AI Article Synopsis
- Severe community-acquired pneumonia (SCAP) has high mortality rates globally, and this study aims to identify specific biomarkers that can predict patient outcomes.
- A combination of metagenomic and transcriptomic approaches was used to analyze samples from 275 SCAP patients, revealing that mortality isn't solely dependent on the type of pathogens, but rather on differences in host gene expression.
- The study developed a predictive model with six bio-signatures that showed a strong accuracy (AUC of 0.953) in forecasting 30-day mortality, highlighting the importance of integrating clinical data with multi-omics approaches for improving treatment strategies.
Article Abstract
Background: Severe community-acquired pneumonia (SCAP) results in high mortality as well as massive economic burden worldwide, yet limited knowledge of the bio-signatures related to prognosis has hindered the improvement of clinical outcomes. Pathogen, microbes and host are three vital elements in inflammations and infections. This study aims to discover the specific and sensitive biomarkers to predict outcomes of SCAP patients.
Methods: In this study, we applied a combined metagenomic and transcriptomic screening approach to clinical specimens gathered from 275 SCAP patients of a multicentre, prospective study.
Findings: We found that 30-day mortality might be independent of pathogen category or microbial diversity, while significant difference in host gene expression pattern presented between 30-day mortality group and the survival group. Twelve outcome-related clinical characteristics were identified in our study. The underlying host response was evaluated and enrichment of genes related to cell activation, immune modulation, inflammatory and metabolism were identified. Notably, omics data, clinical features and parameters were integrated to develop a model with six signatures for predicting 30-day mortality, showing an AUC of 0.953 (95% CI: 0.92-0.98).
Interpretation: In summary, our study linked clinical characteristics and underlying multi-omics bio-signatures to the differential outcomes of patients with SCAP. The establishment of a comprehensive predictive model will be helpful for future improvement of treatment strategies and prognosis with SCAP.
Funding: National Natural Science Foundation of China (No. 82161138018), Shanghai Municipal Key Clinical Specialty (shslczdzk02202), Shanghai Top-Priority Clinical Key Disciplines Construction Project (2017ZZ02014), Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases (20dz2261100).
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10507133 | PMC |
| http://dx.doi.org/10.1016/j.ebiom.2023.104790 | DOI Listing |
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