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Preparation of a Blood Culture Pellet for Rapid Bacterial Identification and Antibiotic Susceptibility Testing
Published on: October 15, 2014
Identification and validation of clinical phenotypes in Staphylococcus aureus bloodstream infection and their
Belén Gutiérrez-Gutiérrez1,2, Belén Gallego-Mesa1,2, Achim J Kaasch3
1Unidad de Enfermedades Infecciosas y Microbiología, Hospital Universitario Virgen Macarena and Departamentos de Medicina y Microbiología, Universidad de Sevilla/Instituto de Biomedicina de Sevilla/CSIC, Seville, Spain.
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).
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