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Abstract
Background

The nasopharyngeal (NP) microbiota may play a critical role in modulating host immune responses during SARS-CoV-2 infection, yet its utility for predicting clinical outcomes is not fully defined. We aimed to determine if an integrative approach, combining NP microbial profiles with virological and clinical data, could improve mortality prediction in COVID-19 patients.

Methods

We analyzed nasopharyngeal swabs from 81 COVID-19 patients and 70 non-infected controls. Full-length 16S rRNA sequencing was used for microbiome profiling. Predictive models were developed using machine learning to integrate microbial taxa with viral load and other clinical metadata.

Results

High viral load was identified as the strongest independent predictor of mortality by multivariate logistic regression (OR: 3.80). SARS-CoV-2 infection and its associated viral load were linked to significant reductions in microbial community evenness. Critically, a machine-learning model that integrated viral load, patient age, and specific microbial taxa achieved a high predictive accuracy for mortality (AUC = 0.9046), significantly outperforming models based on clinical data alone.

Conclusions

Our findings demonstrate that an integrative approach, combining nasopharyngeal microbiota profiles with viral load, provides a robust framework for predicting mortality in COVID-19 patients. This strategy offers a promising, non-invasive tool for improving clinical risk stratification.
Keywords: COVID-19; Full-length 16S rRNA sequencing; Mortality prediction; Nasopharyngeal microbiota; Predictive modeling; Viral load.