Gupta Punkaj, Rettiganti Mallikarjuna, Wilcox Andrew, Vuong-Dac Mai-Anh, Gossett Jeffrey M, Imamura Michiaki, Chakraborty Avishek
Division of Pediatric Cardiology, Department of Pediatrics, University of Arkansas for Medical Sciences, College of Medicine, Arkansas Children's Hospital, Little Rock, Arkansas.
Biostatistics Program, Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
Semin Thorac Cardiovasc Surg. 2018 Spring;30(1):62-68. doi: 10.1053/j.semtcvs.2018.01.003. Epub 2018 Jan 31.
We aimed to empirically derive an inotrope score to predict real-time outcomes using the doses of inotropes after pediatric cardiac surgery. The outcomes evaluated included in-hospital mortality, prolonged hospital length of stay, and composite poor outcome (mortality or prolonged hospital length of stay). The study population included patients <18 years of age undergoing heart operations (with or without cardiopulmonary bypass) of varying complexity. To create this novel pediatric cardiac inotrope score (PCIS), we collected the data on the highest doses of 4 commonly used inotropes (epinephrine, norepinephrine, dopamine, and milrinone) in the first 24 hours after heart operation. We employed a hierarchical framework by representing discrete probability models with continuous latent variables that depended on the dosage of drugs for a particular patient. We used Bayesian conditional probit regression to model the effects of the inotropes on the mean of the latent variables. We then used Markov chain Monte Carlo simulations for simulating posterior samples to create a score function for each of the study outcomes. The training dataset utilized 1030 patients to make the scientific model. An online calculator for the tool can be accessed at https://soipredictiontool.shinyapps.io/InotropeScoreApp. The newly proposed empiric PCIS demonstrated a high degree of discrimination for predicting study outcomes in children undergoing heart operations. The newly proposed empiric PCIS provides a novel measure to predict real-time outcomes using the doses of inotropes among children undergoing heart operations of varying complexity.
我们旨在通过小儿心脏手术后使用的血管活性药物剂量，凭经验得出一个血管活性药物评分，以预测实时预后。评估的预后指标包括住院死亡率、住院时间延长以及综合不良预后（死亡率或住院时间延长）。研究人群包括年龄小于18岁、接受不同复杂程度心脏手术（有或无体外循环）的患者。为了创建这个新的小儿心脏血管活性药物评分（PCIS），我们收集了心脏手术后最初24小时内4种常用血管活性药物（肾上腺素、去甲肾上腺素、多巴胺和米力农）的最高剂量数据。我们采用了一个分层框架，通过用依赖于特定患者药物剂量的连续潜在变量来表示离散概率模型。我们使用贝叶斯条件概率单位回归来模拟血管活性药物对潜在变量均值的影响。然后，我们使用马尔可夫链蒙特卡罗模拟来模拟后验样本，为每个研究预后创建一个评分函数。训练数据集使用了1030名患者来构建科学模型。可通过https://soipredictiontool.shinyapps.io/InotropeScoreApp访问该工具的在线计算器。新提出的经验性PCIS在预测接受心脏手术儿童的研究预后方面显示出高度的区分度。新提出的经验性PCIS提供了一种新的方法，可利用不同复杂程度心脏手术儿童使用血管活性药物的剂量来预测实时预后。
