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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.

這篇研究想知道：把「病毒量」和「鼻子裡的細菌組成」兩種資訊放在一起看，能不能更準確預測COVID-19病人是否會死亡。研究團隊採集了81位住院病人和70位沒感染的人的鼻咽拭子，做詳細的細菌基因定序分析。結果發現，病毒量越高，死亡風險越高（這點大家原本就知道），而且COVID-19病人鼻子裡的細菌生態也明顯改變、變得比較不平衡，尤其是死亡的病人身上，某些「伺機性」細菌（像大腸桿菌）特別多。有意思的是，當研究團隊把「病毒量+年齡+特定細菌種類」三種資訊一起輸入電腦模型時，預測病人存活與否的準確率高達90%以上，比只用病毒量或只用細菌資料的模型都準確許多。

不過要提醒的是，這是一個單一醫院、單一時間點的研究，樣本數不算大，也沒有考慮到病人是否使用抗生素等可能影響細菌組成的因素，因此目前的結論還算是「初步發現」，作者也強調需要更多、更大規模、不同醫院的研究來驗證，才能真正應用在臨床上幫病人做風險評估。

全文連結: https://pubmed.ncbi.nlm.nih.gov/41588872/