Impact of inter-hospital transfers on the prevalence of resistant pathogens in a hospital-community system

Incidence, risk factors and clinical outcome of multidrug-resistant organisms after heart transplantation

BACKGROUND

Transplant recipients commonly harbor multidrug-resistant organisms (MDROs), as a result of frequent hospital admissions and increased exposure to antimicrobials and invasive procedures.

AIM

To investigate the impact of patient demographic and clinical characteristics on MDRO acquisition, as well as the impact of MDRO acquisition on intensive care unit (ICU) and hospital length of stay, and on ICU mortality and 1-year mortality post heart transplantation.

METHODS

This retrospective cohort study analyzed 98 consecutive heart transplant patients over a ten-year period (2013-2022) in a single transplantation center. Data was collected regarding MDROs commonly encountered in critical care.

RESULTS

Among the 98 transplanted patients (70% male), about a third (32%) acquired or already harbored MDROs upon transplantation (MDRO group), while two thirds did not (MDRO-free group). The prevalent MDROs were Acinetobacter baumannii (14%), Pseudomonas aeruginosa (12%) and Klebsiella pneumoniae (11%). Compared to MDRO-free patients, the MDRO group was characterized by higher body mass index (P = 0.002), higher rates of renal failure (P = 0.017), primary graft dysfunction (10% vs 4.5%, P = 0.001), surgical re-exploration (34% vs 14%, P = 0.017), mechanical circulatory support (47% vs 26% P = 0.037) and renal replacement therapy (28% vs 9%, P = 0.014), as well as longer extracorporeal circulation time (median 210 vs 161 min, P = 0.003). The median length of stay was longer in the MDRO group, namely ICU stay was 16 vs 9 d in the MDRO-free group (P = 0.001), and hospital stay was 38 vs 28 d (P = 0.006), while 1-year mortality was higher (28% vs 7.6%, log-rank-χ 2: 7.34).

CONCLUSION

Following heart transplantation, a predominance of Gram-negative MDROs was noted. MDRO acquisition was associated with higher complication rates, prolonged ICU and total hospital stay, and higher post-transplantation mortality.

Hub and Spoke: Next level in regional networks for infection prevention

The threat of multidrug-resistant organisms (MDROs) and antimicrobial resistance (AMR) are real and increasing every day. They affect not only healthcare systems but also communities, causing economic and public health concerns. Governments must take action to tackle AMR and prevent the spread of MDROs and regional hubs have a critical role to play in achieving this outcome. Furthermore, bacteria have no borders, consequently, cooperation networks should be extended between countries as a crucial strategy for achieving the success of infection control. Euregions, which are a specific form of cooperation between local authorities of two or more bordering European countries, can help solve common problems and improve the lives of people living on both sides of the border. Regional collaboration strategies can enhance infection control and build resilience against antimicrobial resistance. This review identifies risk factors and the correct approaches to infection prevention and control, including education and awareness programs for healthcare professionals, appropriate prescribing practices, and infection prevention control measures. These measures can help reduce the incidence of antimicrobial resistance in the region and save lives. It is therefore essential to take concrete actions and foster the creation of more effective regional and cross-border centers to ensure the success of infection control policies and the management of healthcare-associated infections. This work sheds light on the issue of MDRO infections within healthcare settings, while also acknowledging the crucial role of the One Health concept in understanding the broader context of these infections. By recognizing the interdependence of human and animal health and the environment, we can take constructive steps toward mitigating the risks of these infections and promoting better health outcomes for all.

Regional patient transfer patterns matter for the spread of hospital-acquired pathogens

Pathogens typically responsible for hospital-acquired infections (HAIs) constitute a major threat to healthcare systems worldwide. They spread via hospital (or hospital-community) networks by readmissions or patient transfers. Therefore, knowledge of these networks is essential to develop and test strategies to mitigate and control the HAI spread. Until now, no methods for comparing healthcare networks across different systems were proposed. Based on healthcare insurance data from four German federal states (Bavaria, Lower Saxony, Saxony and Thuringia), we constructed hospital networks and compared them in a systematic approach regarding population, hospital characteristics, and patient transfer patterns. Direct patient transfers between hospitals had only a limited impact on HAI spread. Whereas, with low colonization clearance rates, readmissions to the same hospitals posed the biggest transmission risk of all inter-hospital transfers. We then generated hospital-community networks, in which patients either stay in communities or in hospitals. We found that network characteristics affect the final prevalence and the time to reach it. However, depending on the characteristics of the pathogen (colonization clearance rate and transmission rate or even the relationship between transmission rate in hospitals and in the community), the studied networks performed differently. The differences were not large, but justify further studies.

Transmission of drug-resistant bacteria in a hospital-community model stratified by patient risk

A susceptible-infectious-susceptible (SIS) model for simulating healthcare-acquired infection spread within a hospital and associated community is proposed. The model accounts for the stratification of in-patients into two susceptibility-based risk groups. The model is formulated as a system of first-order ordinary differential equations (ODEs) with appropriate initial conditions. The mathematical analysis of this system is demonstrated. It is shown that the system has unique global solutions, which are bounded and non-negative. The basic reproduction number ([Formula: see text]) for the considered model is derived. The existence and the stability of the stationary solutions are analysed. The disease-free stationary solution is always present and is globally asymptotically stable for [Formula: see text], while for [Formula: see text] it is unstable. The presence of an endemic stationary solution depends on the model parameters and when it exists, it is globally asymptotically stable. The endemic state encompasses both risk groups. The endemic state within only one group only is not possible. In addition, for [Formula: see text] a forward bifurcation takes place. Numerical simulations, based on the anonymised insurance data, are also presented to illustrate theoretical results.

The effect of re-directed patient flow in combination with targeted infection control measures on the spread of multi-drug-resistant Enterobacteriaceae in the German health-care system: a mathematical modelling approach

OBJECTIVE

The introduction of multi-drug-resistant Enterobacteriaceae (MDR-E) by colonized patients transferred from high-prevalence countries has led to several large outbreaks of MDR-E in low-prevalence countries, with the risk of propagated spread to the community. The goal of this study was to derive a strategy to counteract the spread of MDR-E at the regional health-care network level.

METHODS

We used a hybrid ordinary differential equation and network model built based on German health insurance data to evaluate whether the re-direction of patient flow in combination with targeted infection control measures can counteract the spread of MDR-E in the German health-care system. We applied pragmatic re-direction strategies focusing on a reduced choice of hospitals for subsequent stays after initial hospitalization but not manipulating direct transfers because these are most likely determined by medical needs.

RESULTS

The re-direction strategies alone did not reduce the system-wide spread of MDR-E (system-wide prevalence of MDR-E is 18.7% vs. 25.7%/29.9%). In contrast, targeted hospital-based infection control measures restricted to institutions with the highest institutional basic reproduction numbers in the network were identified as an effective tool for reducing system-wide prevalence (system-wide prevalence of MDR-E is 18.7% vs. 9.3%). If these measures were applied to the top one-third of hospitals, the system-wide prevalence could be reduced by approximately 80% (system-wide prevalence of 18.7% vs. 3.5% for one-third of patients subjected to interventions). A combination of this hospital-based intervention and patient re-direction strategies could not improve the effectiveness of the hospital-based approach (system-wide prevalence of MDR-E is 9.3% vs. 14.2%/14.3%).

CONCLUSIONS

The pragmatic patient re-direction strategies were not capable of restricting the spread of MDR-E in a simulation of the German health-care system; in contrast, hospital-based interventions focusing on institutions identified based on network transmission patterns seem to be a promising approach for sustainable reduction of the spread of MDR-E through the German population.

Effects of patient-level risk factors, departmental allocation and seasonality on intrahospital patient transfer patterns: network analysis applied on a Norwegian single-centre data set

OBJECTIVES

Describe patient transfer patterns within a large Norwegian hospital. Identify risk factors associated with a high number of transfers. Develop methods to monitor intrahospital patient flows to support capacity management and infection control.

DESIGN

Retrospective observational study of linked clinical data from electronic health records.

SETTING

Tertiary care university hospital in the Greater Oslo Region, Norway.

PARTICIPANTS

All adult (≥18 years old) admissions to the gastroenterology, gastrointestinal surgery, neurology and orthopaedics departments at Akershus University Hospital, June 2018 to May 2019.

METHODS

Network analysis and graph theory. Poisson regression analysis.

OUTCOME MEASURES

Primary outcome was network characteristics at the departmental level. We describe location-to-location transfers using unweighted, undirected networks for a full-year study period. Weekly networks reveal changes in network size, density and key categories of transfers over time. Secondary outcome was transfer trajectories at the individual patient level. We describe the distribution of transfer trajectories in the cohort and associate number of transfers with patient clinical characteristics.

RESULTS

The cohort comprised 17 198 hospital stays. Network analysis demonstrated marked heterogeneity across departments and throughout the year. The orthopaedics department had the largest transfer network size and density and greatest temporal variation. More transfers occurred during weekdays than weekends. Summer holiday affected transfers of different types (Emergency department-Any location/Bed ward-Bed ward/To-From Technical wards) differently. Over 75% of transferred patients followed one of 20 common intrahospital trajectories, involving one to three transfers. Higher number of intrahospital transfers was associated with emergency admission (transfer rate ratio (RR)=1.827), non-prophylactic antibiotics (RR=1.108), surgical procedure (RR=2.939) and stay in intensive care unit or high-dependency unit (RR=2.098). Additionally, gastrosurgical (RR=1.211), orthopaedic (RR=1.295) and neurological (RR=1.114) patients had higher risk of many transfers than gastroenterology patients (all effects: p<0.001).

CONCLUSIONS

Network and transfer chain analysis applied on patient location data revealed logistic and clinical associations highly relevant for hospital capacity management and infection control.

Relevance of intra-hospital patient movements for the spread of healthcare-associated infections within hospitals - a mathematical modeling study

The aim of this study is to analyze patient movement patterns between hospital departments to derive the underlying intra-hospital movement network, and to assess if movement patterns differ between patients at high or low risk of colonization. For that purpose, we analyzed patient electronic medical record data from five hospitals to extract information on risk stratification and patient intra-hospital movements. Movement patterns were visualized as networks, and network centrality measures were calculated. Next, using an agent-based model where agents represent patients and intra-hospital patient movements were explicitly modeled, we simulated the spread of multidrug resistant enterobacteriacae (MDR-E) inside a hospital. Risk stratification of patients according to certain ICD-10 codes revealed that length of stay, patient age, and mean number of movements per admission were higher in the high-risk groups. Movement networks in all hospitals displayed a high variability among departments concerning their network centrality and connectedness with a few highly connected departments and many weakly connected peripheral departments. Simulating the spread of a pathogen in one hospital network showed positive correlation between department prevalence and network centrality measures. This study highlights the importance of intra-hospital patient movements and their possible impact on pathogen spread. Targeting interventions to departments of higher (weighted) degree may help to control the spread of MDR-E. Moreover, when the colonization status of patients coming from different departments is unknown, a ranking system based on department centralities may be used to design more effective interventions that mitigate pathogen spread.

Pathogens including multidrug resistant enterobacteriacae (MDR-E) inside hospital settings are associated with higher morbidity, mortality, and healthcare costs. Better understanding of the transmission routes of these pathogens is required to develop targeted and efficient measures to contain the spread of MDR-E in a hospital. We analyzed datasets from five hospitals in different countries to explore patient movement patterns between departments of these hospitals (intra-hospital movements). We assessed whether movement patterns differ between patients at high or low risk of colonization. Our results show that in every intra-hospital network, there exist a few departments which are strongly connected and many peripheral departments which are loosely connected. High-risk patients stay on average longer in the hospital, and move more frequently between departments than low-risk patients. Targeting interventions to strongly connected departments may help to reduce pathogen spread inside the hospital. To explore this, we simulated the spread of MDR-E inside one hospital using an agent-based model that includes patient movements. In the simulations, we found positive correlations between departments’ prevalence and network characteristics such as degree and weighted degree, thus highlighting the importance of targeting interventions to departments of higher (weighted) degree to control the spread of MDR-E.