基于人工神经网络的 COVID-19 肺部组织受累预测及其作为院内独立死亡和机械通气风险因素。

Artificial neural network based prediction of the lung tissue involvement as an independent in-hospital mortality and mechanical ventilation risk factor in COVID-19.

机构信息

Department of Infectious, Tropical Diseases and Immune Deficiency, Pomeranian Medical University in Szczecin, Szczecin, Poland.

Department of Biophysics, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Zabrze, Poland.

出版信息

J Med Virol. 2023 May;95(5):e28787. doi: 10.1002/jmv.28787.

DOI:10.1002/jmv.28787

PMID:37219059

Abstract

INTRODUCTION

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

ANN-based lung tissue involvement is the strongest predictor of unfavorable outcomes in COVID-19 and represents a valuable support tool for clinical decisions.

摘要

简介

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，这些模型应该将多个临床数据点链接到简单的模型中。本研究旨在通过两步法结合临床变量和 ANN 分析的肺部炎症数据，对住院死亡率和机械通气风险进行建模。

方法

分析了 4317 名 COVID-19 住院患者的数据，其中 266 名患者需要机械通气。收集了人口统计学和临床数据（包括住院时间和死亡率）和胸部计算机断层扫描 (CT) 数据。使用经过训练的 ANN 分析肺部受累情况。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

总体而言，与 ANN 分配的肺部受累百分比相关的院内死亡率（风险比 [HR]：5.72，95%置信区间 [CI]：4.4-7.43，p<0.001，用于 >50%肺组织受累的患者COVID-19 肺炎）、年龄类别（HR：>80 岁的病例为 5.34，95%CI：3.32-8.59，p<0.001）、降钙素（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，p<0.001，用于>50%受累的患者）、年龄、降钙素（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）、心血管和脑血管疾病（HR：3.16，95%CI：2.38-4.2，p<0.001）和慢性肺部疾病（HR：2.31，95%CI：1.44-3.7，p<0.001）。