Not just a matter of size: a hospital-level risk factor analysis of MRSA bacteraemia in Scotland

Preexisting Atrial Fibrillation Associated with Higher Mortality in Patients with Methicillin-Resistant Staphylococcus aureus Bloodstream Infections: Analysis of the National Inpatient Sample

Background

The purpose of this study was to investigate the prevalence of preexisting atrial fibrillation (AF) in patients with MRSA-BSI during a three-year period and the impact of preexisting AF on MRSA-BSI outcomes.

Methods

This was a retrospective analysis performed using the National Inpatient Sample (NIS) over a three-year period (2016, 2017, 2018) for patients with MRSA-BSI as a principal diagnosis with and without preexisting AF. The primary outcome was mortality with secondary outcomes of acute coronary syndrome, cardiac arrest, cardiogenic shock, endocarditis, respiratory failure, acute kidney injury, length of stay, hospital cost, and patient charge. A univariate and multivariable logistic regression analysis estimated clinical outcomes.

Results

Preexisting AF in patients with MRSA-BSI significantly increased the primary outcome of the study, mortality (1.67% vs. 0.66%, p=0.030) with an adjusted odds ratio (AOR) of 1.98 (95% CI, 1.1–3.7). Secondary outcomes showed increased likelihood of cardiac arrest with MRSA-BSI and AF (0.48% vs. 0.2%, p=0.025) with an AOR 3.59 (CI 1.18–11.0), ACS (3.44% vs. 1.21%, p=0.008) with an AOR of 1.81 (CI 1.16–2.80), respiratory failure (8.92% vs. 4.02%, p=0.045) with an AOR 1.39 (CI 1.01–1.91), prolonged LOS (6.4 vs. 5.4 days, p=0.0001), increased hospital cost ($13,374 vs. $11,401, p=0.0001), and increased overall patient charge ($50,091 vs. $43,018, p=0.0001). Secondary outcomes that showed statistical significance included ACS (1,497 (3.44%) vs. 113 (1.21%), p=0.008) with an AOR of 1.81 (CI 1.16–2.80), cardiac arrest (209 (0.48%) vs. 19 (0.2%), p=0.025) with an AOR 3.59 (CI 1.18–11.0), and respiratory failure (3,881 (8.92%) vs. 374 (4.02%), p=0.045 with an AOR 1.39 (CI 1.01–1.91).

Conclusions

Preexisting AF is a significant risk factor for mortality in patients admitted for MRSA-BSI and increases risk for cardiac arrest, respiratory failure, and ACS. Considerations should focus on early treatment and source control, especially with AF given the mortality risk, increased hospitalization cost, and prolonged LOS.

One Health in hospitals: how understanding the dynamics of people, animals, and the hospital built-environment can be used to better inform interventions for antimicrobial-resistant gram-positive infections

Despite improvements in hospital infection prevention and control, healthcare associated infections (HAIs) remain a challenge with significant patient morbidity, mortality, and cost for the healthcare system. In this review, we use a One Health framework (human, animal, and environmental health) to explain the epidemiology, demonstrate key knowledge gaps in infection prevention policy, and explore improvements to control Gram-positive pathogens in the healthcare environment. We discuss patient and healthcare worker interactions with the hospital environment that can lead to transmission of the most common Gram-positive hospital pathogens – methicillin-resistant Staphylococcus aureus, Clostridioides (Clostridium) difficile, and vancomycin-resistant Enterococcus – and detail interventions that target these two One Health domains. We discuss the role of animals in the healthcare settings, knowledge gaps regarding their role in pathogen transmission, and the absence of infection risk mitigation strategies targeting animals. We advocate for novel infection prevention and control programs, founded on the pillars of One Health, to reduce Gram-positive hospital-associated pathogen transmission.

Patient- and hospital-level predictors of vancomycin-resistant Enterococcus (VRE) bacteremia in Ontario, Canada

BACKGROUND

Data are limited on risk factors for vancomycin-resistant Enterococcus (VRE) bacteremia.

METHODS

All patients with a confirmed VRE bacteremia in Ontario, Canada, between January 2009 and December 2013 were linked to provincial healthcare administrative data sources and frequency matched to 3 controls based on age, sex, and aggregated diagnosis group. Associations between predictors and VRE bacteremia were estimated by generalized estimating equations and summarized using odds ratios (ORs) and corresponding 95% confidence intervals (CIs).

RESULTS

In total, 217 cases and 651 controls were examined. In adjusted analyses, patient-level predictors included bone marrow transplant (OR 106.99 [95% CI 12.19-939.26]); solid organ transplant (OR 17.17 [95% CI 4.95-59.54]); any cancer (OR 8.64 [95% CI 3.88-19.21]); intensive care unit (ICU) admission (OR 6.81 [95% CI 3.53-13.13]); heart disease (OR 5.27 [95% CI 2.00-13.90]); and longer length of stay (OR 1.07 per day [95% CI 1.06-1.09]). Hospital-level predictors included hospital size (per increase in 100 beds (OR 1.26 [95% CI 1.07-1.48]) and teaching hospitals (OR 3.87 [95% CI 1.85-8.08]).

CONCLUSIONS

Patients with a bone marrow transplant, solid organ transplant, cancer, or who are admitted to the ICU are at highest risk of VRE bacteremia, particularly at large hospitals and teaching hospitals.

Applications of network analysis to routinely collected health care data: a systematic review

Objective

To survey network analyses of datasets collected in the course of routine operations in health care settings and identify driving questions, methods, needs, and potential for future research.

Materials and Methods

A search strategy was designed to find studies that applied network analysis to routinely collected health care datasets and was adapted to 3 bibliographic databases. The results were grouped according to a thematic analysis of their settings, objectives, data, and methods. Each group received a methodological synthesis.

Results

The search found 189 distinct studies reported before August 2016. We manually partitioned the sample into 4 groups, which investigated institutional exchange, physician collaboration, clinical co-occurrence, and workplace interaction networks. Several robust and ongoing research programs were discerned within (and sometimes across) the groups. Little interaction was observed between these programs, despite conceptual and methodological similarities.

Discussion

We use the literature sample to inform a discussion of good practice at this methodological interface, including the concordance of motivations, study design, data, and tools and the validation and standardization of techniques. We then highlight instances of positive feedback between methodological development and knowledge domains and assess the overall cohesion of the sample.

Spread of hospital-acquired infections: A comparison of healthcare networks

Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the potential scale of HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. Using an exhaustive database of all hospital discharge summaries in France in 2014, we construct and analyze three patient networks based on the following: transfers of patients with HAI (HAI-specific network); patients with suspected HAI (suspected-HAI network); and all patients (general network). All three networks have heterogeneous patient flow and demonstrate small-world and scale-free characteristics. Patient populations that comprise these networks are also heterogeneous in their movement patterns. Ranking of hospitals by centrality measures and comparing community clustering using community detection algorithms shows that despite the differences in patient population, the HAI-specific and suspected-HAI networks rely on the same underlying structure as that of the general network. As a result, the general network may be more reliable in studying potential spread of HAIs. Finally, we identify transfer patterns at both the French regional and departmental (county) levels that are important in the identification of key hospital centers, patient flow trajectories, and regional clusters that may serve as a basis for novel wide-scale infection control strategies.

Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the scale of potential HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. We construct and compare the characteristics of three different patient transfer networks based on data on transfers of patients with diagnosed HAIs, suspected HAIs, or of all patients. Our analyses show that these healthcare networks, the patient populations that comprise them and the patient movement patterns are heterogeneous and centralized. Despite the differences in patient populations, the HAI-specific and suspected-HAI healthcare networks have the same underlying structure as that of the general healthcare network. We identify key hospital centers, patient flow trajectories, at both the regional and department (county) level that may serve as a basis for proposing novel wide-scale infection control strategies.

Measuring distance through dense weighted networks: The case of hospital-associated pathogens

Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014–2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen. While the risk of transferring a hospital-associated pathogen between directly neighbouring hospitals is a direct reflection of the number of shared patients, the distance between two hospitals far-away in the network is determined largely by the number of intermediate hospitals in the network. Because the network is dense, most long distance transmission chains in fact involve only few intermediate steps, spreading along the many weak links. The dense connectivity of hospital networks, together with a strong regional structure, causes hospital-associated pathogens to spread from the initial outbreak in a two-step process: first, the directly surrounding hospitals are affected through the strong connections, second all other hospitals receive introductions through the multitude of weaker links. Although the strong connections matter for local spread, weak links in the network can offer ideal routes for hospital-associated pathogens to travel further faster. This hold important implications for infection prevention and control efforts: if a local outbreak is not controlled in time, colonised patients will appear in other regions, irrespective of the distance to the initial outbreak, making import screening ever more difficult.

Shared patients can spread hospital-associated pathogens between hospitals, together forming a large network in which all hospitals are connected. We set out to measure the distance between hospitals in such a network, best reflecting the risk of a hospital-associated pathogen spreading from one to the other. The central problem is that this risk may not be a directly reflected by the weight of the direct connections between hospitals, because the pathogen could arrive through a longer indirect route, first causing a problem in an intermediate hospital. We determined the optimal balance between connection weights and path length, by testing different weighting factors between them against simulated spread of a pathogen. We found that while strong connections are important risk factor for a hospital’s direct neighbours, weak connections offer ideal indirect routes for hospital-associated pathogens to travel further faster. These routes should not be underestimated when designing control strategies.

Population genetic structuring of methicillin-resistant Staphylococcus aureus clone EMRSA-15 within UK reflects patient referral patterns

Antibiotic resistance forms a serious threat to the health of hospitalised patients, rendering otherwise treatable bacterial infections potentially life-threatening. A thorough understanding of the mechanisms by which resistance spreads between patients in different hospitals is required in order to design effective control strategies. We measured the differences between bacterial populations of 52 hospitals in the United Kingdom and Ireland, using whole-genome sequences from 1085 MRSA clonal complex 22 isolates collected between 1998 and 2012. The genetic differences between bacterial populations were compared with the number of patients transferred between hospitals and their regional structure. The MRSA populations within single hospitals, regions and countries were genetically distinct from the rest of the bacterial population at each of these levels. Hospitals from the same patient referral regions showed more similar MRSA populations, as did hospitals sharing many patients. Furthermore, the bacterial populations from different time-periods within the same hospital were generally more similar to each other than contemporaneous bacterial populations from different hospitals. We conclude that, while a large part of the dispersal and expansion of MRSA takes place among patients seeking care in single hospitals, inter-hospital spread of resistant bacteria is by no means a rare occurrence. Hospitals are exposed to constant introductions of MRSA on a number of levels: (1) most MRSA is received from hospitals that directly transfer large numbers of patients, while (2) fewer introductions happen between regions or (3) across national borders, reflecting lower numbers of transferred patients. A joint coordinated control effort between hospitals, is therefore paramount for the national control of MRSA, antibiotic-resistant bacteria and other hospital-associated pathogens.