Casemix (illness acuity score, clinical diagnostic impression) was the strongest predictor of pre-alert use but male patient gender, clinician role, receiving hospital, and hospital turnaround delay at receiving hospitals were also statistically significant predictors, after adjusting for casemix. There was no evidence of higher pre-alert rates in the final hour of shift. Conclusions Pre-alert decisions are determined by factors other than illness acuity and clinical diagnostic impression. Research is required to determine whether our findings are reproducible elsewhere and why non-clinical factors (e.g. patient gender) may influence pre-alert practice.
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