Language
English
Publication Date
1-3-2026
Journal
Communications Medicine
DOI
10.1038/s43856-025-01230-w
PMID
41484172
PMCID
PMC12764860
PubMedCentral® Posted Date
1-3-2026
PubMedCentral® Full Text Version
Post-print
Abstract
Background: The post-acute sequelae of SARS-CoV-2 (PASC), also known as long COVID, remain a significant health issue that is incompletely understood. Predicting which acutely infected individuals will develop long COVID is challenging due to the absence of established biomarkers, clear disease mechanisms, or well-defined sub-phenotypes. Machine learning (ML) models may address this gap by leveraging clinical data to enhance diagnostic precision.
Methods: Clinical data, including antibody titers and viral load measurements collected at the time of hospital admission, are used to predict the likelihood of acute COVID-19 progressing to long COVID. Machine learning models are trained and evaluated for predictive performance. Feature importance analysis is performed to identify the most influential predictors.
Results: The machine learning models achieve median AUROC values ranging from 0.64 to 0.66 and AUPRC values between 0.51 and 0.54, demonstrating predictive capabilities. Low antibody titers and high viral loads at hospital admission emerge as the strongest predictors of long COVID outcomes. Comorbidities-such as chronic respiratory, cardiac, and neurologic diseases-and female sex are also identified as significant risk factors.
Conclusions: Machine learning models identify patients at risk for developing long COVID based on baseline clinical characteristics. These models guide early interventions, improve patient outcomes, and mitigate the long-term public health impacts of SARS-CoV-2.
Published Open-Access
yes
Recommended Citation
Doni Jayavelu, Naresh; Samaha, Hady; Wimalasena, Sonia Tandon; et al., "Machine Learning Models Predict Long COVID Outcomes Based on Baseline Clinical and Immunologic Factors" (2026). Faculty, Staff and Students Publications. 6309.
https://digitalcommons.library.tmc.edu/baylor_docs/6309