Development of a clinical decision support tool using machine learning to assess a patient’s individual risk of extubation failure in mechanically-ventilated surgical ICU patients

使用机器学习开发临床决策支持工具，以评估机械通气外科 ICU 患者拔管失败的个体风险

• 批准号: 531886557
• 负责人: Dr. Sabine Friedrich
• 金额: --
• 依托单位: Klinik und Poliklinik für Anästhesiologie
• 依托单位国家: 德国
• 项目类别: WBP Fellowship
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/531886557?language=en
• 关键词: Development; clinical; decision; support; tool

Patients in the intensive care unit (ICU) require intubation and mechanical ventilation for various reasons. Prolonged mechanical ventilation can result in serious complications such as ventilator-associated pneumonia, and tracheostomy may become necessary. However, patients who are extubated prematurely may need to be reintubated under suboptimal conditions, which in turn may carry a higher risk of serious complications. Both scenarios may lead to extended ICU length of stay and increased mortality. Determining when an ICU patient is ready to be extubated is a critical decision that depends on many different factors discussed among all members of the patient's care team. Risk factors for extubation failure may vary greatly among different ICU populations and individual patients. The aim of the proposed project is to develop a clinical decision support tool using machine learning to predict the risk of extubation failure in mechanically-ventilated surgical ICU patients. This tool may guide the interdisciplinary discussion around a patient’s extubation readiness. By accurately identifying patients at high risk of extubation failure and providing information on the parameters that contribute to the patient’s individual high risk, this tool would support the critical care team in tailoring the care plan to the patient’s individual risk profile, thus helping to reduce extubation failure while avoiding prolonged mechanical ventilation. Focusing on surgical ICU patients allows for the integration of risk factors specific to this population that may not be considered in a mixed ICU population due to a large proportion of missing values in non-surgical patients.

重症监护室（ICU）的患者因各种原因需要插管和机械通气。长时间机械通气可导致严重并发症，如呼吸机相关性肺炎，气管切开术可能成为必要。然而，过早拔管的患者可能需要在次优条件下重新插管，这反过来又可能带来更高的严重并发症风险。这两种情况都可能导致ICU住院时间延长和死亡率增加。确定ICU患者何时准备好拔管是一个关键的决定，取决于患者护理团队所有成员之间讨论的许多不同因素。拔管失败的危险因素在不同ICU人群和个体患者中可能差异很大。该项目的目的是开发一种使用机器学习的临床决策支持工具，以预测机械通气外科ICU患者拔管失败的风险。这个工具可以引导围绕病人的拔管准备跨学科的讨论。通过准确识别拔管失败的高风险患者并提供有关导致患者个体高风险的参数的信息，该工具将支持重症监护团队根据患者的个体风险状况定制护理计划，从而有助于减少拔管失败，同时避免长时间的机械通气。关注外科ICU患者可以整合该人群特有的风险因素，由于非外科患者中缺失值的比例很大，因此在混合ICU人群中可能不考虑这些风险因素。