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

当受伤或疾病严重损害重要的身体功能时，在称为重症监护病房(ICU)的专门医院单元中提供生命维持干预。ICU的患者管理是复杂的，医生通过分析快速变化的患者数据来做出至关重要的决定。目前的ICU预后预测工具仅提供医院死亡率的预测，因此不能用于预测患者在ICU期间的临床结果。由于现有预后预测工具的局限性，ICU临床决策导致患者护理不规范，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)医生新斯科舍省合作，旨在基于加拿大ICU数据开发新的ICU预后模型，以帮助ICU医生在临床重要时间点做出有效的护理决策，以应对患者在整个ICU逗留期间的病情变化。该项目将研究深度学习(DL)方法，以开发ICU结果预测模型，以预测5个临床重要时间点的临床结果--即入院时、24、48、72小时，出院前24小时。我们建议解决两个关键挑战--即(A)逐步更新预测模型，以确保与新的ICU数据的预测相关性；以及(B)对患者在ICU停留期间不同时间点的临床结果进行时间概率预测。我们将整合哈利法克斯新斯科舍省卫生管理局中心区(NSHA-CZ)ICU的多个患者数据源-即ICU临床资料、病理资料和放射学资料。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对预测结果提供临床上有意义的解释。这些预测模型将根据NSHA-CZ的新ICU病例在一年的时间内进行前瞻性评估，以衡量模型预测的临床后果。该项目将提供一个原型ICU预测和规划系统(ICU-PPS)，其中包括为NSHA-CZ ICU开发的预测模型。

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.