Rapid, Genomic-Based Surveillance IDs Aids Hospital Infection Control

Routine genomic sequencing can identify and inform hospital infection control personnel of patient transmission events in near real-time, enhancing not only detection, but follow-up and investigation for better outbreak control, according to a study published April 23 in Infection Control & Hospital Epidemiology. The integration of genomic and clinical epidemiologic data analyses detected transmission clusters not identified with standard surveillance of nosocomial infections.

Currently, investigation of suspected health care-associated infectious transmissions requires manual surveillance of case clusters by infection control personnel, followed by strain typing of clinical and environmental isolates in suspected clusters.

To rapidly detect transmission clusters, the researchers assessed the effectiveness of infection control surveillance using whole-genome sequencing (WGS) of microbial pathogens to identify potential transmission events for epidemiologic review. Prospective sampling of clinical isolates at a single academic medical center occurred from Sept. 1, 2016, to Sept. 30, 2017. Surveillance cultures for methicillin-resistant S. aureus (MRSA) and vancomycinresistant enterococci were routinely obtained on admission and weekly in the seven adult intensive care units (ICUs), the pediatric ICU, the neonatal ICU, and the bone marrow transplant unit. This study included one isolate per body site per patient per day.

Strains of Staphylococcus aureus, Enterococcus faecium, Pseudomonas aeruginosa, and Klebsiella pneumonia were evaluated. Isolate genomes were sequenced. Single-nucleotide variants were analyzed and a cloud-computing platform was used for WGS analysis and cluster identification through genomic analysis with linkage to geospatial and temporal data from integrated medical records. The clinical infection control department staff conducted a retrospective manual chart review to determine whether the clinical evidence supported the transmission of the genomically related bacterial isolates between patients.

The researchers report that 1,257 isolates with a positive culture for the species of interest were received from 1,073 patients. Sequencing was successful for 823 patients (87 percent inpatients). Most strains of the four pathogens were unrelated. However, 34 potential transmission clusters (n = 96 patients) were identified.

Chart review found that nine clusters had obvious clinical associations that were identified retrospectively. Only one of these clusters was suspected with routine, manual surveillance. Cross-transmission occurred in both the inpatient and outpatient settings. The authors note that the characteristics of the potential clusters were complex and likely not identifiable by traditional surveillance alone.

While the average cluster had 2.9 patients, the largest genetic cluster, included 21 MRSA isolates from 13 patients who all had community-onset MRSA infections. Six of these patients shared a history of recent or current intravenous (IV) drug use, one patient was an emergency medical technician who may have had occupational contact with IV drug users, and two patients with no prior history of IV drug use were followed for chronic medical conditions by the same clinical service. Four additional patients had no history of IV drug use and no obvious clinical connection to the other cases.

“The integration of clinical data is essential to prioritize suspect clusters for investigation, and for existing infections, a timely review of both the clinical and WGS results can hold promise to reduce health care-associated infections,” write the authors led by Doyle Ward Ph.D., from University of Massachusetts, Worcester. “A richer understanding of cross-transmission events within health care settings will require the expansion of current surveillance approaches.”

The cloud-computing approach has potential to inform infection control practice “proactively,” the authors say, noting that WGS analysis of a cultivated isolate can be performed in less than 48 hours and their cloud computing platform can analyze and generate potential relatedness matches in about 3 hours.

Takeaway: Integration of genomic and clinical epidemiologic data can augment infection control with nearly real-time surveillance for to identify of infectious transmission events within the health care setting. Rapid expanded surveillance may ultimately improve patient safety and lead to health care savings.