Singh Vivek, Kamaleswaran Rishikesan, Chalfin Donald, Buño-Soto Antonio, San Roman Janika, Rojas-Kenney Edith, Molinaro Ross, von Sengbusch Sabine, Hodjat Parsa, Comaniciu Dorin, Kamen Ali
Siemens Healthineers, Digital Technology and Innovation, 755 College Road East, Princeton, NJ 08540, USA.
Emory University School of Medicine WMB, 1010 Woodruff Circle, Suite 4127, Atlanta, GA 30322, USA.
iScience. 2021 Dec 17;24(12):103523. doi: 10.1016/j.isci.2021.103523. Epub 2021 Nov 27.
The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0.78, 95% CI: 0.77-0.82, and the negative predictive value NPV of 0.86, 95% CI: 0.84-0.88 for the need to use a ventilator and has an accuracy with AUC of 0.85, 95% CI: 0.84-0.86, and the NPV of 0.94, 95% CI: 0.92-0.96 for predicting in-hospital 30-day mortality.
严重急性呼吸综合征冠状病毒2(SARS-CoV-2)病毒在全球造成了巨大的医疗负担。我们的重点是开发一个实用且易于部署的系统,以预测2019冠状病毒病(COVID-19)患者的疾病严重表现,旨在协助临床医生进行分诊和治疗决策。我们提出的预测算法是一个基于人工智能的网络,它使用了来自四个医疗系统的8427份COVID-19患者记录进行训练。该模型提供了一个严重程度风险评分以及各种临床结果的可能性,即使用呼吸机和死亡的可能性。使用患者年龄和九个实验室指标训练的模型,对于是否需要使用呼吸机的预测准确性,曲线下面积(AUC)为0.78,95%置信区间(CI):0.77-0.82,阴性预测值(NPV)为0.86,95%CI:0.84-0.88;对于预测30天院内死亡率,准确性的AUC为0.85,95%CI:0.84-0.86,NPV为0.94,95%CI:0.92-0.96。