Department of Cardiovascular Surgery-Pediatric Cardiac Unit, Heart Institute of University of São Paulo Medical School-HCFMUSP-InCor, São Paulo, Brazil.
Fundação Armando Alvares Penteado-FAAP, São Paulo, Brazil.
PLoS One. 2020 Sep 4;15(9):e0238199. doi: 10.1371/journal.pone.0238199. eCollection 2020.
Congenital heart disease accounts for almost a third of all major congenital anomalies. Congenital heart defects have a significant impact on morbidity, mortality and health costs for children and adults. Research regarding the risk of pre-surgical mortality is scarce.
Our goal is to generate a predictive model calculator adapted to the regional reality focused on individual mortality prediction among patients with congenital heart disease undergoing cardiac surgery.
Two thousand two hundred forty CHD consecutive patients' data from InCor's heart surgery program was used to develop and validate the preoperative risk-of-death prediction model of congenital patients undergoing heart surgery. There were six artificial intelligence models most cited in medical references used in this study: Multilayer Perceptron (MLP), Random Forest (RF), Extra Trees (ET), Stochastic Gradient Boosting (SGB), Ada Boost Classification (ABC) and Bag Decision Trees (BDT).
The top performing areas under the curve were achieved using Random Forest (0.902). Most influential predictors included previous admission to ICU, diagnostic group, patient's height, hypoplastic left heart syndrome, body mass, arterial oxygen saturation, and pulmonary atresia. These combined predictor variables represent 67.8% of importance for the risk of mortality in the Random Forest algorithm.
The representativeness of "hospital death" is greater in patients up to 66 cm in height and body mass index below 13.0 for InCor's patients. The proportion of "hospital death" declines with the increased arterial oxygen saturation index. Patients with prior hospitalization before surgery had higher "hospital death" rates than who did not required such intervention. The diagnoses groups having the higher fatal outcomes probability are aligned with the international literature. A web application is presented where researchers and providers can calculate predicted mortality based on the CgntSCORE on any web browser or smartphone.
先天性心脏病占所有重大先天性异常的近三分之一。先天性心脏缺陷对儿童和成人的发病率、死亡率和医疗费用有重大影响。关于术前死亡率风险的研究很少。
我们的目标是生成一个适用于关注个体先天性心脏病患者心脏手术后死亡率预测的区域性现实的预测模型计算器。
使用 InCor 心脏手术项目的 2240 例连续 CHD 患者数据，开发和验证了接受心脏手术的先天性患者术前死亡风险预测模型。本研究使用了医学参考文献中最常引用的六个人工智能模型：多层感知器（MLP）、随机森林（RF）、极端树（ET）、随机梯度提升（SGB）、AdaBoost 分类（ABC）和袋决策树（BDT）。
随机森林（0.902）获得了最佳的曲线下面积。最具影响力的预测因素包括先前入住 ICU、诊断组、患者身高、左心发育不全综合征、体重、动脉血氧饱和度和肺动脉闭锁。这些综合预测变量在随机森林算法中代表了 67.8%的死亡率风险。
对于 InCor 的患者，身高不超过 66 厘米且体重指数低于 13.0 的患者，“院内死亡”的代表性更大。动脉血氧饱和度指数增加，“院内死亡”的比例下降。与国际文献一致，术前有住院史的患者比无需此类干预的患者的“院内死亡”率更高。具有更高致死率概率的诊断组与国际文献一致。我们提出了一个网络应用程序，研究人员和提供者可以在任何网络浏览器或智能手机上根据 CgntSCORE 计算预测死亡率。
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