STTR Phase I: Development of a Machine Learning Platform to Predict Surgical Complications

这个小型企业技术转移（STTR）第一阶段项目的更广泛的影响/商业潜力是为外科手术创建一个个性化的、基于精确的实践范例，以提高手术效果并降低医疗保健成本。这种模式利用个体患者特征和机器学习算法来准确预测术后并发症的风险。此外，它提供了在高风险患者中实施有效干预的可能性，同时减少了低风险患者中不必要的治疗，从而改善手术结果，最大限度地提高医疗保健效率，并最大限度地降低成本。在基于价值的护理模式中，这种模式使卫生系统、外科医生和患者的目标保持一致。该项目将个体患者数据与机器学习算法相结合，以有效预测手术并发症风险并改善手术临床结果。目前，在美国每年进行的5000万例外科手术中，有13%会导致手术并发症，其中一半是可以避免的。可避免并发症的主要原因包括风险评估的显著变量和标准化的预防措施。因此，本提案的主要目标是开发各种手术并发症（伤口、心脏、呼吸、肾脏等）的机器学习预测模型，为外科医生提供客观的风险评估。此外，该风险评估平台将允许将患者分层为高风险和低风险类别，并在护理点将患者与风险适当的预防干预措施联系起来。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is to create a personalized, precision-based practice paradigm for surgery that improves surgical outcomes and reduces the cost of healthcare. This paradigm utilizes individual patient characteristics with machine-learning algorithms to accurately predict the risk of post-surgical complications. Additionally, it offers the possibility of enacting impactful interventions among high-risk patients, while reducing unnecessary therapies among low-risk patients, thereby improving surgical outcomes, maximizing the efficiency of healthcare, and minimizing cost. In a value-based care model, this paradigm aligns the goals of health systems, surgeons, and patients.The proposed project combines individual patient data with machine-learning algorithms to effectively predict surgical complication risk and improve surgical clinical outcomes. Currently, 13% of 50 million surgical procedures performed in the United States annually result in a surgical complication, half which are potentially avoidable. A primary cause of avoidable complications include significant variable in risk assessment and standardized preventative practice. Therefore, the principal objective of this proposal is to develop machine-learning predictive models of various surgical complications (wound, cardiac, respiratory, renal, etc.), which provides an objective risk assessment for surgeons. Additionally, this risk assessment platform will allow stratification of patients into high- vs. low-risk categories and link patients with risk-appropriate preventative interventions at the point-of-care.