Unsuspected Clonal Spread of Methicillin-Resistant Staphylococcus aureus Causing Bloodstream Infections in Hospitalized Adults Detected Using Whole Genome Sequencing. (1st May 2022)

• Record Type: Journal Article
• Authors: Talbot, Brooke M; Jacko, Natasia F; Petit, Robert A; Pegues, David A; Shumaker, Margot J; Read, Timothy D; David, Michael Z
• Is Part Of: Clinical infectious diseases. Volume 75:Number 12(2022)
• Page Start: 2104
• Page End: 2112
• Publication Date: 2022-05-01
• Subjects: Staphylococcus aureus -- bloodstream infections -- hospital epidemiology -- outbreak detection -- infection prevention; Communicable diseases -- Periodicals; 616.905
• DOI: 10.1093/cid/ciac339
• Languages: English
• ISSNs: 1058-4838

Abstract

Abstract: Background: Though detection of transmission clusters of methicillin-resistant Staphylococcus aureus (MRSA) infections is a priority for infection control personnel in hospitals, the transmission dynamics of MRSA among hospitalized patients with bloodstream infections (BSIs) has not been thoroughly studied. Whole genome sequencing (WGS) of MRSA isolates for surveillance is valuable for detecting outbreaks in hospitals, but the bioinformatic approaches used are diverse and difficult to compare. Methods: We combined short-read WGS with genotypic, phenotypic, and epidemiological characteristics of 106 MRSA BSI isolates collected for routine microbiological diagnosis from inpatients in 2 hospitals over 12 months. Clinical data and hospitalization history were abstracted from electronic medical records. We compared 3 genome sequence alignment strategies to assess similarity in cluster ascertainment. We conducted logistic regression to measure the probability of predicting prior hospital overlap between clustered patient isolates by the genetic distance of their isolates. Results: While the 3 alignment approaches detected similar results, they showed some variation. A gene family–based alignment pipeline was most consistent across MRSA clonal complexes. We identified 9 unique clusters of closely related BSI isolates. Most BSIs were healthcare associated and community onset. Our logistic model showed that with 13 single-nucleotide polymorphisms, the likelihood that any 2 patients in a cluster had overlapped in a hospital was 50%. Conclusions: Multiple clusters of closely related MRSA isolates can be identified using WGS among strains cultured from BSI in 2 hospitals. Genomic clustering of these infections suggests that transmission resulted from a mix of community spread and healthcare exposures long before BSI diagnosis. Abstract : Multiple clusters of closely related methicillin-resistant Staphylococcus aureus bloodstream infections were identified using whole genome sequencing in 2 hospitals by 3 bioinformatic workflows. Genomic epidemiology suggests that transmission resulted from a mix of community spread and healthcare exposures long before symptom onset.