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一项护士主导的人工智能辅助预防和管理 ICU 谵妄(AI-AntiDelirium)的集束随机对照试验:研究方案。

A cluster-randomized controlled trial of a nurse-led artificial intelligence assisted prevention and management for delirium (AI-AntiDelirium) on delirium in intensive care unit: Study protocol.

机构信息

School of Nursing, Capital Medical University, Beijing, China.

Cardiology Department, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.

出版信息

PLoS One. 2024 Feb 29;19(2):e0298793. doi: 10.1371/journal.pone.0298793. eCollection 2024.

Abstract

BACKGROUND

Delirium is a common complication among intensive care unit (ICU) patients that is linked to negative clinical outcomes. However, adherence to the Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU (PADIS guidelines), which recommend the use of the ABCDEF bundle, is sub-optimal in routine clinical care. To address this issue, AI-AntiDelirium, a nurse-led artificial intelligence-assisted prevention and management tool for delirium, was developed by our research team. Our pilot study yielded positive findings regarding the use of AI-AntiDelirium in preventing patient ICU delirium and improving activities of daily living and increasing intervention adherence by health care staff.

METHODS

The proposed large-scale pragmatic, open-label, parallel-group, cluster randomized controlled study will assess the impact of AI-AntiDelirium on the incidence of ICU delirium and delirium-related outcomes. Six ICUs in two tertiary hospitals in China will be randomized in a 1:1 ratio to an AI-AntiDelirium or a PADIS guidelines group. A target sample size of 1,452 ICU patients aged 50 years and older treated in the ICU for at least 24 hours will be included. The primary outcome evaluated will be the incidence of ICU delirium and the secondary outcomes will be the duration of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, patient activities of daily living, and ICU nurse adherence to the ABCDEF bundle.

DISCUSSION

If this large-scale trial provides evidence of the effectiveness of AI-AntiDelirium, an artificial intelligence-assisted system tool, in decreasing the incidence of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, and patient activities of daily living while increasing ICU nurse adherence to the ABCDEF bundle, it will have a profound impact on the management of ICU delirium in both research and clinical practice.

CLINICAL TRIAL REGISTRATION

ChiCTR1900023711 (Chinese Clinical Trial Registry).

摘要

背景

谵妄是重症监护病房(ICU)患者常见的并发症,与不良临床结局相关。然而,在常规临床护理中,对预防和管理 ICU 成人患者疼痛、躁动/镇静、谵妄、活动受限和睡眠障碍的临床实践指南(PADIS 指南)的依从性并不理想,该指南建议使用 ABCDEF 捆绑包。为了解决这个问题,我们的研究团队开发了一种由护士主导的人工智能辅助谵妄预防和管理工具 AI-AntiDelirium。我们的初步研究结果表明,使用 AI-AntiDelirium 预防患者 ICU 谵妄、改善日常生活活动和提高医护人员干预依从性是有效的。

方法

本研究拟采用大规模、实用、开放性、平行组、集群随机对照研究,评估 AI-AntiDelirium 对 ICU 谵妄发生率和谵妄相关结局的影响。中国两家三级医院的 6 个 ICU 将以 1:1 的比例随机分为 AI-AntiDelirium 组或 PADIS 指南组。纳入 ICU 治疗至少 24 小时、年龄≥50 岁的 ICU 患者 1452 例。主要结局为 ICU 谵妄发生率,次要结局为 ICU 谵妄持续时间、ICU 和住院时间、ICU 和住院死亡率、患者认知功能、患者日常生活活动和 ICU 护士对 ABCDEF 捆绑包的依从性。

讨论

如果这项大规模试验提供了 AI-AntiDelirium(一种人工智能辅助系统工具)在降低 ICU 谵妄发生率、ICU 和住院时间、ICU 和住院死亡率、患者认知功能和日常生活活动能力,同时提高 ICU 护士对 ABCDEF 捆绑包的依从性方面的有效性证据,它将对 ICU 谵妄的管理在研究和临床实践中产生深远的影响。

临床试验注册

ChiCTR1900023711(中国临床试验注册中心)。

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