Publication date: Jun 01, 2025
Background: COVID-19, caused by SARS-CoV-2, has posed significant challenge to global healthcare systems, necessitating reliable biomarkers to predict disease severity and mortality. This systematic review and meta-analysis evaluated the prognostic value of novel biomarkers in COVID-19 patients. The aim of this study was to identify and prioritize the most prognostically relevant novel biomarkers associated with COVID-19 outcomes. Methods: We conducted a systematic review and meta-analysis of the available evidence. A systematic search of PubMed and Web of Science was performed to identify studies on the COVID-19 biomarkers. Observational studies that compared poor (severe disease/mortality) and good outcomes were included. For continuous measures, standard mean differences (SMDs) with 95% confidence intervals (CIs) were calculated. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) curve analyses for the biomarkers were used. The risk of bias was assessed using the Newcastle-Ottawa scale. Results: Of the 2907 screened studies, 38 were included (21 in the meta-analysis). MR-proADM showed higher levels of prediction for poor outcomes (SMD = 1. 40, 95% CI: 1. 11-1. 69; AUC 0. 74-0. 96; sensitivity, 85%; specificity, 71%). The neutrophil-to-lymphocyte ratio (NLR) showed a high correlation with disease severity (SMD = 1. 07, 95% CI: 0. 79-1. 35; AUC 0. 73-0. 98; sensitivity, 86%; specificity, 78%). Increased KL-6 levels were associated with lung injury (SMD = 1. 22, 95% CI: 0. 24-2. 19; AUC 0. 85-0. 95). Other biomarkers (suPAR, miR-155, Galectin-3) showed promise but lacked sufficient data for pooled analysis. Heterogeneity was observed among the included studies in terms of diagnostic accuracy. These findings indicate that elevated levels of MR-proADM, NLR, and KL-6 are significantly associated with COVID-19 prognostic accuracy to guide patient management. Conclusions: MR-proADM, NLR, and KL-6 levels demonstrated strong prognostic value for COVID-19 severity and mortality. These biomarkers can enhance clinical decision-making.
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| Concepts | Keywords |
|---|---|
| Covid | biomarkers |
| Healthcare | COVID-19 |
| Heterogeneity | KL-6 |
| Ottawa | MR-proADM |
| Reliable | SARS-CoV-2 |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | SARS-CoV-2 Infection |
| pathway | REACTOME | SARS-CoV-2 Infection |
| disease | MESH | lung injury |
| drug | DRUGBANK | Coenzyme M |
| disease | MESH | acute respiratory distress syndrome |
| disease | MESH | cytokine storm |
| disease | MESH | respiratory failure |
| disease | MESH | death |
| disease | IDO | host |
| disease | MESH | inflammation |
| disease | MESH | pulmonary fibrosis |
| disease | MESH | fibrosis |
| disease | MESH | clinical relevance |
| disease | IDO | country |
| drug | DRUGBANK | Saquinavir |
| disease | IDO | quality |
| disease | MESH | complications |
| disease | IDO | immune response |
| disease | IDO | intervention |
| drug | DRUGBANK | (S)-Des-Me-Ampa |
| disease | MESH | Pneumonia |
| drug | DRUGBANK | Ribostamycin |
| disease | MESH | Adenovirus Infections |
| disease | MESH | emergency |
| disease | MESH | Critically Ill |
| disease | MESH | Allergy |
| disease | MESH | Asthma |
| pathway | KEGG | Asthma |
| disease | MESH | sepsis |
| drug | DRUGBANK | Creatinine |
| disease | MESH | infection |
| disease | IDO | production |
| drug | DRUGBANK | Sulfasalazine |