Sepsis Physiomarkers for Appropriate Risk Knowledge of monitored patients in the ICU (SPARK-ICU)

用于对 ICU 中受监测患者进行适当风险了解的脓毒症生理标志物 (SPARK-ICU)

• 批准号: 10655628
• 负责人: Rishikesan Kamaleswaran
• 金额: $ 51.84万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10655628
• 关键词: Acute; Admission activity; Adult; Algorithms; Blood Pressure; Bolus Infusion; Cessation of life; Characteristics; Clinical; Clinical Data; Complex; Computerized Medical Record; Critical Illness; Data; Data Sources; Deterioration; Development; Effectiveness of Interventions; Electrocardiogram; Electrophysiology (science); Encapsulated; Face; Fluid Balance; Fluid Therapy; Functional disorder; Funding; Futility; General Ward; Goals; Health; Heterogeneity; Hospital Mortality; Hospitalization; Hospitals; Hour; Human; Institution; Intensive Care Units; Intervention; Knowledge; Length of Stay; Literature; Machine Learning; Manuals; Mathematics; Measures; Medicare; Medicine; Methods; Modeling; Morbidity - disease rate; Multiple Organ Failure; Organ; Outcome; Patient Admission; Patient Monitoring; Patients; Phenotype; Physiological; Physiology; Population; Recovery; Reporting; Reproducibility; Resources; Risk; Role; Sepsis; Severities; Shock; Signal Transduction; Stream; Structure; Testing; Time; Training; Translations; Treatment Efficacy; Universities; Vulnerable Populations; Work; clinical decision-making; clinical phenotype; clinical practice; computer science; data streams; day length; differential expression; hemodynamics; high risk; improved; improved outcome; insight; knowledge integration; learning algorithm; machine learning algorithm; machine learning method; machine learning prediction; machine learning prediction algorithm; mortality; mortality risk; novel; novel marker; pediatric sepsis; point of care; predictive modeling; programs; secondary infection; sensor; septic patients; signal processing; structured data; targeted treatment; tool

Project Summary Critically ill patients admitted to the ICU who develop secondary infection and sepsis, can face up to a five-fold increase in the risk for death when compared to non-sepsis patients. The majority of patients who developed secondary infections are more critically ill at admission and therefore require significantly greater resources. Traditional machine learning algorithms for predicting sepsis has been largely focused and relied on the use of structured data from the electronic medical record (EMR), however the EMR was developed largely as a billing mechanism and an audit log for clinical workflow. Hence, much of the structure and availability of data are often time-delayed, prone to errors from manual entry, biases from various institutional, personal and training biases, and finally contain a significant amount of missing data. In this proposal, we seek to discover novel `physiomarkers' extracted from continuous physiological data streams, generated from non-human derived data sources, that predict the onset of sepsis in this critical population. Using such routinely collected data, along with common clinical indicators extracted from the EMR, we propose to generate robust machine learning algorithms that can be more generalized, reproducible and removed from the biases and pitfalls of manual data entry. We propose that such classes of models not only may alert clinicians to acute and critically ill patients at risk for developing sepsis in real-time, but also investigate intervention effectiveness, such as volume responsiveness and support the discovery of novel sub-types of sepsis. Secondly, much of the existing literature on predictive models for sepsis focus on hospitalized patients in the general ward, however, models that predict the onset of sepsis among patients who developed secondary infections after admission to the ICU is limited. In our previous work, we have demonstrated that markers discovered from continuous numeric data streams can inform earlier prediction of sepsis in children and adults. However, those analysis did not use high-fidelity data from the waveforms, which encapsulate rich characteristics of physiology. Therefore, by emphasizing the discovery of such novel markers and through the application of data-driven learning algorithms, we expect to develop algorithms and tools that improve our understanding of the changing physiologic dynamics of sepsis in critically ill patient. In this proposed program, we will integrate knowledge across a number of distinctive expertise that spans signal processing, mathematics, computer science and medicine to develop sophisticated tools that can analyze such data to reveal meaningful insight. In short, we will contribute significant knowledge about the role and utility of complex physiological interactions that are at present abundantly available in clinical practice but seldom used for clinical decision making.

项目摘要 患病患者患有患病的患者，患有继发性感染和败血症的ICU，最多可以面对五倍 与非七居患者相比，死亡风险增加。大多数发育的患者 次要感染在入院时更为严重，因此需要更大的资源。 用于预测脓毒症的传统机器学习算法主要集中于 来自电子病历（EMR）的结构化数据，但是EMR主要是作为计费开发的 机制和临床工作流程的审核日志。因此，数据的大部分结构和可用性通常是 延期的，容易出现手动输入中的错误，来自各种机构，个人和培训偏见的偏见， 最后包含大量丢失的数据。在此提案中，我们试图发现小说 从非人类衍生数据产生的连续生理数据流中提取的“物理标志物” 来源，可以预测这一关键人群中败血症的发作。使用此类常规收集的数据以及 从EMR提取的常见临床指标，我们建议生成强大的机器学习算法 这可以更概括，可再现并从手动数据输入的偏见和陷阱中删除。我们 建议这样一类模型不仅会提醒临床医生急性和重病患者有风险 实时开发败血症，同时还要研究干预效果，例如体积响应能力 并支持发现败血症的新型子类型。其次，关于预测性的许多现有文献 但是 入院ICU后发生继发感染的患者中的败血症是有限的。在我们的上一个 工作，我们已经证明了从连续数字数据流中发现的标记可以早期告知 在儿童和成人中预测败血症。但是，这些分析没有使用来自 波形，它封装了生理的丰富特征。因此，通过强调发现 这种新颖的标记以及通过数据驱动的学习算法的应用，我们希望开发 算法和工具，可以提高我们对败血症不断变化的生理动态的理解 病患者。在此拟议的计划中，我们将纳入许多独特的专业知识的知识 跨越信号处理，数学，计算机科学和医学，以开发可以使用的复杂工具 分析此类数据以揭示有意义的见解。简而言之，我们将为角色提供重要的知识 目前在临床实践中大量可用的复杂生理相互作用的效用 很少用于临床决策。