Development and Validation of an Empiric Tool to Predict Favorable Neurologic Outcomes Among PICU Patients
Development and Validation of an Empiric Tool to Predict Favorable Neurologic Outcomes Among PICU Patients
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DOI：
10.1097/ccm.0000000000002753
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发表时间：
2018-01-01
影响因子：
8.8
通讯作者：
Wetzel, Randall C.
中科院分区：
文献类型：
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作者：
Gupta, Punkaj;Rettiganti, Mallikarjuna;Wetzel, Randall C.
目的：建立一种新的工具来预测重症监护室期间的儿童重症疾病的良好神经功能预后。设计：采用自适应套索方法的Logistic回归模型来确定与良好神经功能预后相关的独立因素。使用混合效应逻辑回归模型创建最终预测模型，包括从套索模型中选择的所有预测因子。使用10倍内部交叉验证方法进行模型验证。设置：虚拟儿科系统（VPS，LLC，洛杉矶，CA）数据库。患者：包括在虚拟儿科系统数据库中的一个参与ICU中入院的小于18岁的患者（2009-2015）.干预措施：无.测量和主要结果：来自90家医院的160，570名患者符合纳入标准。其中，1，675例患者（1.04%）在ICU入院和ICU出院之间的儿科脑功能分类量表下降至少2分（不利的神经系统结局）。与不利的神经功能结局相关的独立因素包括ICU入院时体重较高、ICU入院时儿科道德指数-2评分较高、心脏骤停、卒中、癫痫发作、头部/非头部创伤、使用常规机械通气和高频振荡通气、ICU住院时间延长和机械通气使用时间延长。染色体异常、心脏手术和一氧化氮的应用与良好的神经功能结局相关。最终的在线预测工具可以在https://soipredictiontool.shinyapps.io/GNOScore/上访问。我们的模型在内部验证样本中预测了139，688例具有良好神经功能结局的患者，而观察到的具有良好神经功能结局的患者数量为139，591例。验证模型的受试者工作曲线下面积为0.90。结论：该预测工具将20个危险因素整合为一个概率，可以预测重症监护病房患儿的良好神经功能预后。未来的研究应寻求外部验证和改善这种预测工具的歧视。
Objectives: To create a novel tool to predict favorable neurologic outcomes during ICU stay among children with critical illness.Design: Logistic regression models using adaptive lasso methodology were used to identify independent factors associated with favorable neurologic outcomes. A mixed effects logistic regression model was used to create the final prediction model including all predictors selected from the lasso model. Model validation was performed using a 10-fold internal cross-validation approach.Setting: Virtual Pediatric Systems (VPS, LLC, Los Angeles, CA) database.Patients: Patients less than 18 years old admitted to one of the participating ICUs in the Virtual Pediatric Systems database were included (2009-2015).Interventions: None.Measurements and Main Results: A total of 160,570 patients from 90 hospitals qualified for inclusion. Of these, 1,675 patients (1.04%) were associated with a decline in Pediatric Cerebral Performance Category scale by at least 2 between ICU admission and ICU discharge (unfavorable neurologic outcome). The independent factors associated with unfavorable neurologic outcome included higher weight at ICU admission, higher Pediatric Index of Morality-2 score at ICU admission, cardiac arrest, stroke, seizures, head/nonhead trauma, use of conventional mechanical ventilation and high-frequency oscillatory ventilation, prolonged hospital length of ICU stay, and prolonged use of mechanical ventilation. The presence of chromosomal anomaly, cardiac surgery, and utilization of nitric oxide were associated with favorable neurologic outcome. The final online prediction tool can be accessed at https://soipredictiontool.shinyapps.io/GNOScore/. Our model predicted 139,688 patients with favorable neurologic outcomes in an internal validation sample when the observed number of patients with favorable neurologic outcomes was among 139,591 patients. The area under the receiver operating curve for the validation model was 0.90.Conclusions: This proposed prediction tool encompasses 20 risk factors into one probability to predict favorable neurologic outcome during ICU stay among children with critical illness. Future studies should seek external validation and improved discrimination of this prediction tool.