Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era
Heliyon, vol.10, no.3, 2024 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 10 Issue: 3
- Publication Date: 2024
- Doi Number: 10.1016/j.heliyon.2024.e25410
- Journal Name: Heliyon
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, Food Science & Technology Abstracts, Veterinary Science Database, Directory of Open Access Journals
- Keywords: Algorithms, Clinical laboratory tests, COVID-19, Disease severity, Predictive value of tests
- Ankara Yıldırım Beyazıt University Affiliated: Yes
Abstract
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.