RT journal article
T1 Identification and validation of clinical phenotypes in Staphylococcus aureus blood stream infection and their association with mortality (FEN-AUREUS cohort-based study)
A1 Gutiérrez Gutiérrez, Belén
A1 Gallego-Mesa, Belén
A1 Kaasch, Achim J.
A1 Riediger, Matthias
A1 Rieg, Siegbert
A1 Trigo, Marta
A1 Salto-Alejandre, Sonsoles
A1 Anguita-Santos, Francisco
A1 Cano, Ángela
A1 Prolo-Acosta, Andrea
A1 López Cardenas, Salvador
A1 Pérez Rodríguez, María Teresa
A1 Martínez-Marcos, Francisco Javier
A1 Merino-Lucas, Esperanza
A1 Anaya-Baz, Blanca
A1 Arizcorreta Yarza, Ana
A1 Piscaglia, Marco
A1 Aceituno, Alexandra
A1 Romero-Calderón, Lidia
A1 Hornuss, Daniel
A1 Alemán-Rodríguez, Aurora
A1 Gimeno-Gascón, Adelina
A1 Recacha, Esther
A1 Torre-Cisneros, Julián
A1 Merchante, Nicolás
A1 Pascual, Álvaro
A1 López Cortés, Luis Eduardo
A1 Rodríguez Baño, Jesús
A2 Cirugía
A2 Medicina
K1 Bacteraemia
K1 Clinical profiles
K1 Mortality
K1 Phenotypes
K1 Staphylococcus aureus
AB Background: Staphylococcus aureus bacteraemia (SAB) is heterogeneous in patients and infection-related features. The aim of the study was to identify clinical phenotypes among patients with SAB, to evaluate their association with mortality, and to derive and validate a simplified probabilistic model for phenotypes assignment. Methods: Phenotypes were derived using two-stage cluster analysis of 2128 patients from the ISAC cohort (recruited between 2013 and 2015), analysing 62 variables. Cox regression assessed phenotype–mortality associations. Logistic regression was employed to develop a simplified probabilistic model for sub-phenotype allocation, validated in two external international cohorts: INSTINCT (1217 patients, recruited between 2006 and 2011) and FEN-AUREUS (1185 patients, recruited between January 2021 and October 2024). The association between sub-phenotypes and 30-day mortality in the validation cohorts was also assessed. Findings: Cluster analysis identified three clinical phenotypes based on the probable portal of entry: A (skin and soft tissues), 458 cases; B (vascular device-associated), 573 cases; and C (other portals of entry or unknown), 1097 cases. Their 30-day mortality was significantly different (13·1%, 18·2% and 25·3%, respectively, p < 0·001). Each phenotype contained two sub-phenotypes with differing characteristics and mortality risks. Also, three phenotypes were found in the INSTINCT cohort, which clustered on the same portals of entry, with two sub-phenotypes in each. When the simplified probabilistic model was applied, the sub-phenotypes showed significant associations with 30-day mortality in both validation cohorts. In INSTINCT, the aHRs were 1·93 (A2 vs A1), 3·40 (B2 vs B1), and 3·04 (C2 vs C1). In FEN-AUREUS, the aHRs were 2·02 (A2 vs A1), 2·11 (B2 vs B1), and 2·44 (C2 vs C1). Interpretation: Patients with SAB can be classified into phenotypes and sub-phenotypes, each exhibiting considerable variations in mortality rates. To facilitate clinical application, a validated open-access algorithm and calculator for phenotype and sub-phenotype assignment have been developed, enabling their use at the time of SAB confirmation. This tool aims to support timely and personalised patient care. Funding: Instituto de Salud Carlos III, Spanish Ministry of Science, Innovation and Universities (PI21/01801).
PB Elsevier Ltd
SN 2589-5370
YR 2025
FD 2025-05
LK http://hdl.handle.net/10498/38159
UL http://hdl.handle.net/10498/38159
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 09-may-2026