Carbapenemase-producing Enterobacteriaceae Infections Clinical Trial
Official title:
Cost-effectiveness of Strategies to Control the Spread of Carbapenemase-producing Enterobacteriaceae (CPE) in Hospitals
Spread of extensively drug-resistant bacteria (XDR) such as carbapenemase-producing Enterobacterales (CPE) is a major public health problem. Various prevention and control interventions are implemented to limit the transmission of XDR but they are expensive and often disrupt hospital organization. In this study, a mathematical model will be used to study the effectiveness and cost-effectiveness of control strategies to limit the spread of CPE in a general hospital ward. A case-control study will be conducted to estimate the excess length of stay and mortality attributable to CPE colonisation or infection.
Carbapenemase-producing Enterobacterales (CPE) are increasingly common in hospitals and represent a serious health problem. These multidrug-resistant organisms colonise the gastrointestinal tract after direct (person-to-person) or indirect (via contaminated surfaces) transmission and are a common cause of urinary tract infections, ventilator-associated pneumonia and bloodstream infections in healthcare settings. Treatment options for patients infected with CPE are limited leading to high mortality, increased length of hospital stay and hospital costs. The successful implementation of cost-effective infection control measures to prevent CPE spread and infections is key for infection control preventionists and hospital managers. Recommendations to limit the transmission of CPE in healthcare facilities are based on the early detection of asymptomatic carriers, implementation of contact precautions and isolation in single room. In practice, strategies combining various interventions are employed according to the risk assessment and available resources: 1) universal or targeted rectal screening on admission, 2) standard precautions (SP), applied to all patients regardless of their infectious status, 3) contact precautions (CP) for identified carriers or infected patients, 4) isolation in a single room, 5) environmental cleaning, 6) rectal screening of contact patients, i.e. those whose care was provided by the same team as the CPE patient, and/or 7) isolation of carriers in a dedicated area with dedicated nursing staff (DNS), thereafter designated as cohorting. However, these control measures pose challenges such as the high cost of patient's screening and cohorting, requirement for single room isolation or staff shortage for implementing precautions. Moreover, the effectiveness and cost-effectiveness of various CPE control strategies is under-documented. OBJECTIVES: Primary: To evaluate the cost-effectiveness of different strategies combining screening and contact precaution measures to control the spread of CPE in a general medicine ward (GW) using a mathematical model. Secondary: - To assess the impact of CPE colonisation/ infection on patient's outcome (mortality, length of stay) - To analyse the resources used to manage CPE cases - To analyse the care pathway of a CPE case and study whether carrying the CPE represents the opportunity loss for the patient, METHODS AND TOOLS: A dynamic, stochastic model simulates the transmission of CPE by the hands of healthcare workers (HCWs) and the environment in a hypothetical GW. Input parameters are based on data from APHP hospitals and from the literature. 12 strategies are compared to the baseline (no control) and combined different prevention and control interventions: targeted or universal screening at admission (TS or US), contact precautions (CP), isolation in a single room, dedicated nursing staff (DNS) for carriers and weekly screening of contact patients (WS). Time horizon is one year. A hospital perspective is adopted to estimate costs, which included laboratory costs, single room, contact precautions, staff time, i.e. infection control nurse and/or nurses for cohorting, and lost bed-days due to prolonged hospital stay of identified carriers. A case-control study will be conducted to assess the impact of CPE colonization / infection on patient's outcomes. Cases will be the patients newly identified as CPE colonized or infected during the first semester 2016 in the APHP hospital network, i.e. around 450 cases. In the case of hospital CPE outbreak, index and secondary cases will be included. Cases will be followed until discharge from the hospital setting, including rehabilitation or home care. Since all discharges in France, either from public or private HCFs, are registered in the single national hospital discharge database PMSI (HDD), it is possible to link all hospital stays and retrieve the entire hospital course. Controls will be selected in the national HDD on a 1:1 ratio, based on the following matching criteria: absence of known colonization with CPE, age, gender, hospital stay in the same unit as the corresponding case during the 1st semester 2016, same DRG and duration of hospital stays at least as long at the time to colonization in the case. The following information will be collected in cases and controls: - demographic data, country of born (and if so date of entry in France), - Charlson comorbidity score, McCabe score, activity of daily living (ADL) score at discharge from the index hospital stay, - main and secondary DRG in the HDD system, - socioeconomic status, estimated by the address of living (IRIS score), type of registration in the national insurance security, - origin at hospital admission, whether the case is an index or secondary CPE colonized/infected, - whether the case was only colonized or infected, type of Enterobacteriaceae carrying the resistance gene, and type of resistance gene - control measures implemented for controlling the episode: contact precautions, reinforced personnel, dedicated personnel, interruption of admission and transfers - activity of the unit of first admission: number of bed, yearly number of admissions and hospital days A cost-effectiveness analysis will be undertaken to find the most favourable control strategies from the model simulations. Sensitivity analysis will identify the factors that have the greatest impact on the results. ;