Transmission of Mobile Colistin Resistance (mcr-1) by Duodenoscope.

BACKGROUND: Clinicians increasingly utilize polymyxins for treatment of serious infections caused by multidrug-resistant gram-negative bacteria. Emergence of plasmid-mediated, mobile colistin resistance genes creates potential for rapid spread of polymyxin resistance. We investigated the possible transmission of Klebsiella pneumoniae carrying mcr-1 via duodenoscope and report the first documented healthcare transmission of mcr-1-harboring bacteria in the United States. METHODS: A field investigation, including screening targeted high-risk groups, evaluation of the duodenoscope, and genome sequencing of isolated organisms, was conducted. The study site included a tertiary care academic health center in Boston, Massachusetts, and extended to community locations in New England. RESULTS: Two patients had highly related mcr-1-positive K. pneumoniae isolated from clinical cultures; a duodenoscope was the only identified epidemiological link. Screening tests for mcr-1 in 20 healthcare contacts and 2 household contacts were negative. Klebsiella pneumoniae and Escherichia coli were recovered from the duodenoscope; neither carried mcr-1. Evaluation of the duodenoscope identified intrusion of biomaterial under the sealed distal cap; devices were recalled to repair this defect. CONCLUSIONS: We identified transmission of mcr-1 in a United States acute care hospital that likely occurred via duodenoscope despite no identifiable breaches in reprocessing or infection control practices. Duodenoscope design flaws leading to transmission of multidrug-resistant organsisms persist despite recent initiatives to improve device safety. Reliable detection of colistin resistance is currently challenging for clinical laboratories, particularly given the absence of a US Food and Drug Administration-cleared test; improved clinical laboratory capacity for colistin susceptibility testing is needed to prevent the spread of mcr-carrying bacteria in healthcare settings.

Community-acquired in name only: A cluster of carbapenem-resistant Acinetobacter baumannii in a burn intensive care unit and beyond.

OBJECTIVE: To describe an investigation into 5 clinical cases of carbapenem-resistant Acinetobacter baumannii (CRAB). DESIGN: Epidemiological investigation supplemented by whole-genome sequencing (WGS) of clinical and environmental isolates. SETTING: A tertiary-care academic health center in Boston, Massachusetts. PATIENTS OR PARTICIPANTS: Individuals identified with CRAB clinical infections. METHODS: A detailed review of patient demographic and clinical data was conducted. Clinical isolates underwent phenotypic antimicrobial susceptibility testing and WGS. Infection control practices were evaluated, and CRAB isolates obtained through environmental sampling were assessed by WGS. Genomic relatedness was measured by single-nucleotide polymorphism (SNP) analysis. RESULTS: Four clinical cases spanning 4 months were linked to a single index case; isolates differed by 1-7 SNPs and belonged to a single cluster. The index patient and 3 case patients were admitted to the same room prior to their development of CRAB infection, and 2 case patients were admitted to the same room within 48 hours of admission. A fourth case patient was admitted to a different unit. Environmental sampling identified highly contaminated areas, and WGS of 5 environmental isolates revealed that they were highly related to the clinical cluster. CONCLUSIONS: We report a cluster of highly resistant Acinetobacter baumannii that occurred in a burn ICU over 5 months and then spread to a separate ICU. Two case patients developed infections classified as community acquired under standard epidemiological definitions, but WGS revealed clonality, highlighting the risk of burn patients for early-onset nosocomial infections. An extensive investigation identified the role of environmental reservoirs.

Real-Time, Automated Detection of Ventilator-Associated Events: Avoiding Missed Detections, Misclassifications, and False Detections Due to Human Error.

OBJECTIVETo validate a system to detect ventilator associated events (VAEs) autonomously and in real time.DESIGNRetrospective review of ventilated patients using a secure informatics platform to identify VAEs (ie, automated surveillance) compared to surveillance by infection control (IC) staff (ie, manual surveillance), including development and validation cohorts.SETTINGThe Massachusetts General Hospital, a tertiary-care academic health center, during January-March 2015 (development cohort) and January-March 2016 (validation cohort).PATIENTSVentilated patients in 4 intensive care units.METHODSThe automated process included (1) analysis of physiologic data to detect increases in positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (FiO2); (2) querying the electronic health record (EHR) for leukopenia or leukocytosis and antibiotic initiation data; and (3) retrieval and interpretation of microbiology reports. The cohorts were evaluated as follows: (1) manual surveillance by IC staff with independent chart review; (2) automated surveillance detection of ventilator-associated condition (VAC), infection-related ventilator-associated complication (IVAC), and possible VAP (PVAP); (3) senior IC staff adjudicated manual surveillance-automated surveillance discordance. Outcomes included sensitivity, specificity, positive predictive value (PPV), and manual surveillance detection errors. Errors detected during the development cohort resulted in algorithm updates applied to the validation cohort.RESULTSIn the development cohort, there were 1,325 admissions, 479 ventilated patients, 2,539 ventilator days, and 47 VAEs. In the validation cohort, there were 1,234 admissions, 431 ventilated patients, 2,604 ventilator days, and 56 VAEs. With manual surveillance, in the development cohort, sensitivity was 40%, specificity was 98%, and PPV was 70%. In the validation cohort, sensitivity was 71%, specificity was 98%, and PPV was 87%. With automated surveillance, in the development cohort, sensitivity was 100%, specificity was 100%, and PPV was 100%. In the validation cohort, sensitivity was 85%, specificity was 99%, and PPV was 100%. Manual surveillance detection errors included missed detections, misclassifications, and false detections.CONCLUSIONSManual surveillance is vulnerable to human error. Automated surveillance is more accurate and more efficient for VAE surveillance.Infect Control Hosp Epidemiol 2018;826-833.