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Development and Validation of a Clinical Prediction Rule to Predict Transmission of Methicillin-Resistant Staphylococcus aureus in Nursing Homes.

ABSTRACT: The prevalence of methicillin-resistant Staphylococcus aureus (MRSA) colonization among nursing home residents is high. Health-care workers (HCWs) often serve as a vector in MRSA transmission. The ability to identify residents who are likely to transmit MRSA to HCWs' hands and clothing during clinical care is important so that infection control measures, such as Contact Precautions, can be employed. Using data on demographic and clinical characteristics collected from residents of community nursing homes in Maryland and Michigan between 2012 and 2014, we developed a clinical prediction rule predicting the probability of MRSA transmission to HCWs' gowns. We externally validated this model in a cohort of Department of Veterans Affairs nursing home residents from 7 states between 2012 and 2016. The prediction model, which included sex, race, resident dependency on HCWs for care, the presence of any medical device, diabetes mellitus, and chronic skin breakdown, showed good performance (C statistic = 0.70; sensitivity = 76%, specificity = 49%) in the development set. The decision curve analysis indicated that this model has greater clinical utility than use of a nares surveillance culture for MRSA colonization, which is current clinical practice for placing hospital inpatients on Contact Precautions. The prediction rule demonstrated less utility in the validation cohort, suggesting that a separate rule should be developed for residents of Veterans Affairs nursing homes.

SUBMITTER: Jackson SS

PROVIDER: S-EPMC6676947 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Nursing home residents are frequently colonized with various strains of methicillin-resistant Staphylococcus aureus (MRSA) but the intra-facility dynamics of strain-specific MRSA remains poorly understood. We aimed at identifying and quantifying the associations between acquisition and carriage of MRSA strains and their potential risk factors in community nursing homes using mathematical modeling.The data was collected during a longitudinal MRSA surveillance study in six nursing homes in South Central Wisconsin. MRSA cultures were obtained from subjects every 3 months for up to one year. MRSA isolates were subsequently strain-typed by pulsed-field gel electrophoresis (PFGE), and their genetic similarity was established based on the Dice coefficients. Bayesian network analysis, logistic regression and elastic net were used to quantify the associations between acquisition and carriage of MRSA strains discriminated at 80% and 95% strain similarity thresholds and potentially modifiable resident characteristics including previous antibiotic exposure, comorbidity, medical devices, chronic wounds, functional and cognitive status and recent hospitalizations.Absence of severe cognitive impairment as well as presence of a wound, device and severe comorbidity was associated with elevated probability of USA100 carriage although there was a variation based on the combination of those risk factors. Residents with severe comorbidity and cognitive status and presence of device and wound were identified as certain carriers of USA100 in our sample. Residents with a chronic wound were more likely to carry USA100 MRSA (OR?=?2.77, 95% CI?=?1.37-5.87). Functional status was identified as an important determinant of carriage of USA100 and USA300 strains. Comorbidity and cognitive status were the two factors associated with carriage of all clonal groups in the study (USA100, USA300 and USA1200).The combination of Bayesian network analysis, logistic regression and elastic net can be used to identify associations between acquisition and carriage of MRSA strains and their potential risk factors in the face of scarce data. The revealed associations may be used to generate hypothesis for further study of determinants of acquisition and carriage of selected MRSA subtypes and to better inform infection control efforts in community nursing homes.

Project description:Methicillin-resistant Staphylococcus aureus (MRSA) is a leading cause of health care-associated (HA) and community-associated (CA) infections. USA300 strains are historically CA-MRSA, while USA100 strains are HA-MRSA. Here, we update an antibiotic prediction rule to distinguish these two genotypes based on antibiotic resistance phenotype using whole-genome sequencing (WGS), a more discriminatory methodology than pulsed-field gel electrophoresis (PFGE). MRSA clinical isolates collected from 2007 to 2017 underwent WGS; associated epidemiologic data were ascertained. In developing the rule, we examined MRSA isolates that included a population with a history of incarceration. Performance characteristics of antibiotic susceptibility for predicting USA300 compared to USA100, as defined by WGS, were examined. Phylogenetic analysis was performed to examine resistant USA300 clades. We identified 275 isolates (221 USA300, 54 USA100). Combination susceptibility to clindamycin or levofloxacin performed the best overall (sensitivity 80.7%, specificity 75.9%) to identify USA300. The average number of antibiotic classes with resistance was higher for USA100 (3 versus 2, P < 0.001). Resistance to ≤2 classes was predictive for USA300 (area under the curve (AUC) 0.84, 95% confidence interval 0.78 to 0.90). Phylogenetic analysis identified a cluster of USA300 strains characterized by increased resistance among incarcerated individuals. Using a combination of clindamycin or levofloxacin susceptibility, or resistance to ≤2 antibiotic classes, was predictive of USA300 as defined by WGS. Increased resistance was observed among individuals with incarceration exposure, suggesting circulation of a more resistant USA300 clade among at-risk community networks. Our phenotypic prediction rule could be used as an epidemiologic tool to describe community and nosocomial shifts in USA300 MRSA and quickly identify emergence of lineages with increased resistance. IMPORTANCE Methicillin-resistant Staphylococcus aureus (MRSA) is an important cause of health care-associated (HA) and community-associated (CA) infections, but the epidemiology of these strains (USA100 and USA300, respectively) now overlaps in health care settings. Although sequencing technology has become more available, many health care facilities still lack the capabilities to perform these analyses. In this study, we update a simple prediction rule based on antibiotic resistance phenotype with integration of whole-genome sequencing (WGS) to predict strain type based on antibiotic resistance profiles that can be used in settings without access to molecular strain typing methods. This prediction rule has many potential epidemiologic applications, such as analysis of retrospective data sets, regional monitoring, and ongoing surveillance of CA-MRSA infection trends. We demonstrate application of this rule to identify an emerging USA300 strain with increased antibiotic resistance among incarcerated individuals that deviates from the rule.

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Development and Validation of a Clinical Prediction Rule to Predict Transmission of Methicillin-Resistant Staphylococcus aureus in Nursing Homes.

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