Predicting Patient Instability Noninvasively for Nursing Care – Three (PPINNC-3)

以无创方式预测患者不稳定的护理 — 三 (PPINNC-3)

• 批准号: 10578789
• 负责人: Salah S Al-Zaiti
• 金额: $ 76.45万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-27 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10578789
• 关键词: Adverse event; Architecture; Beds; Benchmarking; California; Care given by nurses; Caring; Cessation of life; Classification; Clinical; Clinical Trials; Data; Data Set; Databases; Decision Support Systems; Detection; Deterioration; Diagnostic; Discipline of Nursing; Disparate; Electronic Health Record; Engineering; Evaluation; Event; Expert Systems; Failure; Fatigue; Frequencies; Funding; Future; Health; Healthcare; Heart; Hospitalization; Hospitals; Human; Intelligence; Intensive Care; Intensive Care Units; Intervention; Lead; Link; Logistics; Machine Learning; Methods; Modeling; Monitor; Morbidity - disease rate; Morphologic artifacts; Nurses; Patient-Focused Outcomes; Patients; Phenotype; Physiological; Predictive Value; Prevention; Process; Productivity; Public Health; Readiness; Recommendation; Risk; Risk Estimate; San Francisco; Series; Signal Transduction; Site; Source; System; Testing; Time; Translating; Triage; Universities; Validation; Work; care delivery; clinical research site; clinically actionable; clinically relevant; complex data; data streams; design; detection platform; digital; effectiveness trial; electronic health database; field study; graphical user interface; implementation barriers; improved; innovation; iterative design; lead optimization; machine learning algorithm; machine learning classifier; machine learning model; model building; mortality; novel strategies; patient safety; predictive modeling; prevent; prospective; prototype; risk prediction; signal processing; tool; treatment response; trend; usability; user centered design

PREDICTING PATIENT INSTABILITY NONINVASIVELY FOR NURSING CARE (PPINNC-3) Project Summary Timely recognition and forecasting of cardiorespiratory instability (CRI) in hospitalized patients in step-down units (SDU) has clear implications for strategies to reduce preventable morbidity and mortality. Continuous noninvasive vital signs (VS) monitoring is widely used to facilitate nurse detection of actionable events requiring a diagnostic or therapeutic response, yet failure-to-rescue rates in US hospitals, defined as death due to complications, remain high. Traditional VS monitoring alarms are largely based on numeric threshold exceedance, translating to very low true positive rates and adversely leading to alarm fatigue and reactive nursing care. We and others have demonstrated that decompensation evolves over time and featurization, trending, and phenotyping of multi-channel VS time series for building relevant models for forecasting CRI is feasible. Over the past two funding periods, we have built the largest multi-site database of EHR-linked, high fidelity VS monitoring data from SDU patients known to us. Using this multi-expert, multi-tier ground truth annotated database we have begun to build clinically relevant Machine Learning models to discriminate artifactual anomaly from real CRI with high accuracy, as well as to classify mild vs. serious CRI and forecast cases that require escalation of care or up transfer to intensive care. We now aim to move these extensive efforts to fruition and refine and build these models in the workflow for prospective validation and clinical deployment. The specific aims of this renewal application are: 1) build and deploy a real-time data streaming architecture at bedside for an intelligent alerting system, including the iterative design and usability testing of a clinician-facing graphical user interface; 2) build and externally validate a multi-layered alerting system forecasting CRI; and 3) perform a prospective validation of the intelligent alerting system, including silent deployment and evaluation at SDUs at UPITT and UCSF followed by prospective field testing at a 16-bed SDU at UPITT. The final deliverable is an intelligent alerting system for detection and mitigation of CRI in SDU patients of sufficient readiness to be deployed in a multicenter human effectiveness trial as a next step. Building such a clinically relevant system in clinical workflow to predict patient instability has important implications for reducing preventable morbidity and mortality, eliminating alarm fatigue, improving patient safety, nursing care logistics (monitoring frequency, case load and mixture, staff allocation) and care delivery systems (triage, bed allocation, prevention of adverse events).

非侵入性预测患者的生存能力以进行护理（PPINNC-3） 项目摘要 及时识别和预测住院患者降压过程中的心肺功能不稳定 单位（SDU）对减少可预防的发病率和死亡率的战略具有明确的意义。连续 非侵入性生命体征（VS）监测被广泛用于帮助护士检测可操作事件 需要诊断或治疗反应，但美国医院的抢救失败率，定义为死亡原因 并发症的比例仍然很高传统的VS监控报警主要基于数字阈值 服从，转化为非常低的真阳性率，并不利地导致警报疲劳和反应性 护理我们和其他人已经证明，失代偿随着时间的推移和特征化而发展， 趋势，和表型的多通道VS时间序列，用于建立预测CRI的相关模型， 可行在过去的两个资助期内，我们建立了最大的多站点电子健康记录数据库， 保真度与我们已知的SDU患者的监测数据。使用这个多专家、多层次的地面实况 我们已经开始建立临床相关的机器学习模型来区分 高精度地从真实的CRI中提取人为异常，以及对轻度CRI和严重CRI进行分类和预测 需要升级护理或向上转移到重症监护的病例。我们现在的目标是将这些广泛的 努力在工作流程中实现、完善和构建这些模型，以进行前瞻性验证和临床研究。 部署.该更新应用程序的具体目标是：1）构建和部署实时数据流 在床边的智能报警系统的架构，包括迭代设计和可用性测试， 面向临床医生的图形用户界面; 2）构建和外部验证多层警报系统 预测CRI;以及3）执行智能警报系统的预期验证，包括静音 在UPITT和UCSF的SDU部署和评估，然后在16个床位的SDU进行前瞻性现场测试 在UPITT。最后的交付成果是一个智能报警系统，用于检测和缓解SDU中的CRI 准备充分的患者可在多中心人体有效性试验中展开，作为下一步。 在临床工作流程中建立这样的临床相关系统以预测患者不稳定具有重要意义。 降低可预防的发病率和死亡率，消除警报疲劳，改善患者 安全、护理后勤（监测频率、病例负荷和混合、工作人员分配）和护理提供 系统（分诊、床位分配、不良事件预防）。