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

在现代儿科重症监护病房(PICU)，随着死亡率的持续下降，重点已转移到降低神经发病率的措施上。在PICU中很难检测到神经系统并发症，因为儿童通常会因为病情的严重性而接受镇静和/或神经肌肉阻滞。及早确定和实施循证治疗策略对减少神经系统发病率至关重要。传统的神经监测方法(计算机断层扫描[CT]、磁共振成像[MRI]、脑电成像[EEG])实际上不能用于常规筛查目的。我们认为，结合生物标记物和临床数据的生物数学模型可能是检测PICU神经并发症的重要工具。这一策略可以快速识别神经系统并发症，并及早进行干预，最终降低发病率和死亡率。在这个以患者为导向的指导研究职业发展奖中，我们将尝试使用由共同赞助商开发的一种新算法MGM-LEARN(混合图形模型学习)来开发混合图形模型，该算法具有处理连续和离散变量的独特能力。在匹兹堡大学匹兹堡儿童医院的PICU将招收228名诊断不同的儿童。血清生物标记物(髓鞘碱性蛋白[MBP]、S100B、脑源性神经营养因子[BDNF]和胶质纤维酸性蛋白[GFAP])在预测颅脑损伤或心脏骤停等神经损伤后的预后方面有希望，将与从电子健康记录获得的临床和实验室变量一起使用，通过混合图形模型的综合分析来预测入院时未出现的神经系统并发症(例如癫痫、中风、出血、脑病)和发病率(例如功能状态量表(FSS))的急性发展。出院时和危重疾病后6个月的儿科生活质量量表(PedsQL)。这个K23奖项将为我提供混合图形建模方面的深入培训，极大地提高我在神经生物标记物临床应用方面的技能，以及有效的领导和管理，以过渡到一名成功的独立研究人员。它将为My R01提供初步数据，通过使用混合图形模型不断填充最新的生物标志物和临床数据变量，实现早期预警神经生物传感器系统，并将其应用于临床实践，以随时检测神经并发症；以及评估其通过及早识别神经并发症和及时执行治疗策略以防止不可逆转的脑损伤来降低神经发病率的能力。

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.