SCH: INT: Anesthesiology Control Tower: Forecasting Algorithms to Support Treatment (ACTFAST)

在美国，手术后的重大并发症和死亡是一个重大的公共卫生问题。每年有4000万美国人接受手术。大约百分之五的人在手术后一年内死亡，大约百分之十的人患有严重的住院疾病（例如，中风、心脏病发作、肺炎、肾衰竭、伤口感染）。早期识别风险和适当的管理往往可以预防或改变这些不良后果。我们认为这是一个令人兴奋的科学机会。现代术中监测产生了来自美国数千个手术室的丰富数据。 这些数据流的集成和实时分析有可能彻底改变围手术期护理。我们建议开发，验证和评估机器学习，预测算法，预测个别患者的不良结果。预测算法将基于来自各种来源的数据，包括患者的电子医疗记录，手术室中的生理监测器阵列和循证科学文献。这些算法将促进患者特定的临床决策支持，利用麻醉控制塔的创新方法。手术室的麻醉控制塔在概念上类似于忙碌机场的空中交通控制塔。我们乐观地认为，为单个手术患者开发预测算法的宏伟目标可以实现，因为我们团队的成员已经为重症医院患者开发并验证了类似的预测算法。不良事件的可改变的风险因素可以在手术过程中早期检测到，从而允许有针对性的干预和预防措施。ACTFAST研究将提供关于将预测算法纳入常规外科护理的潜在效用的重要信息，包括在资源不足的医疗保健环境中。该项目还将产生重要的教育效益。对于帮助开发和验证预测算法的学生来说，将有大量的学习。此外，控制塔的概念是一种颠覆性的教育创新，它将使麻醉学学员具备同时为多名手术患者提供护理的新能力。众所周知，为人类个体构建高保真的科学模型是非常困难的，因为我们是复杂的生物系统。最终，这个雄心勃勃的项目的成功，它涉及跨学科的观点，并将复杂的预测算法应用于临床决策支持，将产生重大的科学和临床影响。现代术中监测产生了来自美国数千个手术室的丰富数据。 这些数据流的集成和实时分析有可能彻底改变围手术期护理。这项研究的目的是利用我们在运行创新机器学习算法方面的经验，包括过滤和结果相关模型，以构建针对个体手术患者的预测算法。我们的中心假设是，充分了解患者特征，再加上术中电子病历中重复的高保真时间序列数据，可以为个体患者构建高级模型，预测术后不良结局的风险。首先，使用训练数据集，我们将应用我们之前验证过的数据挖掘和机器学习方法，从电子健康记录和实时生理变量中提取有用的信息。我们将开发使用混合学习技术的算法，以联合收割机非参数（生成）模型（如直方图和核密度估计）和参数（判别）模型（如支持向量机，逻辑回归和核机器）的优势，以改善对不良围手术期结局的预测。目标是提供上级预测质量，具有良好的可解释性和高计算效率，支持大数据的快速处理。其次，使用测试数据集，我们将通过确定预测不良结果的可靠性来验证所开发算法的预测准确性。将测试所开发的算法的预测性能的准确性。这些评价方法包括保留抽样、交叉验证和自助抽样。在经过培训和测试后，将使用准确度、精密度和稳健性的标准指标对开发的算法的性能进行额外的前瞻性验证（样本外性能）。我们设想，所开发的算法将促进患者特定的临床决策支持，利用麻醉控制塔，这将是一个概念上类似于一个忙碌机场的空中交通管制塔的创新方法。该项目的主要贡献将包括：（1）新的机器学习算法，用于从异质、多尺度和高维数据流中预测围手术期不良事件;（2）临床决策支持系统，用于识别预后因素并基于新的特征排序算法提出干预措施;以及（3）麻醉团队教育和围手术期护理范例的变革方法。成功地推进实时分析方法和建模的个别手术患者，使用异构和稀疏的数据，将有巨大的科学和社会影响。

There is a significant public health concern in the United States regarding major complications and death following surgery. Forty million Americans undergo surgery yearly. Approximately five percent die within a year of their operation, and roughly ten percent suffer major in-hospital morbidity (e.g., stroke, heart attack, pneumonia, renal failure, wound infection). Early recognition of risk and appropriate management could often prevent or modify these adverse outcomes. We believe that this represents an exciting scientific opportunity. Modern intraoperative monitoring yields a wealth of data from thousands of operating rooms across the US. Integration and real-time analysis of these data streams has the potential to revolutionize perioperative care. We propose to develop, validate and assess machine-learning, forecasting algorithms that predict adverse outcomes for individual patients. The forecasting algorithms will be based on data derived from various sources, including the patient's electronic medical record, the array of physiological monitors in the operating room, and evidence-based scientific literature. These algorithms will facilitate patient-specific clinical decision support, utilizing an innovative approach of an anesthesiology control tower. The anesthesiology control tower for the operating room will be conceptually similar to an air traffic control tower for a busy airport. We are optimistic that the ambitious goal of developing forecasting algorithms for individual surgical patients can be realized, since members of our team have already developed and validated similar forecasting algorithms for critically ill hospital patients. Modifiable risk factors for adverse events could be detected early during surgery, allowing targeted interventions and preventive measures. The ACTFAST study will provide important information on the potential utility of incorporating forecasting algorithms into routine surgical care, including in under-resourced healthcare settings. This project will also yield important educational benefits. There will be tremendous learning for the students who help to develop and validate the forecasting algorithms. Furthermore, the control tower concept is a disruptive educational innovation, which will equip anesthesiology trainees with a new ability to provide simultaneous care to multiple surgical patients. It is notoriously difficult to construct high fidelity scientific models for individual humans, as we are complex biological systems. Ultimately, the success of this ambitious project, which engages interdisciplinary perspectives and applies sophisticated forecasting algorithms to clinical decision support, will have substantial scientific and clinical impact.Modern intraoperative monitoring yields a wealth of data from thousands of operating rooms across the United States. Integration and real-time analysis of these data streams has the potential to revolutionize perioperative care. The objective for this investigation is to exploit our experience in running innovative machine learning algorithms, including filtering and outcome-related models, in order to build forecasting algorithms tailored to individual surgical patients. Our central hypothesis is that with sufficient knowledge of patient characteristics coupled with repeated, high-fidelity time series data from the intraoperative electronic medical record, advanced models can be constructed for individual patients that will forecast risk for adverse postoperative outcomes. First, using a training dataset, we will apply data mining and machine-learning methods that we have previously validated to extract useful information from electronic health records and real-time physiological variables. We will develop algorithms using hybrid-learning techniques to combine the strength of non-parametric (generative) models, such as histogram and kernel density estimation, and parametric (discriminative) models, such as support vector machines, logistic regressions, and kernel machines to improve predictions of adverse perioperative outcomes. The goal is to deliver superior prediction quality, with good interpretability and high computational efficiency, that supports fast processing of big data. Second, using a testing dataset, we will validate the predictive accuracy of the developed algorithms, by determining the reliability in forecasting adverse outcomes. The developed algorithms will be tested for accuracy of their predictive performance. These evaluation methods include hold-out sampling, cross-validation, and bootstrap sampling. After being trained and tested, the performance of the developed algorithms will be additionally validated prospectively (out-of-sample performance), using standard measures of accuracy, precision and robustness. We envision that the developed algorithms will facilitate patient-specific clinical decision support, utilizing an innovative approach of an anesthesiology control tower, which will be conceptually similar to an air traffic control tower for a busy airport. The main contributions of this project will include: (1) new machine-learning algorithms for forecasting perioperative adverse events from heterogeneous, multi-scale, and high-dimensional data streams; (2) a clinical decision support system that identifies prognostic factors and suggests interventions based on novel feature ranking algorithms; and (3) a transformative approach to the education of the anesthesiology team and the paradigm of perioperative care. Successfully advancing real-time analytic methods and modeling for individual surgical patients, using heterogeneous and sparse data, would have tremendous scientific and societal impact.