SBIR Phase I: Objective Assessment of Agitation and Sedation

SBIR 第一阶段：躁动和镇静的客观评估

• 批准号: 1315336
• 负责人: Behnood Gholami
• 金额: $ 15万
• 依托单位: Autonomous Healthcare Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1315336&HistoricalAwards=false
• 关键词: SBIR; Phase; Objective; Assessment; Agitation

This Small Business Innovation Research Phase I project is will investigate the feasibility of developing an objective agitation and sedation assessment algorithm for patients in the intensive care unit (ICU). Use of machine learning to identify clinically relevant information to characterize the agitation and sedation state of the patients is investigated. Furthermore, a plan to ?calibrate? and validate the agitation and sedation score provided by the algorithm will be designed. Agitation and sedation assessment is a challenging problem for patients undergoing critical care. Agitation, which is primarily characterized by excessive gross motor movement, is experienced by 74% of adults during ICU stay. Agitated patients may do physical harm to themselves by dislodging vital life support and monitoring devices with excessive musculoskeletal activity. Oversedation increases risk to the patient since liberation from mechanical ventilation may not be possible due to a diminished level of consciousness and respiratory depression from sedative. Currently, the assessment is performed by the clinical staff and no technology exists for such assessment. It is anticipated that through this research, novel algorithms for reliable detection of a patient?s agitation and sedation state using their physiological signals will be developed.The broader impact/commercial potential of this project includes reduction in clinical staff workload and healthcare costs. Current clinical practice in patient critical care requires the nursing staff to assess the patient's agitation and sedation state and provide sedatives to ameliorate the patient's agitation. The process relies on subjective assessments and may result in oversedation, which in turn increases the number of interventions, length of mechanical ventilation, and duration of stay in the ICU, and hence, increases healthcare costs. Development of an objective agitation and sedation assessment system can have a great impact on the quality of care in a critical care setting. Such a system can enable continuous patient monitoring and increase quality of care. Currently, clinical staff need to attend to multiple patients and continuous monitoring of patients is not feasible. In addition, in the absence of an automated agitation and sedation assessment algorithm, early indications of undersedation or oversedation can be overlooked due to the complex nature of the patient critical care problem and clinical staff's workload.

这个小企业创新研究第一阶段项目将研究为重症监护病房（ICU）患者开发客观躁动和镇静评估算法的可行性。使用机器学习来识别临床相关信息，以表征患者的躁动和镇静状态。此外，还有一个“校准”计划。并对算法提供的躁动和镇静评分进行验证。躁动和镇静评估是一个具有挑战性的问题，病人接受重症监护。躁动主要表现为过度的大肌肉运动，74%的成年人在ICU住院期间经历过躁动。激动的患者可能会因过度的肌肉骨骼活动而使重要的生命支持和监测设备脱落，从而对自己造成身体伤害。过度镇静会增加患者的风险，因为由于镇静造成的意识水平下降和呼吸抑制，可能无法从机械通气中解脱出来。目前，这种评估是由临床工作人员进行的，不存在这种评估的技术。预计通过这项研究，新的算法可以可靠地检测患者。利用它们的生理信号来开发美国的躁动和镇静状态。该项目的更广泛影响/商业潜力包括减少临床工作人员的工作量和医疗保健费用。目前临床在危重病人护理中，需要护理人员评估患者的躁动和镇静状态，并提供镇静剂来改善患者的躁动。该过程依赖于主观评估，并可能导致过度镇静，这反过来又增加了干预次数、机械通气时间和在ICU的住院时间，因此增加了医疗保健费用。开发一个客观的躁动和镇静评估系统可以对重症监护设置的护理质量产生很大的影响。这样的系统可以实现对患者的持续监测，提高护理质量。目前，临床工作人员需要照顾多名患者，对患者进行持续监测是不可行的。此外，由于患者重症监护问题的复杂性和临床工作人员的工作量，在缺乏自动躁动和镇静评估算法的情况下，可能会忽略镇静不足或过度镇静的早期指征。