Yilmaz Gulsen, Sezer Sevilay, Bastug Aliye, Singh Vivek, Gopalan Raj, Aydos Omer, Ozturk Busra Yuce, Gokcinar Derya, Kamen Ali, Gramz Jamie, Bodur Hurrem, Akbiyik Filiz
Department of Medical Biochemistry, Ankara Yıldırım Beyazıt University, Ankara, Turkey.
Department of Medical Biochemistry, Ministry of Health, Ankara Bilkent City Hospital, Ankara, Turkey.
Heliyon. 2024 Feb 2;10(3):e25410. doi: 10.1016/j.heliyon.2024.e25410. eCollection 2024 Feb 15.
All viruses, including SARS-CoV-2, the virus responsible for COVID-19, continue to evolve, which can lead to new variants. The objective of this study is to assess the agreement between real-world clinical data and an algorithm that utilizes laboratory markers and age to predict the progression of disease severity in COVID-19 patients during the pre-Omicron and Omicron variant periods. The study evaluated the performance of a deep learning (DL) algorithm in predicting disease severity scores for COVID-19 patients using data from the USA, Spain, and Turkey (Ankara City Hospital (ACH) data set). The algorithm was developed and validated using pre-Omicron era data and was tested on both pre-Omicron and Omicron-era data. The predictions were compared to the actual clinical outcomes using a multidisciplinary approach. The concordance index values for all datasets ranged from 0.71 to 0.81. In the ACH cohort, a negative predictive value (NPV) of 0.78 or higher was observed for severe patients in both the pre-Omicron and Omicron eras, which is consistent with the algorithm's performance in the development cohort.
所有病毒，包括导致新冠疫情的新冠病毒2型（SARS-CoV-2），都在持续进化，这可能会导致新的变体出现。本研究的目的是评估真实世界临床数据与一种利用实验室指标和年龄来预测新冠患者在奥密克戎变体出现之前和奥密克戎变体时期疾病严重程度进展的算法之间的一致性。该研究使用来自美国、西班牙和土耳其（安卡拉市立医院（ACH）数据集）的数据，评估了一种深度学习（DL）算法在预测新冠患者疾病严重程度评分方面的性能。该算法使用奥密克戎变体出现之前的数据进行开发和验证，并在奥密克戎变体出现之前和奥密克戎变体时期的数据上进行测试。使用多学科方法将预测结果与实际临床结果进行比较。所有数据集的一致性指数值在0.71至0.81之间。在ACH队列中，在奥密克戎变体出现之前和奥密克戎变体时期，重症患者的阴性预测值（NPV）均观察到为0.78或更高，这与该算法在开发队列中的表现一致。
