Tracking a Hospital Outbreak of Carbapenem-Resistant Klebsiella pneumoniae with Whole-Genome Sequencing
Science Translational Medicine • 22 Aug 2012 • Vol 4, Issue 148 • p. 148ra116 • DOI: 10.1126/scitranslmed.3004129
A Detective Story
Some infections are largely a thing of the past—plague, syphilis. The unfortunate result of these antibiotic-driven successes is the emergence of drug-resistant pathogens. And, ironically enough, hospitals are at the center of the problem. An example of this occurred in 2011 at the Clinical Center of the U.S. National Institutes of Health (NIH), in which an outbreak of drug-resistant Klebsiella pneumoniae infected 18 patients, causing the death of 6 of them. Using a combination of whole-genome sequencing and patient tracking, Snitkin and his colleagues examined how the bacteria was spreading through the hospital. The results outline a complicated path of transmission within the hospital that defied standard containment methods, yielding lessons for the future.
A patient known to be infected with a drug-resistant form of K. pneumoniae was admitted to the NIH Clinical Center on 13 June 2011. Enhanced isolation procedures were immediately implemented, and no spread of the bacteria was seen for the month she was in the hospital. Although all seemed well, a few weeks later on August 5th, a second infected patient was discovered, followed by a series of other patients with infection or colonization—about 1 a week to a total of 18 by the end of 2011. Six people ultimately died as a result of the bacteria. The outbreak was finally contained by rigorous control procedures.
A careful survey of the bed locations of each patient did not shed much light on how the bacteria traveled on its deadly path: The first patient did not even come into contact with any of the others. So the authors performed whole-genome sequencing on all of the bacteria that were found, determining the most likely evolutionary relationships among them by comparing the variations at single nucleotides that arise as bacteria grow. Combining this evolutionary information with the physical tracking of the patients pointed to the most likely transmission scenario.
The authors concluded that all of the K. pneumoniae cases likely originated with the index patient, from at least two different sites on her body, rather than by independently introduced bacteria. There were at least three different initial transmission events. Particularly disturbing was the fact that one of the infections could be linked to contamination of a ventilator that had been cleaned by thorough methods.
Sophisticated deployment of whole-genome sequencing revealed the weaknesses in this medical who-done-it, informing improvements in hospital preventive measures. If applied rapidly, such analysis can even expose the causes of nosocomial infections in real time.
Abstract
The Gram-negative bacteria Klebsiella pneumoniae is a major cause of nosocomial infections, primarily among immunocompromised patients. The emergence of strains resistant to carbapenems has left few treatment options, making infection containment critical. In 2011, the U.S. National Institutes of Health Clinical Center experienced an outbreak of carbapenem-resistant K. pneumoniae that affected 18 patients, 11 of whom died. Whole-genome sequencing was performed on K. pneumoniae isolates to gain insight into why the outbreak progressed despite early implementation of infection control procedures. Integrated genomic and epidemiological analysis traced the outbreak to three independent transmissions from a single patient who was discharged 3 weeks before the next case became clinically apparent. Additional genomic comparisons provided evidence for unexpected transmission routes, with subsequent mining of epidemiological data pointing to possible explanations for these transmissions. Our analysis demonstrates that integration of genomic and epidemiological data can yield actionable insights and facilitate the control of nosocomial transmission.
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Supplementary Material
Summary
Methods
Fig. S1. Repetitive element PCR and pulsed-field gels of representative outbreak isolates.
Fig. S2. Surveillance cultures for outbreak patients.
Fig. S3. Transmission opportunities between patients when using negative rectal surveillance to exclude patient colonization
Fig. S4. Predicted transmission chart based only on genetic data.
Fig. S5. Predicted transmission chart based only on epidemiological data.
Fig. S6. Computing epidemiological distances between patients.
Table S1. Genome sequencing statistics.
Table S2. Characteristics of patients who acquired outbreak strain.
Table S3. MICs for antibiotic susceptibility of outbreak isolates.
Table S4. Mutations identified among outbreak genomes.
Resources
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Information & Authors
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Published In
Science Translational Medicine
Volume 4 | Issue 148
August 2012
August 2012
Copyright
Copyright © 2012, American Association for the Advancement of Science.
Submission history
Received: 10 April 2012
Accepted: 30 July 2012
Acknowledgments
We thank S. Holland, P. Murray, F. Candotti, and H. Kong for their thoughtful discussions and advice, and T. Connor and J. Parkhill for sharing unpublished sequence data. Funding: Supported by the National Human Genome Research Institute and NIH Clinical Center Intramural Research Programs and by an NIH Director’s Challenge Award for genome sequencing. E.S.S. is supported by a Pharmacology Research Associate Training Fellowship, National Institute of General Medical Sciences. Author contributions: E.S.S., A.M.Z., D.K.H., T.N.P., and J.A.S. conceived the study. NISC performed genome sequencing. P.J.T. performed bioinformatics annotation and coordinated sequencing studies. F.S. performed repetitive PCR and pulsed-field gel electrophoresis. E.S.S. performed all data analysis. E.S.S., D.K.H., T.N.P., and J.A.S. wrote the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: The results of the Whole Genome Shotgun project have been deposited at DNA Database of Japan/European Molecular Biology Laboratory/GenBank under accession numbers AJZU00000000, AJZV00000000, AJZW00000000, AJZX00000000, AJZY00000000, AJZZ00000000, AKAA00000000, AKAB00000000, AKAC00000000, AKAD00000000, AKAE00000000, AKAF00000000, AKAG00000000, AKAH00000000, AKAI00000000, AKAJ00000000, AKAK00000000, AKAL00000000, AKAM00000000, and AKAN00000000.
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