Description and validation of a new automated surveillance system for Clostridium difficile in Denmark

Automated surveillance systems for healthcare-associated infections: results from a European survey and experiences from real-life utilization

BACKGROUND

As most automated surveillance (AS) methods to detect healthcare-associated infections (HAIs) have been developed and implemented in research settings, information about the feasibility of large-scale implementation is scarce.

AIM

We aimed to describe key aspects of the design of AS systems and implementation in European institutions and hospitals.

METHODS

An online survey was distributed via email in February/March 2019 among 1) PRAISE (Providing a Roadmap for Automated Infection Surveillance in Europe) network members; 2) corresponding authors of peer-reviewed European publications on existing AS systems; and 3) the mailing list of national infection prevention and control focal points of the European Centre for Disease Prevention and Control. Three AS systems from the survey were selected, based on quintessential features, for in-depth review focusing on implementation in practice.

FINDINGS

Through the survey and the review of three selected AS systems, notable differences regarding the methods, algorithms, data sources and targeted HAIs were identified. The majority of AS systems used a classification algorithm for semi-automated surveillance and targeted HAIs were mostly surgical site infections, urinary tract infections, sepsis or other bloodstream infections. AS systems yielded a reduction of workload for hospital staff. Principal barriers of implementation were strict data security regulations as well as creating and maintaining an information technology infrastructure.

CONCLUSION

AS in Europe is characterized by heterogeneity in methods and surveillance targets. To allow for comparisons and encourage homogenization, future publications on AS systems should provide detailed information on source data, methods and the state of implementation.

The accuracy of fully automated algorithms for surveillance of healthcare-onset Clostridioides difficile infections in hospitalized patients

We developed and validated a set of fully automated surveillance algorithms for healthcare-onset CDI using electronic health records. In a validation data set of 750 manually annotated admissions, the algorithm based on International Classification of Disease, Tenth Revision (ICD-10) code A04.7 had insufficient sensitivity. Algorithms based on microbiological test results with or without addition of symptoms performed well.

Automated digital reporting of clinical laboratory information to national public health surveillance systems, results of a EU/EEA survey, 2018

BackgroundTimely reporting of microbiology test results is essential for infection management. Automated, machine-to-machine (M2M) reporting of diagnostic and antimicrobial resistance (AMR) data from laboratory information management systems (LIMS) to public health agencies improves timeliness and completeness of communicable disease surveillance.AimWe surveyed microbiology data reporting practices for national surveillance of EU-notifiable diseases in European Union/European Economic Area (EU/EEA) countries in 2018.MethodsEuropean Centre for Disease Prevention and Control (ECDC) National Microbiology and Surveillance Focal Points completed a questionnaire on the modalities and scope of clinical microbiology laboratory data reporting.ResultsComplete data were provided for all 30 EU/EEA countries. Clinical laboratories used a LIMS in 28 countries. LIMS data on EU-notifiable diseases and AMR were M2M-reported to the national level in 14 and nine countries, respectively. In the 14 countries, associated demographic data reported allowed the de-duplication of patient reports. In 13 countries, M2M-reported data were used for cluster detection at the national level. M2M laboratory data reporting had been validated against conventional surveillance methods in six countries, and replaced those in five. Barriers to M2M reporting included lack of information technology support and financial incentives.ConclusionM2M-reported laboratory data were used for national public health surveillance and alert purposes in nearly half of the EU/EEA countries in 2018. Reported data on infectious diseases and AMR varied in extent and disease coverage across countries and laboratories. Improving automated laboratory-based surveillance will depend on financial and regulatory incentives, and harmonisation of health information and communication systems.

The Economic Burden of Clostridioides difficile in Denmark: A Retrospective Cohort Study

Objectives: The aim of this study was to make a comprehensive economic assessment of the costs of hospital-acquired C. difficile infections (CDI).

Methods: We carried out a retrospective matched cohort study utilizing Danish registry data with national coverage to identify CDI cases and matched reference patients without CDI (controls) for economic burden assessment in Denmark covering 2011–2014. Health care costs and public transfer costs were obtained from national registries, and calculated for 1 year prior to, and 2 years after index admission using descriptive statistics and regression analysis.

Results: The study included 12,768 CDI patients and 23,272 matched controls. The total health care cost was significantly larger for CDI cases than controls throughout all periods. During the index admission period, cost was €12,867 per CDI case compared to €4,522 (p < 0.001) for controls, which increased to an average of €31,388 and €19,512 (p < 0.001) in Year 1 for the two groups, respectively. Excess costs were found both among infections with onset in hospitals and in the community. Diagnosis compatible with complications increased costs to on average >€91,000 per case. The regression analysis showed that CDI adds a substantial economic burden, but only explains about 1/3 of the crude difference observed in the matched analysis.

Discussion: The major economic impact of hospital-acquired CDI with complications underlines the importance of preventing complications in these patients. Our study provides an informed estimate of the potential economic gain per patient by successful intervention, which is likely to be relatively comparable across countries.

Incidence Rates and Risk Factors of Clostridioides difficile Infection in Solid Organ and Hematopoietic Stem Cell Transplant Recipients

Background

Transplant recipients are an immunologically vulnerable patient group and are at elevated risk of Clostridioides difficile infection (CDI) compared with other hospitalized populations. However, risk factors for CDI post-transplant are not fully understood.

Methods

Adults undergoing solid organ (SOT) and hematopoietic stem cell transplant (HSCT) from January 2010 to February 2017 at Rigshospitalet, University of Copenhagen, Denmark, were retrospectively included. Using nationwide data capture of all CDI cases, the incidence and risk factors of CDI were assessed.

Results

A total of 1687 patients underwent SOT or HSCT (1114 and 573, respectively), with a median follow-up time (interquartile range) of 1.95 (0.52–4.11) years. CDI was diagnosed in 15% (164) and 20% (114) of the SOT and HSCT recipients, respectively. CDI rates were highest in the 30 days post-transplant for both SOT and HSCT (adjusted incidence rate ratio [aIRR], 6.64; 95% confidence interval [CI], 4.37–10.10; and aIRR, 2.85; 95% CI, 1.83–4.43, respectively, compared with 31–180 days). For SOT recipients, pretransplant CDI and liver and lung transplant were associated with a higher risk of CDI in the first 30 days post-transplant, whereas age and liver transplant were risk factors in the later period. Among HSCT recipients, myeloablative conditioning and a higher Charlson Comorbidity Index were associated with a higher risk of CDI in the early period but not in the late period.

Conclusions

Using nationwide data, we show a high incidence of CDI among transplant recipients. Importantly, we also find that risk factors can vary relative to time post-transplant.