SBIR Phase II: A Multi-Modality Sensing Approach for the Objective Assessment of Agitation and Sedation

SBIR 第二阶段：用于客观评估躁动和镇静的多模态传感方法

• 批准号: 1456404
• 负责人: Behnood Gholami
• 金额: $ 75万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1456404&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Multi; Modality

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project involves advancements in the state-of-the-art in biomedical technology, improvement of the quality of care for intensive care unit (ICU) patients, and a reduction in healthcare costs. The proposed agitation and sedation system will assist in realizing an improved distribution of ICU workload by allowing the medical staff to focus on patients with increased agitation and in need of immediate attention. It is expected that this technology will also improve the quality of care in military ICUs. The proposed project focuses on the development of a multi-modality agitation and sedation assessment system for patients in ICUs. The primary consequence of oversedation is the prolongation of mechanical ventilation and ICU stay. Prolonged ventilation is expensive and is associated with known risks, such as inadvertent extubation, laryngo-tracheal trauma, and, most significantly, ventilator-associated pneumonia. Alternatively, agitated patients can do physical harm to themselves by dislodging vital life support and monitoring devices with excessive musculoskeletal activity. Undersedation and oversedation result in excessive economic burden on the healthcare system. The annual cost of oversedation in the US is $400 million, whereas reducing mechanical ventilation in oversedated patients by just one day can result in an annual savings of $4 billion in the US alone. 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. This process relies on subjective assessments and can be influenced by personal bias. This project proposes to use machine learning algorithms to integrate data from multiple sensing modalities to objectively identify cases of undersedation and oversedation in ICU patients.

这一小型企业创新研究(SBIR)第二阶段项目的更广泛影响/商业潜力包括最先进的生物医学技术的进步，改善重症监护病房(ICU)患者的护理质量，以及降低医疗成本。拟议的煽动和镇静系统将有助于改善ICU工作量的分配，使医务人员能够专注于情绪激动和需要立即救治的病人。预计这项技术还将提高军事ICU的护理质量。拟议的项目重点是为ICU中的患者开发一种多模式兴奋和镇静评估系统。过度镇静的主要后果是延长机械通气和ICU的停留时间。延长呼吸机是昂贵的，而且与已知的风险有关，如不慎拔管、喉-气管损伤，最重要的是呼吸机相关性肺炎。或者，焦虑症患者可能会通过移开重要的生命支持和过度肌肉骨骼活动的监测设备而对自己造成身体伤害。镇静不足和镇静过度会给医疗保健系统带来过大的经济负担。在美国，每年过度镇静的成本为4亿美元，而仅在美国，将过度镇静患者的机械通风减少一天就可以每年节省40亿美元。目前重症监护的临床实践要求护理人员对患者S躁动和镇静状态进行评估，并给予镇静药物以缓解患者S躁动。这一过程依赖于主观评估，并可能受到个人偏见的影响。该项目建议使用机器学习算法来整合来自多个传感模式的数据，以客观地识别ICU患者的镇静不足和过度镇静的情况。