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.

项目总结/摘要：如果能够准确预测谁，何时以及为什么患者会发展 心肺不稳定（CRI），则可给予有效的预防性治疗以改善预后， 更好地利用医疗资源。然而，CRI通常不被识别，直到它被很好地建立并且患者被 更难治疗或进展为器官损伤。我们已经证明， 从连续非侵入性获取的监测参数获得并与护理相耦合的系统警报 算法改善了降压单位（SDU）患者结局。我们还发现， 分析（样本熵）在2分钟内确定SDU患者存在CRI风险，如果监测5分钟， 在接下来的48小时内，将发生CRI或保持稳定的患者区分开来。我们也 将机器学习（ML）建模应用于我们临床相关的出血性休克猪模型， 描述对低血容量、出血和复苏的反应，预测哪些动物会或将 在血容量不足时不会虚脱，并且比传统监测提前5分钟发现隐匿性出血。 我们现在建议将我们的工作应用于脆弱和侵入性监测的ICU患者。我们将开发 多变量模型通过ML数据驱动的分类技术，如回归，傅立叶和 主成分分析，人工神经网络，随机森林分类等，以及更新颖的 方法（由我们的团队开发的时间规则学习;贝叶斯聚合）来预测ICU中的CRI 患者我们将首先使用我们现有的注释高保真波形MIMIC II临床数据集（4200 患者）开发用于各种CRI驱动因素的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（伯努利），以前瞻性地收集来自三个ICU的数据 研究机构：匹兹堡大学（PITT）、加州大学（UC）欧文分校和加州大学圣地亚哥分校（初始算法 在PITT进行开发并在UC系统中验证）。我们将确定的数量和类型 独立的措施，采样频率和必要的前置时间，以创建强大的算法：1）预测 即将到来的CRI，2）选择最有效的治疗，3）监测治疗反应，4）确定何时 治疗已经恢复了生理稳定性并且可以停止。我们还将确定最小数量 与各类参数相耦合的CRI提前期最长，达到上述四个指标的最佳 灵敏度和特异性（我们称之为监控简约性的概念）。我们将同时迭代设计 并测试图形用户界面（GUI）和临床决策支持系统（CDSS）， 简约衍生的预测性智能警报和功能性血流动力学监测治疗方法 在两个人类模拟环境（PITT和加州大学欧文分校）。我们设想一个基本的监测监视， 确定最有可能发生CRI的患者，以集中临床医生的注意力和针对性治疗， 提供高度个性化的医疗服务。