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

在现代儿科重症监护病房（PICU）中，随着死亡率持续下降，重点已转向降低神经系统发病率的措施。神经系统并发症在PICU中很难检测，因为儿童由于疾病的严重性而经常接受镇静和/或神经肌肉阻滞。早期识别和实施循证治疗策略对降低神经系统疾病发病率至关重要。传统的神经监测方法（计算机断层扫描[CT]、磁共振成像[MRI]、脑电图[EEG]）实际上不能用于常规筛查目的。我们认为，生物数学模型集成生物标志物和临床数据可能是一个重要的工具，在PICU神经系统并发症的检测。这种策略可以快速识别神经系统并发症并进行早期干预，最终降低发病率和死亡率。在这个指导性的以患者为导向的研究职业发展奖中，我们将尝试使用共同赞助商MGM-Learn（混合图形模型学习）开发的新算法开发混合图形模型，该算法具有处理连续和离散变量的独特能力。将纳入228名在UPMC匹兹堡儿童医院PICU住院的诊断不同的儿童。血清生物标志物（髓鞘碱性蛋白[MBP]、S100 B、脑源性神经营养因子[BDNF]和胶质细胞酸性蛋白[GFAP]）将与从电子健康记录获得的临床和实验室变量结合使用，通过混合图形模型中的综合分析，预测入院时不存在的神经系统并发症的急性发展（如癫痫、卒中、出血、脑病）和发病率（例如功能状态量表（FSS）、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，大大提高我在神经生物标志物临床应用方面的技能，以及有效的领导和管理，以过渡到一个成功的独立研究者。它将为我的R 01提供初步数据，早期预警神经生物传感器系统的实施，通过使用不断填充最新生物标志物和临床数据变量的混合图形模型，进入临床实践，以随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和及时执行治疗策略以防止不可逆脑损伤来评估其降低神经系统发病率的能力。

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.