背景:在COVID-19大流行期间,人工神经网络(ANN)系统一直在为临床决策提供帮助。然而,为了达到最佳效果,这些模型应将多个临床数据点与简单模型联系起来.本研究旨在使用结合临床变量和ANN分析的肺部炎症数据的两步方法对院内死亡率和机械通气风险进行建模。
方法:4317例COVID-19住院患者的数据集,包括266名需要机械通气的病人,被分析。收集人口统计学和临床数据(包括住院时间和死亡率)以及胸部计算机断层扫描(CT)数据。使用经过训练的ANN分析肺受累。然后使用未调整和多变量Cox比例风险模型分析组合数据。
结果:与ANN分配的肺部受累百分比相关的总体院内死亡率(风险比[HR]:5.72,95%置信区间[CI]:4.4-7.43,对于肺组织受COVID-19肺炎影响>50%的患者,p<0.001),年龄类别(HR:5.34,95%CI:3.32-8.59,>80岁,p<0.001),降钙素原(HR:2.1,95%CI:1.59-2.76,p<0.001,C反应蛋白水平(CRP)(HR:2.11,95%CI:1.25-3.56,p=0.004),肾小球滤过率(eGFR)(HR:1.82,95%CI:1.37-2.42,p<0.001)和肌钙蛋白(HR:2.14,95%CI:1.69-2.72,p<0.001)。此外,机械通气的风险也与基于ANN的肺部炎症百分比相关(HR:13.2,95%CI:8.65-20.4,>50%受累的患者p<0.001),年龄,降钙素原(HR:1.91,95%CI:1.14-3.2,p=0.14,eGFR(HR:1.82,95%CI:1.2-2.74,p=0.004)和临床变量,包括糖尿病(HR:2.5,95%CI:1.91-3.27,p<0.001),心脑血管疾病(HR:3.16,95%CI:2.38-4.2,p<0.001)和慢性肺病(HR:2.31,95%CI:1.44-3.7,p<0.001)。
结论:基于人工神经网络的肺组织受累是COVID-19不良结局的最强预测因子,并且是临床决策的有价值的支持工具。
During COVID-19 pandemic, artificial neural network (ANN) systems have been providing aid for clinical decisions. However, to achieve optimal results, these models should link multiple clinical data points to simple models. This study aimed to model the in-hospital mortality and mechanical ventilation risk using a two step approach combining clinical variables and ANN-analyzed lung inflammation data.
A data set of 4317 COVID-19 hospitalized patients, including 266 patients requiring mechanical ventilation, was analyzed. Demographic and clinical data (including the length of hospital stay and mortality) and chest computed tomography (CT) data were collected. Lung involvement was analyzed using a trained ANN. The combined data were then analyzed using unadjusted and multivariate Cox proportional hazards models.
Overall in-hospital mortality associated with ANN-assigned percentage of the lung involvement (hazard ratio [HR]: 5.72, 95% confidence interval [CI]: 4.4-7.43, p < 0.001 for the patients with >50% of lung tissue affected by COVID-19 pneumonia), age category (HR: 5.34, 95% CI: 3.32-8.59 for cases >80 years, p < 0.001), procalcitonin (HR: 2.1, 95% CI: 1.59-2.76, p < 0.001, C-reactive protein level (CRP) (HR: 2.11, 95% CI: 1.25-3.56, p = 0.004), glomerular filtration rate (eGFR) (HR: 1.82, 95% CI: 1.37-2.42, p < 0.001) and troponin (HR: 2.14, 95% CI: 1.69-2.72, p < 0.001). Furthermore, the risk of mechanical ventilation is also associated with ANN-based percentage of lung inflammation (HR: 13.2, 95% CI: 8.65-20.4, p < 0.001 for patients with >50% involvement), age, procalcitonin (HR: 1.91, 95% CI: 1.14-3.2, p = 0.14, eGFR (HR: 1.82, 95% CI: 1.2-2.74, p = 0.004) and clinical variables, including diabetes (HR: 2.5, 95% CI: 1.91-3.27, p < 0.001), cardiovascular and cerebrovascular disease (HR: 3.16, 95% CI: 2.38-4.2, p < 0.001) and chronic pulmonary disease (HR: 2.31, 95% CI: 1.44-3.7, p < 0.001).
ANN-based lung tissue involvement is the strongest predictor of unfavorable outcomes in COVID-19 and represents a valuable support tool for clinical decisions.