Bennett Tellen D, DeWitt Peter E, Dixon Rebecca R, Kartchner Cory, Sierra Yamila, Ladell Diane, Srivastava Rajendu, Riva-Cambrin Jay, Kempe Allison, Runyan Desmond K, Keenan Heather T, Dean J Michael
1Section of Pediatric Critical Care, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO. 2Children's Hospital Colorado, Aurora, CO. 3Adult and Child Consortium for Health Outcomes Research and Delivery Science (ACCORDS), University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, CO. 4Division of Pediatric Critical Care, Department of Pediatrics, Colorado School of Public Health, Aurora, CO. 5Pediatric Critical Care, University of Utah School of Medicine, Salt Lake City, UT. 6Department of Bioinformatics and Biostatistics, Primary Children's Hospital, Salt Lake City, UT. 7Division of Pediatric Inpatient Medicine, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, UT. 8Office of Research, Intermountain Healthcare, Salt Lake City, UT. 9Division of Pediatric Neurosurgery, Department of Clinical Neurosciences, University of Calgary and Alberta Children's Hospital, Calgary, AB, Canada. 10Department of Pediatrics, Kempe Center, University of Colorado School of Medicine, Aurora, CO.
Pediatr Crit Care Med. 2017 May;18(5):442-451. doi: 10.1097/PCC.0000000000001120.
To develop and validate case definitions (computable phenotypes) to accurately identify neurosurgical and critical care events in children with traumatic brain injury.
Prospective observational cohort study, May 2013 to September 2015.
Two large U.S. children's hospitals with level 1 Pediatric Trauma Centers.
One hundred seventy-four children less than 18 years old admitted to an ICU after traumatic brain injury.
Prospective data were linked to database codes for each patient. The outcomes were prospectively identified acute traumatic brain injury, intracranial pressure monitor placement, craniotomy or craniectomy, vascular catheter placement, invasive mechanical ventilation, and new gastrostomy tube or tracheostomy placement. Candidate predictors were database codes present in administrative, billing, or trauma registry data. For each clinical event, we developed and validated penalized regression and Boolean classifiers (models to identify clinical events that take database codes as predictors). We externally validated the best model for each clinical event. The primary model performance measure was accuracy, the percent of test patients correctly classified. The cohort included 174 children who required ICU admission after traumatic brain injury. Simple Boolean classifiers were greater than or equal to 94% accurate for seven of nine clinical diagnoses and events. For central venous catheter placement, no classifier achieved 90% accuracy. Classifier accuracy was dependent on available data fields. Five of nine classifiers were acceptably accurate using only administrative data but three required trauma registry fields and two required billing data.
In children with traumatic brain injury, computable phenotypes based on simple Boolean classifiers were highly accurate for most neurosurgical and critical care diagnoses and events. The computable phenotypes we developed and validated can be used in any observational study of children with traumatic brain injury and can reasonably be applied in studies of these interventions in other patient populations.
制定并验证病例定义（可计算表型），以准确识别创伤性脑损伤患儿的神经外科和重症监护事件。
前瞻性观察队列研究，2013年5月至2015年9月。
美国两家设有一级儿科创伤中心的大型儿童医院。
174名18岁以下创伤性脑损伤后入住重症监护病房的儿童。
前瞻性数据与每位患者的数据库编码相关联。前瞻性确定的结局包括急性创伤性脑损伤、颅内压监测器置入、开颅手术或颅骨切除术、血管导管置入、有创机械通气以及新的胃造口管或气管造口管置入。候选预测因素为行政、计费或创伤登记数据中存在的数据库编码。对于每一项临床事件，我们开发并验证了惩罚回归和布尔分类器（以数据库编码作为预测因素来识别临床事件的模型）。我们对每项临床事件的最佳模型进行了外部验证。主要的模型性能指标是准确性，即正确分类的测试患者百分比。该队列包括174名创伤性脑损伤后需要入住重症监护病房的儿童。对于九项临床诊断和事件中的七项，简单布尔分类器的准确率大于或等于94%。对于中心静脉导管置入，没有分类器达到90%的准确率。分类器的准确性取决于可用的数据字段。九项分类器中有五项仅使用行政数据时准确率可接受，但三项需要创伤登记字段，两项需要计费数据。
在创伤性脑损伤患儿中，基于简单布尔分类器的可计算表型对于大多数神经外科和重症监护诊断及事件具有高度准确性。我们开发并验证的可计算表型可用于任何创伤性脑损伤患儿的观察性研究，并可合理应用于其他患者群体的这些干预措施研究。
