Publication date: Aug 21, 2026
Elderly patients with coronavirus disease 2019 (COVID-19) exhibit high mortality rates. We assessed whether microRNA (miRNA) profiling provides prognostic information in this population. This multicenter study included hospitalized COVID-19 patients aged ≥65 years (n = 763). Clinical subphenotypes were identified using clinical data through k-prototypes algorithm. Plasma miRNome was profiled using qPCR. Clinical and miRNA-based models predicting 90-day mortality were developed using Variable Selection Using Random Forests (VSURF). Median age was 79 years, 44. 0% were female, and 90-day mortality was 26. 7%. Although 13 candidate miRNAs were identified during screening (n = 39), none were associated with mortality in the full derivation cohort (n = 340), leading to a subphenotype-stratified analysis. Three subphenotypes (elderly COVID-1 [eCOVID-1], -2, and -3) with distinct clinical features and mortality risks were identified. In eCOVID-2, miR-106b-3p was the strongest predictor of 90-day mortality and improved model discrimination beyond clinical variables alone area under the curve (AUC: 0. 75 vs. 0. 66). Plasma miRNAs provide complementary prognostic information when integrated with clinical variables in elderly COVID-19 subphenotypes.
Open Access PDF
| Concepts | Keywords |
|---|---|
| biomarkers | |
| cluster analysis | |
| elderly COVID-19 patients | |
| machine learning | |
| miRNAs | |
| mortality prognostic models | |
| subphenotypes |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | COVID-19 |
| disease | MESH | included |