Malycha James, Redfern Oliver, Pimentel Marco, Ludbrook Guy, Young Duncan, Watkinson Peter
Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Discipline of Acute Care Medicine, University of Adelaide, South Australia, Australia.
Resusc Plus. 2021 Dec 23;9:100193. doi: 10.1016/j.resplu.2021.100193. eCollection 2022 Mar.
We have developed the Hospital Alerting Via Electronic Noticeboard (HAVEN) which aims to identify hospitalised patients most at risk of reversible deterioration. HAVEN combines patients' vital-sign measurements with laboratory results, demographics and comorbidities using a machine learnt algorithm.
The aim of this study was to identify variables or concepts that could improve HAVEN predictive performance.
This was an embedded, mixed methods study. Eligible patients with the five highest HAVEN scores in the hospital (i.e., 'HAVEN Top 5') had their medical identification details recorded. We conducted a structured medical note review on these patients 48 hours post their identifiers being recorded. Methods of constant comparison were used during data collection and to analyse patient data.
The 129 patients not admitted to ICU then underwent constant comparison review, which produced three main groups. Group 1 were patients referred to specialist services (n = 37). Group 2 responded to ward-based treatment, (n = 38). Group 3 were frail and had documented treatment limitations (n = 47).
Digital-only validation methods code the cohort not admitted to ICU as 'falsely positive' in sensitivity analyses however this approach limits the evaluation of model performance. Our study suggested that coding for patients referred to other specialist teams, those with treatment limitations in place, along with those who are deteriorating but then respond to ward-based therapies, would give a more accurate measure of the value of the scores, especially in relation to cost-effectiveness of resource utilisation.
我们开发了通过电子公告栏进行医院警报（HAVEN）系统，旨在识别住院患者中最有可能出现可逆性病情恶化的患者。HAVEN系统使用机器学习算法，将患者的生命体征测量结果与实验室检查结果、人口统计学数据和合并症相结合。
本研究的目的是识别可提高HAVEN预测性能的变量或概念。
这是一项嵌入式混合方法研究。记录了医院中HAVEN评分最高的五名合格患者（即“HAVEN前五名”）的医疗识别详细信息。在记录这些患者的标识符48小时后，我们对他们的病历进行了结构化审查。在数据收集和分析患者数据时使用了持续比较法。
129名未入住重症监护病房的患者随后接受了持续比较审查，结果产生了三个主要组。第一组是转诊至专科服务的患者（n = 37）。第二组对基于病房的治疗有反应（n = 38）。第三组身体虚弱且有记录在案的治疗限制（n = 47）。
在敏感性分析中，仅采用数字验证方法会将未入住重症监护病房的队列编码为“假阳性”，然而这种方法限制了对模型性能的评估。我们的研究表明，对转诊至其他专科团队的患者、有治疗限制的患者以及病情恶化但随后对基于病房的治疗有反应的患者进行编码，将能更准确地衡量评分的价值，尤其是在资源利用的成本效益方面。
