Prognostic value of clinical and microbiological parameters in COVID-19: the COMEPA study

Abstract

Purpose. Clusters’ analysis may indicate distinct phenotypes and symptom profiles potentially due to differing pathophysiology and needing different clinical approaches in COVID-19. However, the research about clusters combining clinical and microbiological information is still limited. The purpose of our study was to examine the prognostic role of clusters, including clinical and microbiological parameters in terms of severity of lung involvement, in-hospital mortality, and the occurrence of long COVID. Methods. Information regarding COVID-19, mortality, severity of lung involvement derived from medical records; long COVID symptomatology was ascertained using phone calls. A k-means clustering method was considered to partition data into clusters considering typical symptoms of COVID-19 present at hospital admission and SarsCov2 variants. Results. Our analysis identified among 414 patients (mean age: 65 years; males: 59.9%) four different clusters. Cluster 1: higher prevalence of respiratory COVID symptoms at hospital admission; Cluster 2: higher frequency of non-respiratory COVID symptoms and a higher prevalence of the Alpha variant; Cluster 3: older subjects and more frequently men, reporting more severe medical conditions and with a higher prevalence of Wild type variant; Cluster 4: patients that more often reported general and gastrointestinal COVID symptoms at the admission. From a prognostic point of view, patients in cluster 3 more frequently died and were admitted in a nursing home, with significantly lower presence of long COVID symptomatology. Conclusions. Clusters combining clinical and microbiological information in individuals hospitalized with COVID-19 that had different not only different profiles, but also different prognostic values, also in terms of long COVID.