Machine learning of physiological variables to predict diagnose and treat cardiorespiratory instability

机器学习生理变量来预测诊断和治疗心肺不稳定

• 批准号: 9029396
• 负责人: MICHAEL R PINSKY
• 金额: $ 66.25万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9029396
• 关键词: Accounting; Acute; Algorithms; Animals; Attention; Biological Neural Networks; California; Cardiovascular system; Caring; Classification; Clinical; Clinical Data; Clinical Decision Support Systems; Clinical Treatment; Complex; Coupled; Critical Illness; Data; Data Collection; Data Set; Development; Diagnosis; Disease; Effectiveness; Electronic Health Record; Engineering; Entropy; Environment; Etiology; Family suidae; Frequencies; Future; Health; Healthcare; Hemorrhage; Hemorrhagic Shock; Homeostasis; Hour; Human; Hypovolemia; Individual; Injury; Institution; Intensive Care Units; Intervention; Lead; Learning; Libraries; Machine Learning; Measures; Mechanical ventilation; Medical; Medical center; Modeling; Monitor; Normal Range; Organ; Organ failure; Pathologic Processes; Patient Monitoring; Patient-Focused Outcomes; Patients; Pattern; Physiologic Monitoring; Physiological; Principal Component Analysis; Process; Public Health; Recommendation; Refractory; Resolution; Resources; Resuscitation; Risk; Running; Sampling; Sensitivity and Specificity; Sepsis; Shock; Signal Transduction; Specificity; Stream; Stress; System; Techniques; Testing; Time; Trauma; Triage; Universities; Validation; Variant; Weaning; Work; abstracting; base; clinical care; clinically relevant; computerized data processing; cost; database structure; density; design; diagnostic accuracy; early onset; effective therapy; fitness; forest; graphical user interface; hemodynamics; high risk; improved; improved outcome; insight; iterative design; mortality; novel strategies; patient population; personalized medicine; predictive modeling; predictive tools; prospective; prototype; response; simulation; support tools; treatment response

Project Summary/Abstract: If one could accurately predict who, when and why patients develop cardiorespiratory instability (CRI), then effective preemptive treatments could be given to improve outcome and better use care resources. However, CRI is often unrecognized until it is well established and patients are more refractory to treatment, or progressed to organ injury. We have shown that an integrated monitoring system alert obtained from continuous noninvasively acquired monitoring parameters and coupled to a care algorithm improved step-down unit (SDU) patient outcomes. We also showed that advanced HR variability analysis (sample entropy) identified SDU patients at CRI risk within 2 minutes, and if monitored for 5 minutes differentiated between patients who would develop CRI or remain stable over the next 48 hours. We also applied machine learning (ML) modeling to our clinically-relevant porcine model of hemorrhagic shock to characterize responses to hypovolemia, hemorrhage, and resuscitation, predict which animals would or would not collapse during hypovolemia, and identify occult bleeding 5 minutes earlier than with traditional monitoring. We now propose to apply our work to vulnerable and invasively monitored ICU patients. We will develop multivariable models through ML data-driven classification techniques such as regression, Fourier and principal component analysis, artificial neural networks, random forest classification, etc. as well as more novel approaches (temporal rule learning developed by our team; Bayesian Aggregation) to predict CRI in ICU patients. We will first use our existing annotated high fidelity waveform MIMIC II clinical data set (4200 patients) to develop predictive models and differential signatures for various CRI drivers. We will also use our high-density data collection and processing platform (Bernoulli) to prospectively collect data from ICUs in three institutions: Univ. Pittsburgh (PITT), Univ. California (UC) Irvine and UC San Diego (initial algorithm development conducted at PITT and validated in the UC systems). We will identify the number and type of independent measures, sampling frequency, and lead time necessary to create robust algorithms to: 1) predict impending CRI, 2) select the most effective treatments, 3) monitor treatment response, and 4) determine when treatment has restored physiologic stability and can be stopped. We will also determine the smallest number and types of parameters coupled to the longest CRI lead time to achieve the above four targets with the best sensitivity and specificity (a concept we call Monitoring Parsimony).We will simultaneously iteratively design and test a graphical user interface (GUI) and clinical decision support system (CDSS) driven by these parsimoniously derived predictive smart alerts and functional hemodynamic monitoring treatment approaches in two human simulation environments (PITT & UC Irvine).We envision a basic monitoring surveillance that identifies patients most likely to develop CRI to apply focused clinician attention and targeted treatments to deliver highly personalized medical care.

项目摘要/摘要：如果可以准确预测患者何时以及为什么发展 心肺不稳定（CRI），然后可以进行有效的先发制人治疗以改善结果和 更好地使用护理资源。但是，CRI通常无法识别直到确定为止，并且患者是 对治疗的难治性更大，或者发展为器官损伤。我们已经证明了一个集成的监视 从连续无创获的监视参数获得的系统警报，并耦合到护理 算法改善了降级单元（SDU）患者结果。我们还表明了高级人力资源变异性 分析（样品熵）在2分钟内确定了CRI风险的SDU患者，如果监测5分钟 在接下来的48小时内会区分患者或将在接下来的48小时内保持稳定的患者。我们也是 应用机器学习（ML）建模到我们临床上与临床相关的出血性休克猪模型 表征对低血容量，出血和复苏的反应，预测哪些动物会或会 低血症期间不会崩溃，并且比传统监测更早地识别出5分钟的隐匿性出血。 现在，我们建议将我们的工作应用于易受伤害和受侵入性的ICU患者。我们将发展 通过ML数据驱动的分类技术，例如回归，傅立叶和 主要成分分析，人工神经网络，随机森林分类等以及更多新颖 方法（我们的团队开发的时间规则学习；贝叶斯聚集）来预测ICU中的CRI 患者。我们将首先使用我们现有的注释高富达波形模仿II临床数据集（4200 患者）为各种CRI驱动器开发预测模型和差异特征。我们还将使用我们的 高密度数据收集和处理平台（Bernoulli），可前瞻性地从ICU收集三个 机构：大学。匹兹堡（皮特），大学。加利福尼亚州（UC）Irvine和UC圣地亚哥（初始算法 在Pitt进行的开发并在UC系统中进行了验证。我们将确定的数量和类型 独立测量，采样频率和提前时间创建可靠算法的必要时间：1）预测 即将发生的CRI，2）选择最有效的治疗方法，3）监测治疗反应，4）确定何时 治疗恢复了生理稳定性，可以停止。我们还将确定最小的数字 与最长的CRI交货时间相连的参数类型，以最佳实现上述四个目标 敏感性和特异性（我们称之为监控的概念）。我们将同时迭代设计 并测试由这些驱动的图形用户界面（GUI）和临床决策支持系统（CDSS） 衍生的预测性智能警报和功能性血液动力学监测治疗方法 在两个人类的模拟环境中（Pitt＆UC Irvine）。 确定最有可能发展CRI的患者以将重点的临床医生注意和针对性的治疗应用于 提供高度个性化的医疗服务。