Mixed graphical models for the prediction of neurological morbidity in the PICU

用于预测 PICU 神经发病率的混合图形模型

• 批准号: 10178124
• 负责人: Alicia K Au
• 金额: $ 18.98万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10178124
• 关键词: Acute; Address; Admission activity; Adult; Age; Algorithms; Biochemical; Biological Markers; Biosensor; Brain; Brain Injuries; Brain-Derived Neurotrophic Factor; Caring; Cell Death; Child; Childhood; Classification; Clinical; Clinical Data; Complication; Critical Illness; Data; Delirium; Detection; Development; Diagnosis; Diagnostic; Early Diagnosis; Early Intervention; Early identification; Electronic Health Record; Encephalopathies; Enrollment; Equipment and supply inventories; Event; Evidence based treatment; Failure; Glial Fibrillary Acidic Protein; Graph; Heart Arrest; Heart Rate; Hemorrhage; Induction of neuromuscular blockade; Injury; Kinetics; Laboratories; Leadership; Learning; Learning Skill; Measures; Mentored Patient-Oriented Research Career Development Award; Methods; Modeling; Modernization; Monitor; Morbidity - disease rate; Myelin Basic Proteins; Nervous System Trauma; Neurologic; Outcome; Pathway interactions; Patients; Pediatric Hospitals; Pediatric Intensive Care Units; Pediatrics; Population; Populations at Risk; Process; Prognosis; Quality of life; Receiver Operating Characteristics; Research; Research Personnel; Resuscitation; Risk; Sedation procedure; Seizures; Serum; Severities; Source; Stroke; Subgroup; System; Techniques; Time; Training; Traumatic Brain Injury; X-Ray Computed Tomography; biomarker development; biomarker signature; biomathematics; clinical application; clinical practice; cohort; discrete data; feature selection; functional status; learning algorithm; magnetic resonance imaging/electroencephalography; mortality; novel; predictive modeling; prevent; primary outcome; prognostic; routine screening; secondary outcome; skills; specific biomarkers; tool; treatment strategy

In the modern pediatric intensive care unit (PICU), as mortality rates continue to decline, focus has shifted towards measures to decrease neurological morbidity. Neurological complications can be difficult to detect in the PICU as children oftentimes receive sedation and/or neuromuscular blockade due to the severity of their illnesses. Early identification and implementation of evidence-based treatment strategies is paramount to the reduction of neurological morbidity. Traditional methods of neuro-monitoring (computed tomography [CT], magnetic resonance imaging [MRI], electroencephalography [EEG]) cannot be practically utilized for routine screening purposes. We believe that biomathematical models integrating biomarkers and clinical data may represent an important tool for the detection of neurological complications in the PICU. This strategy may allow for rapid identification of neurologic complications and earlier intervention to ultimately reduce morbidity and mortality. In this mentored patient-oriented research career development award we will attempt to develop mixed graphical models using a novel algorithm developed by the co-sponsor, MGM-Learn (Mixed Graphical Model Learning), which has the unique capability of processing continuous and discrete variables. Two hundred and twenty-eight diagnostically diverse children admitted to the PICU at Children's Hospital of Pittsburgh of UPMC will be enrolled. Serum biomarkers (myelin basic protein [MBP], S100B, brain derived neurotrophic factor [BDNF], and glial fibrillary acidic protein [GFAP]) that have shown promise in prognostication of outcome after neurological injuries such as traumatic brain injury or cardiac arrest will be used in conjunction with clinical and laboratory variables obtained from the electronic health record, through integrative analysis in mixed graphical models to predict acute development of neurological complications that were not present at the time of admission (e.g. seizure, stroke, hemorrhage, encephalopathy) and morbidity (e.g. Functional Status Scale (FSS), Pediatric Quality of Life Inventory (PedsQL)) at discharge and 6 months following critical illness. This K23 award will provide me with in-depth training in mixed graphical modeling, greatly enhance my skills in the clinical application of neuro-biomarkers and effective leadership and management to transition to a successful independent investigator. It will provide preliminary data for my R01, the implementation of an early warning neuro-biosensor system, through the use of mixed graphical models that continually populates with the most up-to-date biomarker and clinical data variables, into clinical practice to detect neurological complications at a moment to moment basis; and the assessment of its ability to reduce neurologic morbidity through early recognition of neurological complications and timely execution of treatment strategies to prevent irreversible brain damage.

在现代的小儿重症监护病房（PICU）中，随着死亡率继续下降，重点已转向降低神经系统发病率的措施。由于患病的严重程度，儿童通常会受到镇静和/或神经肌肉阻滞，因此在PICU中可能很难发现神经系统并发症。早期鉴定和实施基于证据的治疗策略对于降低神经系统发病率至关重要。传统的神经照明方法（计算机断层扫描[CT]，磁共振成像[MRI]，脑电图[EEG]）实际上不能用于常规筛选目的。我们认为，整合生物标志物和临床数据的生物学模型可能是检测PICU神经系统并发症的重要工具。该策略可以快速鉴定神经系统并发症和更早的干预措施，以最终降低发病率和死亡率。在这个受过指导的患者研究职业发展奖中，我们将尝试使用由共同发起人MGM-Learn（混合图形模型学习）开发的新型算法开发混合图形模型，该算法具有连续和离散变量的独特能力。将招募228名诊断出多样化的儿童，这些儿童将被录取在UPMC匹兹堡儿童医院的PICU。血清生物标志物（髓磷脂碱性蛋白[MBP]，S100B，脑衍生的神经营养因子[BDNF]和神经胶质纤维纤维酸性蛋白[GFAP]）在通过跨性别或心脏损伤中使用诸如康科疗法的临床降临型变异后，在预后表现出了在预后的预后，这些预后会在康置的临床上使用，并将其用于康科疗法。在混合图形模型中，预测入院时不存在神经系统并发症的急性发展（例如，癫痫发作，中风，出血，脑病，脑病）和发病率（例如功能状态量表（FSS），生命质量库存（PEDSQL）和6个月后的6个月后，在出院和6个月后。这项K23奖将为我提供混合图形建模的深入培训，从而极大地提高了我在神经生物标志物的临床应用方面的技能，以及有效的领导力和管理，以过渡到成功的独立研究者。它将通过使用混合图形模型来为我的R01，即预警神经生物传感器系统的实施提供初步数据，这些模型不断地以最新的生物标志物和临床数据变量持续填充，以临时检测神经系统并发症；以及通过早期识别神经系统并发症以及及时执行治疗策略以防止不可逆的脑损伤来降低神经系统发病率的能力。