利用机器学习在澳大利亚医院实施实时预警系统，以改善患者预后。

The implementation of a real time early warning system using machine learning in an Australian hospital to improve patient outcomes.

机构信息

Sydney Adventist Hospital, Sydney, Australia; Royal North Shore Hospital, Sydney, Australia.

The University of Sydney, Faculty of Medicine and Health, School of Medical Sciences, Biomedical Informatics and Digital Health, Sydney, Australia.

出版信息

Resuscitation. 2023 Jul;188:109821. doi: 10.1016/j.resuscitation.2023.109821. Epub 2023 May 5.

DOI:10.1016/j.resuscitation.2023.109821

PMID:37150397

Abstract

BACKGROUND

Early Warning Scores (EWS) monitor inpatient deterioration predominantly using vital signs. We evaluated inpatient outcomes after implementing an Artificial Intelligence (AI) based intervention in our local EWS.

METHODS

A prior study calculated a Deterioration Index (DI) with logistic regression utilising demographics, vital signs, and laboratory results at multiple time points to predict any major adverse event (MAE-all cause mortality, ICU admission, or medical emergency team activation). The current study is a single hospital, pre-post study in Australia comparing the DI plus the existing EWS (Between the Flags-BTF) to only BTF. Data were collected on all eligible inpatients (≥16 years, admitted ≥24 hours, in general non-palliative wards). Controls were inpatients in the same hospital between January and December 2019. The DI was integrated into the electronic medical record and alerts were sent to senior ward nurse phones (July 2020-April 2021).

RESULTS

We enrolled 28,639 patients (median age 73 years, IQR: 60-83) with 52.3% female. The intervention and control groups did not show any statistically significant differences apart from reduced admissions via the emergency department in the intervention group (40.4% vs 41.6%, P = 0.03). Risk for an MAE was lower in intervention than control (RR: 0.81; 95%CI: 0.74-0.89). Length of hospital stay was significantly reduced in the intervention group (3.74 days, IQR 1.84-7.26) compared to the control group (3.86 days, IQR 1.86-7.86, P = 0.002) CONCLUSIONS: Implementing the DI in one hospital in Australia was associated with some improved patient outcomes. Future RCTs are needed for further validation.

摘要

背景

早期预警评分（EWS）主要通过生命体征监测住院患者的病情恶化情况。我们评估了在当地 EWS 中实施人工智能（AI）干预后的住院患者结局。

方法

先前的一项研究利用逻辑回归计算了一个恶化指数（DI），该指数利用了多个时间点的人口统计学、生命体征和实验室结果来预测任何主要不良事件（MAE-全因死亡率、入 ICU 或医疗急救团队激活）。本研究是澳大利亚的一项单中心前后研究，比较了 DI 加现有的 EWS（Between the Flags-BTF）与仅 BTF。所有符合条件的住院患者（≥16 岁，住院≥24 小时，在普通非姑息病房）均纳入数据收集。对照组为 2019 年 1 月至 12 月期间在同一家医院的住院患者。DI 被整合到电子病历中，警报被发送到高级病房护士手机（2020 年 7 月至 2021 年 4 月）。

结果

我们共纳入 28639 名患者（中位年龄 73 岁，IQR：60-83），其中 52.3%为女性。干预组和对照组除了干预组通过急诊科入院的人数减少（40.4%比 41.6%，P=0.03）外，无统计学显著差异。干预组 MAE 风险低于对照组（RR：0.81；95%CI：0.74-0.89）。与对照组相比（3.86 天，IQR 1.86-7.86），干预组的住院时间明显缩短（3.74 天，IQR 1.84-7.26，P=0.002）。