Predicting clinical outcomes for patients admitted to intensive care unit: developing and validating a Canadian data based ICU prognostic and planning system

预测入住重症监护室患者的临床结果：开发和验证基于加拿大数据的 ICU 预后和规划系统

• 批准号: 566275-2021
• 负责人: Abidi, SyedSibteRaza
• 金额: $ 7.26万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=722880
• 关键词: Predicting; clinical; outcomes; patients; admitted

When injury or illness severely compromises vital bodily functions, life-supporting interventions are provided in specialized hospital units called Intensive Care Unit (ICU). Patient management in ICU is complex and physicians make life-critical decisions by analyzing rapidly changing patient data. Current ICU prognostic tools only provide a prediction of hospital mortality, hence are not useful to predict clinical outcomes during the patient's ICU stay. Due to the limitations of current prognostic tools, ICU clinical making leads to non-standardized patient care and inefficient ICU resource utilization. In partnership with (a) Nova Scotia Health Authority, Department of Critical Care, (b) Nova Scotia (NS) Department of Health and Wellness, and (c) Doctors Nova Scotia, this project aims to develop novel ICU prognostic models, based on Canadian ICU data, to help ICU physicians make effective care decisions at clinically important time points to account for changes in the patient's condition during the patient's entire ICU stay. The project will investigate Deep Learning (DL) methods to develop ICU outcome prediction models to predict clinical outcomes at 5 clinically important time points-i.e. at time of admission, after 24, 48 and 72 hours, and 24 hours before discharge. We propose to address two key challenges-i.e. (a) progressive update of the prediction models to ensure predictive relevance to new ICU data; and (b) temporal probabilistic prediction of clinical outcomes across different time points during the patient's ICU stay. We will integrate multiple patient data sources from Nova Scotia health Authority Central Zone (NSHA-CZ) ICUs in Halifax-i.e. ICU clinical data, pathology data and radiologic data. We plan to apply explainable AI methods to provide clinically meaningful explanations of the predicted outcome at the attribute and conceptual levels. The prediction models will be prospectively evaluated over a 1-year period based on new ICU cases at NSHA-CZ to measure the clinical consequences of the model's predictions. The project's will deliver a prototype ICU Prognostic and Planning System (ICU-PPS) incorporating the prediction models developed for NSHA-CZ ICUs.

当受伤或疾病严重损害重要的身体功能时，在称为重症监护室（ICU）的专门医院单位提供生命支持干预措施。ICU中的患者管理非常复杂，医生通过分析快速变化的患者数据来做出至关重要的决策。目前的ICU预后工具仅提供住院死亡率的预测，因此对于预测患者在ICU停留期间的临床结果没有用。由于目前预后工具的局限性，ICU临床制作导致非标准化的患者护理和ICU资源利用效率低下。该项目与（a）新斯科舍省卫生局重症监护部、（B）新斯科舍省（NS）卫生与健康部和（c）新斯科舍省医生合作，旨在开发新的ICU预后模型，基于加拿大ICU数据，帮助ICU医生在临床重要时间点做出有效的护理决策，以考虑患者在整个ICU期间的病情变化留下吧该项目将研究深度学习（DL）方法，以开发ICU结局预测模型，预测5个临床重要时间点的临床结局-即入院时，24小时，48小时和72小时后，以及出院前24小时。我们建议解决两个关键的挑战，即（a）逐步更新的预测模型，以确保预测的相关性，新的ICU数据;（B）时间概率预测的临床结果在不同的时间点，在病人的ICU停留。我们将整合来自Halifax的新斯科舍省卫生局中心区（NSHA-CZ）ICU的多个患者数据源，即ICU临床数据、病理学数据和放射学数据。我们计划应用可解释的人工智能方法，在属性和概念水平上为预测结果提供有临床意义的解释。将基于NSHA-CZ的新ICU病例在1年内对预测模型进行前瞻性评价，以衡量模型预测的临床后果。该项目将提供一个原型ICU预后和计划系统（ICU-PPS），其中包含为NSHA-CZ ICU开发的预测模型。