Hospital-acquired infections--appropriate statistical treatment is urgently needed!

Incidence and potential risk factors of human cytomegalovirus infection in patients with severe and critical coronavirus disease 2019

BACKGROUND

Human cytomegalovirus (HCMV) infection occurs in immunosuppressed individuals and is known to increase mortality. Patients with coronavirus disease 2019 (COVID-19) are often treated with steroids, require intensive care unit (ICU) treatment, and may therefore be at risk for HCMV infection. However, which factors predispose severely ill patients with COVID-19 to HCMV infection and the prognostic value of such infections remain largely unexplored. This study aimed to examine the incidence and potential risk factors of HCMV infection in patients with severe or critical COVID-19 and evaluate the relationship between HCMV infection and mortality.

METHODS AND FINDINGS

We used administrative claims data from advanced treatment hospitals in Japan to identify and analyze patients with severe or critical COVID-19. We explored potential risk factors for HCMV infection using multivariable regression models and its contribution to mortality in patients with COVID-19. Overall, 33,151 patients who progressed to severe or critical COVID-19 illness were identified. The incidence of HCMV infection was 0.3-1.7 % depending on the definition of HCMV infection. Steroids, immunosuppressants, ICU admission, and blood transfusion were strongly associated with HCMV infection. Furthermore, HCMV infection was associated with patient mortality independent of the observed risk factors for death.

CONCLUSIONS

HCMV infection is a notable complication in patients with severe or critical COVID-19 who are admitted to the ICU or receive steroids, immunosuppressants, and blood transfusion and can significantly increase mortality risk.

Acute kidney injury in Staphylococcus aureus bacteremia: a recurrent events analysis

OBJECTIVES

To estimate risk factors for AKI and the effect of AKI on mortality in Staphylococcus aureus bacteremia, while taking into account recurrent AKI episodes, competing risks, time-varying variables and time-varying effects.

METHODS

We performed an unplanned analysis using data from a multicenter cohort study of patients with SAB. Primary outcome was cumulative incidence of AKI, according to KDIGO definitions.

RESULTS

We included 453 patients in this study of whom 194 (43%) patients experienced one or more AKI episodes. Age (HR 1.013, 95% CI 1.001 - 1.024), Charlson comorbidity index (HR 1.07, 95% CI 1.01 - 1.14), prior chronic kidney disease (HR 1.76, 95% CI 1.28 - 2.42), septic shock (HR 3.28, 95% CI 2.31 - 4.66), persistent bacteremia (HR 1.53, 95% CI 1.08 - 2.17) and vancomycin therapy (HR 1.80, 95% CI 1.05 - 3.09) were independently associated with AKI, but flucloxacillin, cefazolin, rifampicin and aminoglycoside therapy were not. After adjustment for confounders and immortal time bias, AKI was associated with an increased risk of 90-day mortality (HR 4.26, 95% CI 2.91 - 6.23).

CONCLUSIONS

Incidence of AKI in SAB is high and a substantial proportion of patients develops recurrent episodes of AKI after recovery. AKI is specifically linked to the use of vancomycin and not to anti-staphylococcal penicillins. Clinical outcome of patients with SAB complicated by AKI is worse than previously estimated.

Investigation of multiple nosocomial infections using a semi-Markov multi-state model

BACKGROUND

The prevalence of multiple nosocomial infections (MNIs) is on the rise, however, there remains a limited comprehension regarding the associated risk factors, cumulative risk, probability of occurrence, and impact on length of stay (LOS).

METHOD

This multicenter study includes all hospitalized patients from 2020 to July 2023 in two sub-hospitals of a tertiary hospital in Guangming District, Shenzhen. The semi-Markov multi-state model (MSM) was utilized to analyze risk factors and cumulative risk of MNI, predict its occurrence probability, and calculate the extra LOS of nosocomial infection (NI).

RESULTS

The risk factors for MNI include age, community infection at admission, surgery, and combined use of antibiotics. However, the cumulative risk of MNI is lower than that of single nosocomial infection (SNI). MNI is most likely to occur within 14 days after admission. Additionally, SNI prolongs LOS by an average of 7.48 days (95% Confidence Interval, CI: 6.06-8.68 days), while MNI prolongs LOS by an average of 15.94 days (95% CI: 14.03-18.17 days). Furthermore, the more sites of infection there are, the longer the extra LOS will be.

CONCLUSION

The longer LOS and increased treatment difficulty of MNI result in a heavier disease burden for patients, necessitating targeted prevention and control measures.

Stacked probability plots of the extended illness-death model using constant transition hazards - an easy to use shiny app

BACKGROUND

Extended illness-death models (a specific class of multistate models) are a useful tool to analyse situations like hospital-acquired infections, ventilation-associated pneumonia, and transfers between hospitals. The main components of these models are hazard rates and transition probabilities. Calculation of different measures and their interpretation can be challenging due to their complexity.

METHODS

By assuming time-constant hazards, the complexity of these models becomes manageable and closed mathematical forms for transition probabilities can be derived. Using these forms, we created a tool in R to visualize transition probabilities via stacked probability plots.

RESULTS

In this article, we present this tool and give some insights into its theoretical background. Using published examples, we give guidelines on how this tool can be used. Our goal is to provide an instrument that helps obtain a deeper understanding of a complex multistate setting.

CONCLUSION

While multistate models (in particular extended illness-death models), can be highly complex, this tool can be used in studies to both understand assumptions, which have been made during planning and as a first step in analysing complex data structures. An online version of this tool can be found at https://eidm.imbi.uni-freiburg.de/ .

Incidence and outcomes of hospital-associated respiratory virus infections by viral species

BACKGROUND

Although the incidence of hospital-associated respiratory virus infection (HARVI) is well recognized, the risk factors for infection and impact on patient outcomes are not well characterized.

METHODS

We identified a cohort of all inpatient admissions ≥24 hours duration at a single academic medical center from 2017 to 2020. HARVI were defined as respiratory virus detected in a test ordered after the 95th percentile of the virus-specific incubation period. Risk factors for HARVI were assessed using Cox proportional hazards models of the competing outcomes of HARVI and discharge. The associations between time-varying HARVI status and the rates of ICU admission, discharge, and in-hospital death were estimated using Cox-proportional hazards models in a competing risk framework.

RESULTS

HARVI incidences were 8.8 and 3.0 per 10,000 admission days for pediatric and adult patients, respectively. For adults, congestive heart failure, renal disease, and cancer increased HARVI risk independent of their associations with length of stay. HARVI risk was also elevated for patients admitted in September-June relative to July admissions. For pediatric patients, cardiovascular and respiratory conditions, cancer, medical device dependence, and admission in December increased HARVI risk. Lengths of stay were longer for adults with HARVI compared to those without, and hospital-associated influenza A was associated with increased risk of death. Rates of ICU admission were increased in the 5 days after HARVI identification for adult and pediatric patients. HARVI was not associated with length of stay or death among pediatric patients.

CONCLUSIONS

HARVI is associated chronic health conditions and increases morbidity and mortality.

An agent-based model on antimicrobial de-escalation in intensive care units: Implications on clinical trial design

Antimicrobial de-escalation refers to reducing the spectrum of antibiotics used in treating bacterial infections. This strategy is widely recommended in many antimicrobial stewardship programs and is believed to reduce patients' exposure to broad-spectrum antibiotics and prevent resistance. However, the ecological benefits of de-escalation have not been universally observed in clinical studies. This paper conducts computer simulations to assess the ecological effects of de-escalation on the resistance prevalence of Pseudomonas aeruginosa-a frequent pathogen causing nosocomial infections. Synthetic data produced by the models are then used to estimate the sample size and study period needed to observe the predicted effects in clinical trials. Our results show that de-escalation can reduce colonization and infections caused by bacterial strains resistant to the empiric antibiotic, limit the use of broad-spectrum antibiotics, and avoid inappropriate empiric therapies. Further, we show that de-escalation could reduce the overall super-infection incidence, and this benefit becomes more evident under good compliance with hand hygiene protocols among health care workers. Finally, we find that any clinical study aiming to observe the essential effects of de-escalation should involve at least ten arms and last for four years-a size never attained in prior studies. This study explains the controversial findings of de-escalation in previous clinical studies and illustrates how mathematical models can inform outcome expectations and guide the design of clinical studies.

Dealing with time-dependent exposures and confounding when defining and estimating attributable fractions-Revisiting estimands and estimators

The population-attributable fraction (PAF) is commonly interpreted as the proportion of events that can be ascribed to a certain exposure in a certain population. Its estimation is sensitive to common forms of time-dependent bias in the face of a time-dependent exposure. Predominant estimation approaches based on multistate modeling fail to fully eliminate such bias and, as a result, do not permit a causal interpretation, even in the absence of confounding. While recently proposed multistate modeling approaches can successfully eliminate residual time-dependent bias, and moreover succeed to adjust for time-dependent confounding by means of inverse probability of censoring weighting, inadequate application, and misinterpretation prevails in the medical literature. In this paper, we therefore revisit recent work on previously proposed PAF estimands and estimators in settings with time-dependent exposures and competing events and extend this work in several ways. First, we critically revisit the interpretation and applied terminology of these estimands. Second, we further formalize the assumptions under which a causally interpretable PAF estimand can be identified and provide analogous weighting-based representations of the identifying functionals of other proposed estimands. This representation aims to enhance the applied statistician's understanding of different sources of bias that may arise when the aim is to obtain a valid estimate of a causally interpretable PAF. To illustrate and compare these representations, we present a real-life application to observational data from the Ghent University Hospital ICUs to estimate the fraction of ICU deaths attributable to hospital-acquired infections.

A stochastic dynamical model for nosocomial infections with co-circulation of sensitive and resistant bacterial strains

Nosocomial infections (hospital-acquired) has been an important public health problem, which may make those patients with infections or involved visitors and hospital personnel at higher risk of worse clinical outcomes or infection, and then consume more healthcare resources. Taking into account the stochasticity of the death and discharge rate of patients staying in hospitals, in this paper, we propose a stochastic dynamical model describing the transmission of nosocomial pathogens among patients admitted for hospital stays. The stochastic terms of the model are incorporated to capture the randomness arising from death and discharge processes of patients. Firstly, a sufficient condition is established for the stochastic extinction of disease. It shows that introducing randomness in the model will result in lower potential of nosocomial outbreaks. Further, we establish a threshold criterion on the existence of stationary distribution and ergodicity for any positive solution of the model. Particularly, the spectral radius form of stochastic threshold value is calculated in the special case. Moreover, the numerical simulations are conducted to both validate the theoretical results and investigate the effect of prevention and control strategies on the prevalence of nosocomial infection. We show that enhancing hygiene, targeting colonized and infected patients, improving antibiotic treatment accuracy, shortening treatment periods are all crucial factors to contain nosocomial infections.

The population-attributable fraction for time-to-event data

Background

Even though the population-attributable fraction (PAF) is a well-established metric, it is often incorrectly estimated or interpreted not only in clinical application, but also in statistical research articles. The risk of bias is especially high in more complex time-to-event data settings.

Methods

We explain how the PAF can be defined, identified and estimated in time-to-event settings with competing risks and time-dependent exposures. By using multi-state methodology and inverse probability weighting, we demonstrate how to reduce or completely avoid severe types of biases including competing risks bias, immortal time bias and confounding due to both baseline and time-varying patient characteristics.

Results

The method is exemplarily applied to a real data set. Moreover, we estimate the number of deaths that were attributable to ventilator-associated pneumonia in France in the year 2016. The example demonstrates how, under certain simplifying assumptions, PAF estimates can be extrapolated to a target population of interest.

Conclusions

Defining and estimating the PAF in advanced time-to-event settings within a framework that unifies causal and multi-state modelling enables to tackle common sources of bias and allows straightforward implementation with standard software packages.

Disease burden, associated mortality and economic impact of antimicrobial resistant infections in Australia

BACKGROUND

The growing spread of antimicrobial resistance (AMR) is accepted as a threat to humans, animals and the environment. This threat is considered to be both country specific and global, with bacteria resistant to antibiotic treatment geographically dispersed. Despite this, we have very few Australian estimates available that use national surveillance data supplemented with measures of risk, to generate reliable and actionable measures of AMR impact. These data are essential to direct policies and programs and support equitable healthcare resource utilisation. Importantly, such data can lead to implementation of programs to improved morbidity and mortality of patients with a resistant infection.

METHODS

Using data from a previous case-cohort study, we estimated the AMR-associated health and economic impact caused by five hospital-associated AMR pathogens (Enterococcus spp., E. coli, K. pneumoniae, P. aeruginosa and S. aureus) in patients with a bloodstream, urinary tract, or respiratory tract infection in Australia in 2020. We estimated disease burden based on the counterfactual scenario in which all AMR infections were replaced by no infection.We used a population-level simulation model to compute AMR-associated mortality, loss of quality-adjusted life years and costs.

FINDINGS

In 2020, there were 1,031 AMR-associated deaths (95% uncertainty interval [UI] 294, 2,615) from the five resistant hospital-associated infections in Australia. The greatest odds of dying were from respiratory infections (ceftazidime-resistant P. aeruginosa) and bloodstream infections, both resulting in high hospital and premature death costs. MRSA bacteraemia contributed the most to hospital costs (measured as bed-days) as patients with this infection resulted in additional 12,818 (95% UI 7246, 19966) hospital bed-days and cost the hospitals an extra $24,366,741 (95%UI $13,774,548, $37,954,686) per year. However, the cost of premature death from five resistant pathogens was $438,543,052, which was by far greater than the total hospital cost ($71,988,858). We estimate a loss of 27,705 quality-adjusted life years due to the five AMR pathogens.

INTERPRETATION

These are the first Australian estimates of AMR-associated health and economic impact. Country-level estimates of AMR impact are needed to provide local evidence to better inform programs and health policies to reduce morbidity and mortality associated with infection. The burden in hospital is likely an underestimate of the impact of AMR due to community-associated infections where data are limited, and the AMR burden is high. This should now be the focus of future study in this area.

FUNDING

TMW was supported by the Australian Partnership for Preparedness Research on Infectious Disease Emergencies (APPRISE) (grant number GNT1116530) Fellowship.

Incidence and mortality of hospital-acquired bacteraemia: A population-based cohort study applying a multi-state model approach

OBJECTIVES

The effect of hospital-acquired bacteraemia on mortality is sparsely investigated. We investigated the incidence and hospital-acquired bacteraemia impact on mortality.

METHODS

We conducted a 13-year population-based cohort study using The North Denmark Bacteraemia Research Database and Danish health registries. The population comprised all adult patients with a hospital admission lasting ≥48 hours. We used Poisson regression to estimate trends in incidence. The 30-day mortality of hospital-acquired bacteraemia was estimated using an illness-death multi-state model with recovery using the population at risk of hospital-acquired bacteraemia as reference.

RESULTS

We identified 3,588 episodes of hospital-acquired bacteraemia in 484,264 admissions. The incidence increased proportionally by 1.02 episodes yearly (95% CI 1.01 - 1.03) between 2006 and 2018. Hospital-acquired bacteraemia was associated with increased mortality (adjusted hazard ratio (aHR) 4.32, 95% CI 3.95 - 4.72), especially hospital-acquired bacteraemia with unknown source (aHR 6.42 (95% CI 5.67 - 7.26), 'thoracic incl. pneumonia' (aHR 5.89, 95% CI 3.45 - 10.12), and abdominal source (aHR 4.33, 95% CI 3.27 - 5.74)95% CI95% CI. The relative impact on mortality diminished with age (aHR 5.66, 95% CI 2.00 - 16.01 in 18-40 years old vs. 3.69, 95% CI 3.14 - 4.32 in 81-105 years old) and comorbidity (aHR 5.75, 95% CI 4.45 - 7.42 in low vs. 3.55, 95% CI 3.16 -3.98 in high comorbidity), and was higher in elective admissions (aHR 9.09, 95% CI 7.14 - 11.57 vs. aHR of 4.03, 95% CI 3.67 - 4.42).

CONCLUSIONS

Hospital-acquired bacteraemia is associated with high mortality, especially when the source is unknown or originating from the thoracic cavity.

Die Erforschung der Dynamik der Corona-Pandemie in Deutschland: Survey-Konzepte und eine exemplarische Umsetzung mit dem Sozio-oekonomischen Panel (SOEP)

Die Weltgesundheitsorganisation (WHO) hat im Frühjahr 2020 Richtlinien für Bevölkerungsstichproben veröffentlicht, die Basisdaten für gesundheitspolitische Entscheidungen im Pandemiefall liefern können. Diese Richtlinien umzusetzen ist keineswegs trivial. In diesem Beitrag schildern wir die Herausforderungen einer entsprechenden statistischen Erfassung der Corona Pandemie. Hierbei gehen wir im ersten Teil auf die Erfassung der Dunkelziffer bei der Meldung von Corona Infektionen, die Messung von Krankheitsverläufen im außerklinischen Bereich, die Messung von Risikomerkmalen sowie die Erfassung von zeitlichen und regionalen Veränderungen der Pandemie-Intensität ein. Wir diskutieren verschiedene Möglichkeiten, aber auch praktische Grenzen der Survey-Statistik, den vielfältigen Herausforderungen durch eine geeignete Anlage der Stichprobe und des Survey-Designs zu begegnen. Ein zentraler Punkt ist die schwierige Koppelung medizinischer Tests mit bevölkerungsrepräsentativen Umfragen, wobei bei einer personalisierten Rückmeldung der Testergebnisse das Statistik-Geheimnis eine besondere Herausforderung darstellt.

Im zweiten Teil berichten wir wie eine der großen Wiederholungsbefragungen in Deutschland, das Sozio-oekonomische Panel (SOEP), für eine WHO-konforme Covid-19-Erhebung genutzt wird, die im Rahmen einer Kooperation des Robert-Koch-Instituts (RKI) mit dem SOEP als „RKI-SOEP Stichprobe“ im September 2020 gestartet wurde. Erste Ergebnisse zum Rücklauf dieser Studie, die ab Oktober 2021 mit einer zweiten Erhebungswelle bei denselben Personen fortgesetzt werden wird, werden vorgestellt. Es zeigt sich, dass knapp fünf Prozent der bereits in der Vergangenheit erfolgreich Befragten aufgrund der Anfrage zwei Tests zu machen die weitere Teilnahme an der SOEP-Studie verweigern. Berücksichtigt man alle in der Studie erhobenen Informationen (IgG-Antikörper-Tests, PCR-Tests und Fragebögen) ergibt eine erste Schätzung, dass sich bis November 2020 nur etwa zwei Prozent der in Privathaushalten lebenden Erwachsenen in Deutschland mit SARS-CoV‑2 infiziert hatten. Damit war die Zahl der Infektionen etwa doppelt so hoch wie die offiziell gemeldeten Infektionszahlen.

Performance Measurement Outcomes: An Analysis of Health Care-Associated Infections in New York State

BACKGROUND AND OBJECTIVES

Comparing outcome measures in health care is a relatively common practice. Reports are designed to compare hospital infection rates in an accurate and fair manner. The current methodology used by New York State (NYS) has some limitations and flaws. This research provides a methodology that overcomes these limitations and flaws.

METHODS

The methodology is a replication study using data from NYS and includes the use of the binomial and Poisson distribution to calculate upper tail (UTP) and lower tail probabilities (LTP). The UTP is used to screen for poor performers, and the LTP is used to screen for good performers.

RESULTS

The results we obtained using the tail probability method compared with NYS's confidence interval approach are similar across all health care-associated infection (HAI) categories but have the benefit of allowing for the analysis of any hospital regardless of the number of procedures, number of central line-days, or number of patient-days. In addition, we provide an evaluation of a hospital's performance over time where we identified hospitals that were consistently performing poorly and others consistently performing well.

CONCLUSION

Identifying hospitals that are consistently performing poorly and hospitals consistently performing well will allow administrators and clinicians to focus their efforts including budgetary to where improvements are needed. Patient care and the reduction of HAIs are a priority for health care institutions. While the results are similar to those reported by NYS, this approach can be used more comprehensively and can be interpreted more easily by administrators and practitioners. Health care administrators and clinicians may find the information useful to address infection rates. Hospitals consistently performing well may be used as a benchmark.

Attributable Mortality of Ventilator-associated Pneumonia. Replicating Findings, Revisiting Methods

Rationale: Estimating the impact of ventilator-associated pneumonia (VAP) from routinely collected intensive care unit (ICU) data is methodologically challenging.

Objectives: We aim to replicate earlier findings of limited VAP-attributable ICU mortality in an independent cohort. By refining statistical analyses, we gradually tackle different sources of bias.

Methods: Records of 2,720 adult patients admitted to Ghent University Hospital ICUs (2013–2017) and receiving mechanical ventilation within 48 hours after admission were extracted from linked Intensive Care Information System and Computer-based Surveillance and Alerting of Nosocomial Infections, Antimicrobial Resistance, and Antibiotic Consumption in the ICU databases. The VAP-attributable fraction of ICU mortality was estimated using a competing risk analysis that is restricted to VAP-free patients (approach 1), accounts for VAP onset by treating it as either a competing (approach 2) or censoring event (approach 3), or additionally adjusts for time-dependent confounding via inverse probability weighting (approach 4).

Results: A total of 210 patients (7.7%) acquired VAP. Based on benchmark approach 4, we estimated that (compared with current preventive measures) hypothetical eradication of VAP would lead to a relative ICU mortality reduction of 1.7% (95% confidence interval, −1.3 to 4.6) by Day 10 and of 3.6% (95% confidence interval, 0.7 to 6.5) by Day 60. Approaches 1–3 produced estimates ranging from −0.7% to 2.5% by Day 10 and from 5.2% to 5.5% by Day 60.

Conclusions: In line with previous studies using appropriate methodology, we found limited VAP-attributable ICU mortality given current state-of-the-art VAP prevention measures. Our study illustrates that inappropriate accounting of the time dependency of exposure and confounding of its effects may misleadingly suggest protective effects of early-onset VAP and systematically overestimate attributable mortality.

Impact of healthcare-associated infection on length of stay

BACKGROUND

Increased length of stay (LOS) for patients is an important measure of the burden of healthcare-associated infection (HAI).

AIM

To estimate the excess LOS attributable to HAI.

METHODS

This was a one-year prospective incidence study of HAI observed in one teaching hospital and one general hospital in NHS Scotland as part of the Evaluation of Cost of Nosocomial Infection (ECONI) study. All adult inpatients with an overnight stay were included. HAI was diagnosed using European Centres for Disease Prevention and Control definitions. A multi-state model was used to account for the time-varying nature of HAI and the competing risks of death and discharge.

FINDINGS

The excess LOS attributable to HAI was 7.8 days (95% confidence interval (CI): 5.7-9.9). Median LOS for HAI patients was 30 days and for non-HAI patients was 3 days. Using a simple comparison of duration of hospital stay for HAI cases and non-cases would overestimate the excess LOS by 3.5 times (27 days compared with 7.8 days). The greatest impact on LOS was due to pneumonia (16.3 days; 95% CI: 7.5-25.2), bloodstream infections (11.4 days; 5.8-17.0) and surgical site infection (SSI) (9.8 days; 4.5-15.0). It is estimated that 58,000 bed-days are occupied due to HAI annually.

CONCLUSION

A reduction of 10% in HAI incidence could make 5800 bed-days available. These could be used to treat 1706 elective patients in Scotland annually and help reduce the number of patients awaiting planned treatment. This study has important implications for investment decisions in infection prevention and control interventions locally, nationally, and internationally.

Mortality review as a tool to assess the contribution of healthcare-associated infections to death: results of a multicentre validity and reproducibility study, 11 European Union countries, 2017 to 2018

IntroductionThe contribution of healthcare-associated infections (HAI) to mortality can be estimated using statistical methods, but mortality review (MR) is better suited for routine use in clinical settings. The European Centre for Disease Prevention and Control recently introduced MR into its HAI surveillance.AimWe evaluate validity and reproducibility of three MR measures.MethodsThe on-site investigator, usually an infection prevention and control doctor, and the clinician in charge of the patient independently reviewed records of deceased patients with bloodstream infection (BSI), pneumonia, Clostridioides difficile infection (CDI) or surgical site infection (SSI), and assessed the contribution to death using 3CAT: definitely/possibly/no contribution to death; WHOCAT: sole cause/part of causal sequence but not sufficient on its own/contributory cause but unrelated to condition causing death/no contribution, based on the World Health Organization's death certificate; QUANT: Likert scale: 0 (no contribution) to 10 (definitely cause of death). Inter-rater reliability was assessed with weighted kappa (wk) and intra-cluster correlation coefficient (ICC). Reviewers rated the fit of the measures.ResultsFrom 2017 to 2018, 24 hospitals (11 countries) recorded 291 cases: 87 BSI, 113 pneumonia , 71 CDI and 20 SSI. The inter-rater reliability was: 3CAT wk 0.68 (95% confidence interval (CI): 0.61-0.75); WHOCAT wk 0.65 (95% CI: 0.58-0.73); QUANT ICC 0.76 (95% CI: 0.71-0.81). Inter-rater reliability ranged from 0.72 for pneumonia to 0.52 for CDI. All three measures fitted 'reasonably' or 'well' in > 88%.ConclusionFeasibility, validity and reproducibility of these MR measures was acceptable for use in HAI surveillance.

Predicting Potential Prevention Effects on Hospital Burden of Nosocomial Infections: A Multistate Modeling Approach

OBJECTIVES

Hospital-acquired infections (HAIs) place a substantial burden on health systems. Tools are required to quantify the change in this burden as a result of a preventive intervention. We aim to estimate how much a reduction in the rate of hospital-acquired infections translates into a change in hospital mortality and length of stay.

METHODS

Using multistate modelling and competing risks methodology, we created a tool to estimate the reduction in burden after the introduction of a preventive effect on the infection rate. The tool requires as inputs the patients' length of hospital stay, patients' infection information (status, time), patients' final outcome (discharged alive, dead), and a preventive effect. We demonstrated the methods on both simulated data and 3 published data sets from Germany, France, and Spain.

RESULTS

A hypothetical prevention that cuts the infection rate in half would result in 21 lives and 2212 patient-days saved in French ventilator-associated pneumonia data, 61 lives and 3125 patient-days saved in Spanish nosocomial infection data, and 20 lives and 1585 patient-days saved in German nosocomial pneumonia data.

CONCLUSIONS

Our tool provides a quick and easy means of acquiring an impression of the impact a preventive measure would have on the burden of an infection. The tool requires quantities routinely collected and computation can be done with a calculator. R code is provided for researchers to determine the burden in various settings with various effects. Furthermore, cost data can be used to get the financial benefit of the reduction in burden.

Activity of β-Lactam Antibiotics against Metallo-β-Lactamase-Producing Enterobacterales in Animal Infection Models: a Current State of Affairs

Metallo-β-lactamases (MBLs) result in resistance to nearly all β-lactam antimicrobial agents, as determined by currently employed susceptibility testing methods. However, recently reported data demonstrate that variable and supraphysiologic zinc concentrations in conventional susceptibility testing media compared with physiologic (bioactive) zinc concentrations may be mediating discordant in vitro-in vivo MBL resistance. While treatment outcomes in patients appear suggestive of this discordance, these limited data are confounded by comorbidities and combination therapy. To that end, the goal of this review is to evaluate the extent of β-lactam activity against MBL-harboring Enterobacterales in published animal infection model studies and provide contemporary considerations to facilitate the optimization of current antimicrobials and development of novel therapeutics.

The healthcare costs of antimicrobial resistance in Lebanon: a multi-centre prospective cohort study from the payer perspective

Background

Our aim was to examine whether the length of stay, hospital charges and in-hospital mortality attributable to healthcare- and community-associated infections due to antimicrobial-resistant bacteria were higher compared with those due to susceptible bacteria in the Lebanese healthcare settings using different methodology of analysis from the payer perspective .

Methods

We performed a multi-centre prospective cohort study in ten hospitals across Lebanon. The sample size consisted of 1289 patients with documented healthcare-associated infection (HAI) or community-associated infection (CAI). We conducted three separate analysis to adjust for confounders and time-dependent bias: (1) Post-HAIs in which we included the excess LOS and hospital charges incurred after infection and (2) Matched cohort, in which we matched the patients based on propensity score estimates (3) The conventional method, in which we considered the entire hospital stay and allocated charges attributable to CAI. The linear regression models accounted for multiple confounders.

Results

HAIs and CAIs with resistant versus susceptible bacteria were associated with a significant excess length of hospital stay (2.69 days [95% CI,1.5–3.9]; p < 0.001) and (2.2 days [95% CI,1.2–3.3]; p < 0.001) and resulted in additional hospital charges ($1807 [95% CI, 1046–2569]; p < 0.001) and ($889 [95% CI, 378–1400]; p = 0.001) respectively. Compared with the post-HAIs analysis, the matched cohort method showed a reduction by 26 and 13% in hospital charges and LOS estimates respectively. Infections with resistant bacteria did not decrease the time to in-hospital mortality, for both healthcare- or community-associated infections. Resistant cases in the post-HAIs analysis showed a significantly higher risk of in-hospital mortality (odds ratio, 0.517 [95% CI, 0.327–0.820]; p = 0.05).

Conclusion

This is the first nationwide study that quantifies the healthcare costs of antimicrobial resistance in Lebanon. For cases with HAIs, matched cohort analysis showed more conservative estimates compared with post-HAIs method. The differences in estimates highlight the need for a unified methodology to estimate the burden of antimicrobial resistance in order to accurately advise health policy makers and prioritize resources expenditure.

Harmonizing Heterogeneous Endpoints in Coronavirus Disease 2019 Trials Without Loss of Information

OBJECTIVES

Many trials investigate potential effects of treatments for coronavirus disease 2019. To provide sufficient information for all involveddecision-makers (clinicians, public health authorities, and drug regulatory agencies), a multiplicity of endpoints must be considered. The objectives are to provide hands-on statistical guidelines for harmonizing heterogeneous endpoints in coronavirus disease 2019 clinical trials.

DESIGN

Randomized controlled trials for patients infected with coronavirus disease 2019.

SETTING

General methods that apply to any randomized controlled trial for patients infected with coronavirus disease 2019.

PATIENTS

Coronavirus disease 2019 positive individuals.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We develop a multistate model that is based on hospitalization, mechanical ventilation, death, and discharge. These events are both categories of the ordinal endpoint recommended by the World Health Organization and also within the core outcome set of the Core Outcome Measures in Effectiveness Trials initiative for coronavirus disease 2019 trials. To support our choice of states in the multistate model, we also perform a brief review of registered coronavirus disease 2019 clinical trials. Based on the multistate model, we give recommendation for compact, informative illustration of time-dynamic treatment effects and explorative statistical analysis. A majority of coronavirus disease 2019 clinical trials collect information on mechanical ventilation, hospitalization, and death. Using reconstructed and real data of coronavirus disease 2019 trials, we show how a stacked probability plot provides a detailed understanding of treatment effects on the patients' course of hospital stay. It contributes to harmonizing multiple endpoints and differing lengths of follow-up both within and between trials.

CONCLUSIONS

All ongoing clinical trials should include a stacked probability plot in their statistical analysis plan as descriptive analysis. While primary analysis should be on an early endpoint with appropriate capability to be a surrogate (parameter), our multistate model provides additional detailed descriptive information and links results within and between coronavirus disease 2019 trials.

Review of methods and study designs of evaluations related to clinical pathways

Objective The HealthPathways program is an online information portal that helps clinicians provide consistent and integrated patient care within a local health system through localised pathways for diagnosis, treatment and management of various health conditions. These pathways are consistent with the definition of clinical pathways. Evaluations of HealthPathways programs have thus far focused primarily on website utilisation and clinical users' experience and satisfaction, with limited evidence on changes to patient outcomes. This lack motivated a literature review of the effects of clinical pathways on patient and economic outcomes to inform a subsequent HealthPathways evaluation. Methods A systematic review was performed to summarise the analytical methods, study designs and results of studies evaluating clinical pathways with an economic outcome component published between 1 January 2000 and 31 August 2017 in four academic literature databases. Results Fifty-five relevant articles were identified for inclusion in this review. The practical pre-post study design with retrospective baseline data extraction and prospective intervention data collection was most commonly used in the evaluations identified. Straightforward statistical methods for comparing outcomes, such as the t-test or χ2 test, were frequently used. Only four of the 55 articles performed a cost-effectiveness analysis. Clinical pathways were generally associated with improved patient outcomes and positive economic outcomes in hospital settings. Conclusions Clinical pathways evaluations commonly use pragmatic study designs, straightforward statistical tests and cost-consequence analyses. More HealthPathways program evaluations focused on patient and economic outcomes, clinical pathway evaluations in a primary care setting and cost-effectiveness analyses of clinical pathways are needed. What is known about the topic? HealthPathways is a web-based program that originated from Canterbury, New Zealand, and has seen uptake elsewhere in New Zealand, Australia and the UK. The HealthPathways program aims to assist the provision of consistent and integrated health services through dedicated, localised pathways for various health conditions specific to the health region. Evaluations of HealthPathways program focused on patient and economic outcomes have been limited. What does this paper add? This review synthesises the academic literature of clinical pathways evaluations in order to inform a subsequent HealthPathways evaluation. The focus of the synthesis was on the analytical methods and study designs used in the previous evaluations. The previous clinical pathway evaluations have been pragmatic in nature with relatively straightforward study designs and analysis. What are the implications for practitioners? There is a need for more economic and patient outcome evaluations for HealthPathways programs. More sophisticated statistical analyses and economic evaluations could add value to these evaluations, where appropriate and taking into consideration the data limitations.

Statistical Analysis of Clinical COVID-19 Data: A Concise Overview of Lessons Learned, Common Errors and How to Avoid Them

By definition, in-hospital patient data are restricted to the time between hospital admission and discharge (alive or dead). For hospitalised cases of COVID-19, a number of events during hospitalization are of interest regarding the influence of risk factors on the likelihood of experiencing these events. The same is true for predicting times from hospital admission of COVID-19 patients to intensive care or from start of ventilation (invasive or non-invasive) to extubation. This logical restriction of the data to the period of hospitalisation is associated with a substantial risk that inappropriate methods are used for analysis. Here, we briefly discuss the most common types of bias which can occur when analysing in-hospital COVID-19 data.

Estimating length of stay and inpatient charges attributable to hospital-acquired bloodstream infections

Background

Hospital-acquired bloodstream infection (BSI) is associated with high morbidity and mortality and increases patients’ length of stay (LOS) and hospital charges. Our goals were to calculate LOS and charges attributable to BSI and compare results among different models.

Methods

A retrospective observational cohort study was conducted in 2017 in a large general hospital, in Beijing. Using patient-level data, we compared the attributable LOS and charges of BSI with three models: 1) conventional non-matching, 2) propensity score matching controlling for the impact of potential confounding variables, and 3) risk set matching controlling for time-varying covariates and matching based on propensity score and infection time.

Results

The study included 118,600 patient admissions, 557 (0.47%) with BSI. Six hundred fourteen microorganisms were cultured from patients with BSI. Escherichia coli was the most common bacteria (106, 17.26%). Among multi-drug resistant bacteria, carbapenem-resistant Acinetobacter baumannii (CRAB) was the most common (42, 38.53%). In the conventional non-matching model, the excess LOS and charges associated with BSI were 25.06 days (P < 0.05) and US$22041.73 (P < 0.05), respectively. After matching, the mean LOS and charges attributable to BSI both decreased. When infection time was incorporated into the risk set matching model, the excess LOS and charges were 16.86 days (P < 0.05) and US$15909.21 (P < 0.05), respectively.

Conclusion

This is the first study to consider time-dependent bias in estimating excess LOS and charges attributable to BSI in a Chinese hospital setting. We found matching on infection time can reduce bias.

Nosocomial Infections During Extracorporeal Membrane Oxygenation in Neonatal, Pediatric, and Adult Patients: A Comprehensive Narrative Review

OBJECTIVES

Extracorporeal membrane oxygenation is increasingly used in critically ill patients with refractory cardiopulmonary failure. Nosocomial infection acquired during extracorporeal membrane oxygenation represents one of the most frequent complications but the available evidence on the risk of infection and its association with outcomes has not been comprehensively analyzed. We performed a narrative review examining the epidemiology of nosocomial infection during extracorporeal membrane oxygenation, association with clinical outcomes, and preventive strategies.

DATA SOURCES

We searched PubMed, Web of Science, EMBASE, and the Cochrane Library between 1972 and June 2018.

STUDY SELECTION

We included any article which detailed nosocomial infection during extracorporeal membrane oxygenation. Articles were excluded if they were not written in English, detailed extracorporeal membrane oxygenation use for infections acquired prior to extracorporeal membrane oxygenation, or used other forms of extracorporeal support such as ventricular assist devices.

DATA EXTRACTION

Two reviewers independently assessed eligibility and extracted data. We screened 984 abstracts and included 59 articles in the final review.

DATA SYNTHESIS

The reported risk of nosocomial infection among patients receiving extracorporeal membrane oxygenation ranged from 3.5% to 64% per extracorporeal membrane oxygenation run, while the incidence of infection ranged from 10.1 to 116.2/1,000 extracorporeal membrane oxygenation days. Nosocomial infections during extracorporeal membrane oxygenation were consistently associated with longer duration of extracorporeal membrane oxygenation and, in several large multicenter studies, with increased mortality. Risk factors for nosocomial infection included duration of extracorporeal membrane oxygenation, mechanical and hemorrhagic complications on extracorporeal membrane oxygenation, and use of venoarterial and central extracorporeal membrane oxygenation. Biomarkers had low specificity for infection in this population. Few studies examined strategies on how to prevent nosocomial infection on extracorporeal membrane oxygenation.

CONCLUSIONS

Nosocomial infections in extracorporeal membrane oxygenation patients are common and associated with worse outcomes. There is substantial variation in the rates of reported infection, and thus, it is possible that some may be preventable. The evidence for current diagnostic, preventive, and therapeutic strategies for infection during extracorporeal membrane oxygenation is limited and requires further investigation.

The impact of hospital-acquired infections on the patient-level reimbursement-cost relationship in a DRG-based hospital payment system

Hospital-acquired infections (HAIs) are a common complication in inpatient care. We investigate the incentives to prevent HAIs under the German DRG-based reimbursement system. We analyze the relationship between resource use and reimbursements for HAI in 188,731 patient records from the University Medical Center Freiburg (2011-2014), comparing cases to appropriate non-HAI controls. Resource use is approximated using national standardized costing system data. Reimbursements are the actual payments to hospitals under the G-DRG system. Timing of HAI exposure, cost-clustering within main diagnoses and risk-adjustment are considered. The reimbursement-cost difference of HAI patients is negative (approximately - €4000). While controls on average also have a negative reimbursement-cost difference (approximately - €2000), HAI significantly increase this difference after controlling for confounding and timing of infection (- 1500, p < 0.01). HAIs caused by vancomycin-resistant Enterococci have the most unfavorable reimbursement-cost difference (- €10,800), significantly higher (- €9100, p < 0.05) than controls. Among infection types, pneumonia is associated with highest losses (- €8400 and - €5700 compared with controls, p < 0.05), while cost-reimbursement relationship for Clostridium difficile-associated diarrhea is comparatively balanced (- €3200 and - €500 compared to controls, p = 0.198). From the hospital administration's perspective, it is not the additional costs of HAIs, but rather the cost-reimbursement relationship which guides decisions. Costs exceeding reimbursements for HAI may increase infection prevention and control efforts and can be used to show their cost-effectiveness from the hospital perspective.

Healthcare Associated Infections-A New Pathology in Medical Practice?

Background: Hospital-acquired infections (HAI) contribute to the emotional stress and functional disorders of the patient and in some cases, can lead to a state of disability that reduces quality of life. Often, HAI are one of the factors that lead to death. The purpose of this study was to analyze the cases of HAI identified in public hospitals at the county level, through case report sheets, as they are reported according to the Romanian legislation. Methods: We performed a cross sectional study design based on the case law of the data reported to the Mures Public Health Directorate, by all the public hospitals belonging to this county. We tracked hospital-acquired infections reported for 2017-2018, respectively, a number of 1024 cases, which implies a prevalence rate of 0.44%, 1024/228,782 cases discharged from these hospitals during the studied period. Results: The most frequent HAIs were reported by the intensive care units (48.4%), the most common infections being the following: bronchopneumonia (25.3%), enterocolitis with Clostridioidesdifficile (23.3%), sepsis, surgical wound infections and urinary tract infections. At the basis of HAI were 22 pathogens, but the five most common germs were Clostridioidesdifficile, Acinetobacterbaumannii, Klebsiella pneumoniae, Pseudomonas aeruginosa and Staphylococcus aureus. Bronchopneumonia have been most frequently reported in intensive care units, the most common being identified the Acinetobacterbaumannii agent. Sepsis and central catheter infections also appeared predominantly in intensive care units, more often with Klebsiella pneumoniae. The enterocolitis with Clostridioidesdifficile, were the apanage of the medical sections. Infections with Staphylococcus aureus have been identified predominantly in the surgical sections at the level of the surgical wounds. Urinary infections had a similar distribution in the intensive care units, the medical and surgical sections, with Klebsiella pneumoniae being the most commonly incriminated agent. Conclusions: We showed a clear correspondence between the medical units and the type of HAI: what recommends the rapid, vigilant and oriented application of the prevention and control strategies of the HAI.

Impact of mechanical ventilation on the daily costs of ICU care: a systematic review and meta regression

The impact of mechanical ventilation on the daily costs of intensive care unit (ICU) care is largely unknown. We thus conducted a systematic search for studies measuring the daily costs of ICU stays for general populations of adults (age ≥18 years) and the added costs of mechanical ventilation. The relative increase in the daily costs was estimated using random effects meta regression. The results of the analyses were applied to a recent study calculating the excess length-of-stay associated with ICU-acquired (ventilator-associated) pneumonia, a major complication of mechanical ventilation. The search identified five eligible studies including a total of 54 766 patients and ~238 037 patient days in the ICU. Overall, mechanical ventilation was associated with a 25.8% (95% CI 4.7%–51.2%) increase in the daily costs of ICU care. A combination of these estimates with standardised unit costs results in approximate daily costs of a single ventilated ICU day of €1654 and €1580 in France and Germany, respectively. Mechanical ventilation is a major driver of ICU costs and should be taken into account when measuring the financial burden of adverse events in ICU settings.

Measuring the in-hospital costs of Pseudomonas aeruginosa pneumonia: methodology and results from a German teaching hospital

Background

Pseudomonas aeruginosa-related pneumonia is an ongoing healthcare challenge. Estimating its financial burden is complicated by the time-dependent nature of the disease.

Methods

Two hundred thirty-six cases of Pseudomonas aeruginosa-related pneumonia were recorded at a 2000 bed German teaching hospital between 2011 and 2014. Thirty-five cases (15%) were multidrug-resistant (MDR) Pseudomonas aeruginosa. Hospital- and community-acquired cases were distinguished by main diagnoses and exposure time. The impact of Pseudomonas aeruginosa-related pneumonia on the three endpoints cost, reimbursement, and length of stay was analyzed, taking into account (1) the time-dependent nature of exposure, (2) clustering of costs within diagnostic groups, and (3) additional confounders.

Results

Pseudomonas aeruginosa pneumonia is associated with substantial additional costs that are not fully reimbursed. Costs are highest for hospital-acquired cases (€19,000 increase over uninfected controls). However, community-acquired cases are also associated with a substantial burden (€8400 when Pseudomonas aeruginosa pneumonia is the main reason for hospitalization, and €6700 when not). Sensitivity analyses for hospital-acquired cases showed that ignoring or incorrectly adjusting for time-dependency substantially biases results. Furthermore, multidrug-resistance was rare and only showed a measurable impact on the cost of community-acquired cases.

Conclusions

Pseudomonas aeruginosa pneumonia creates a substantial financial burden for hospitals. This is particularly the case for nosocomial infections. Infection control interventions could yield significant cost reductions. However, to evaluate the potential effectiveness of different interventions, the time-dependent aspects of incremental costs must be considered to avoid introduction of bias.

Costs of hospital-acquired Clostridium difficile infections: an analysis on the effect of time-dependent exposures using routine and surveillance data

Background

Hospital-acquired infections have not only gained increasing attention clinically, but also methodologically, as a time-varying exposure. While methods to appropriately estimate extra length of stay (LOS) have been established and are increasingly used in the literature, proper estimation of cost figures has lagged behind.

Methods

Analysing the additional costs and reimbursements of Clostridium difficile-infections (CDI), we use a within-main-diagnosis-time-to-exposure stratification approach to incorporate time-varying exposures in a regression model, while at the same time accounting for cost clustering within diagnosis groups.

Results

We find that CDI is associated with €9000 of extra costs, €7800 of higher reimbursements, and 6.4 days extra length of stay. Using a conventional method, which suffers from time-dependent bias, we derive estimates more than three times as high (€23,000, €8000, 21 days respectively). We discuss our method in the context of recent methodological advances in the estimation of the costs of hospital-acquired infections.

Conclusions

CDI is associated with sizeable in-hospital costs. Neglecting the methodological particularities of hospital-acquired infections can however substantially bias results. As the data needed for an appropriate analysis are collected routinely in most hospitals, we recommend our approach as a feasible way for estimating the economic impact of time-varying adverse events during hospital stay.

Antibiotic Review Kit for Hospitals (ARK-Hospital): study protocol for a stepped-wedge cluster-randomised controlled trial

BACKGROUND

To ensure patients continue to get early access to antibiotics at admission, while also safely reducing antibiotic use in hospitals, one needs to target the continued need for antibiotics as more diagnostic information becomes available. UK Department of Health guidance promotes an initiative called 'Start Smart then Focus': early effective antibiotics followed by active 'review and revision' 24-72 h later. However in 2017, < 10% of antibiotic prescriptions were discontinued at review, despite studies suggesting that 20-30% of prescriptions could be stopped safely.

METHODS/DESIGN

Antibiotic Review Kit for Hospitals (ARK-Hospital) is a complex 'review and revise' behavioural intervention targeting healthcare professionals involved in antibiotic prescribing or administration in inpatients admitted to acute/general medicine (the largest consumers of non-prophylactic antibiotics in hospitals). The primary study objective is to evaluate whether ARK-Hospital can safely reduce the total antibiotic burden in acute/general medical inpatients by at least 15%. The primary hypotheses are therefore that the introduction of the behavioural intervention will be non-inferior in terms of 30-day mortality post-admission (relative margin 5%) for an acute/general medical inpatient, and superior in terms of defined daily doses of antibiotics per acute/general medical admission (co-primary outcomes). The unit of observation is a hospital organisation, a single hospital or group of hospitals organised with one executive board and governance framework (National Health Service trusts in England; health boards in Northern Ireland, Wales and Scotland). The study comprises a feasibility study in one organisation (phase I), an internal pilot trial in three organisations (phase II) and a cluster (organisation)-randomised stepped-wedge trial (phase III) targeting a minimum of 36 organisations in total. Randomisation will occur over 18 months from November 2017 with a further 12 months follow-up to assess sustainability. The behavioural intervention will be delivered to healthcare professionals involved in antibiotic prescribing or administration in adult inpatients admitted to acute/general medicine. Outcomes will be assessed in adult inpatients admitted to acute/general medicine, collected through routine electronic health records in all patients.

DISCUSSION

ARK-Hospital aims to provide a feasible, sustainable and generalisable mechanism for increasing antibiotic stopping in patients who no longer need to receive them at 'review and revise'.

TRIAL REGISTRATION

ISRCTN Current Controlled Trials, ISRCTN12674243 . Registered on 10 April 2017.

The population-attributable fraction for time-dependent exposures using dynamic prediction and landmarking

The public health impact of a harmful exposure can be quantified by the population-attributable fraction (PAF). The PAF describes the attributable risk due to an exposure and is often interpreted as the proportion of preventable cases if the exposure was extinct. Difficulties in the definition and interpretation of the PAF arise when the exposure of interest depends on time. Then, the definition of exposed and unexposed individuals is not straightforward. We propose dynamic prediction and landmarking to define and estimate a PAF in this data situation. Two estimands are discussed which are based on two hypothetical interventions that could prevent the exposure in different ways. Considering the first estimand, at each landmark the estimation problem is reduced to a time-independent setting. Then, estimation is simply performed by using a generalized-linear model accounting for the current exposure state and further (time-varying) covariates. The second estimand is based on counterfactual outcomes, estimation can be performed using pseudo-values or inverse-probability weights. The approach is explored in a simulation study and applied on two data examples. First, we study a large French database of intensive care unit patients to estimate the population-benefit of a pathogen-specific intervention that could prevent ventilator-associated pneumonia caused by the pathogen Pseudomonas aeruginosa. Moreover, we quantify the population-attributable burden of locoregional and distant recurrence in breast cancer patients.

Mortality related to Verona Integron-encoded Metallo-β-lactamase-positive Pseudomonas aeruginosa: assessment by a novel clinical tool

Background

Verona Integron-encoded Metallo-β-lactamase-positive Pseudomonas aeruginosa (VIM-PA) can cause nosocomial infections and may be responsible for increased mortality. Multidrug resistance in VIM-PA complicates treatment. We aimed to assess the contribution of VIM-PA to mortality in patients in a large tertiary care hospital in the Netherlands.

Methods

A focus group of five members created a scheme to define related mortality based on clinical and diagnostic findings. Contribution to mortality was categorized as “definitely”, “probably”, “possibly”, or “not” related to infection with VIM-PA, or as “unknown”. Patients were included when infected with or carrier of VIM-PA between January 2008 and January 2016. Patient-related data and specific data on VIM-PA cultures were retrieved from the electronic laboratory information system. For patients who died in our hospital, medical records were independently reviewed and thereafter discussed by three physicians.

Results

A total of 198 patients with any positive culture with VIM-PA were identified, of whom 95 (48.0%) died. Sixty-seven patients died in our hospital and could be included in the analysis. The death of 15 patients (22.4%) was judged by all reviewers to be definitely related to infection with VIM-PA. In 17 additional patients (25.4%), death was probably or possibly related to an infection with VIM-PA. The level of agreement was 65.7% after the first evaluation, and 98.5% after one session of discussion.

Conclusion

Using our assessment tool, infections with VIM-PA were shown to have an important influence on mortality in our complex and severely ill patients. The tool may be used for other (resistant) bacteria as well but this needs further exploration.

Electronic supplementary material

The online version of this article (10.1186/s13756-019-0556-9) contains supplementary material, which is available to authorized users.

The population-attributable fraction for time-dependent exposures and competing risks-A discussion on estimands

The population-attributable fraction (PAF) quantifies the public health impact of a harmful exposure. Despite being a measure of significant importance, an estimand accommodating complicated time-to-event data is not clearly defined. We discuss current estimands of the PAF used to quantify the public health impact of an internal time-dependent exposure for data subject to competing outcomes. To overcome some limitations, we proposed a novel estimand that is based on dynamic prediction by landmarking. In a profound simulation study, we discuss interpretation and performance of the various estimands and their estimators. The methods are applied to a large French database to estimate the health impact of ventilator-associated pneumonia for patients in intensive care.

Estimating excess length of stay due to healthcare-associated infections: a systematic review and meta-analysis of statistical methodology

BACKGROUND

Healthcare-associated infection (HCAI) affects millions of patients worldwide. HCAI is associated with increased healthcare costs, owing primarily to increased hospital length of stay (LOS) but calculating these costs is complicated due to time-dependent bias. Accurate estimation of excess LOS due to HCAI is essential to ensure that we invest in cost-effective infection prevention and control (IPC) measures.

AIM

To identify and review the main statistical methods that have been employed to estimate differential LOS between patients with, and without, HCAI; to highlight and discuss potential biases of all statistical approaches.

METHODS

A systematic review from 1997 to April 2017 was conducted in PubMed, CINAHL, ProQuest and EconLit databases. Studies were quality-assessed using an adapted Newcastle-Ottawa Scale (NOS). Methods were categorized as time-fixed or time-varying, with the former exhibiting time-dependent bias. Two examples of meta-analysis were used to illustrate how estimates of excess LOS differ between different studies.

FINDINGS

Ninety-two studies with estimates on excess LOS were identified. The majority of articles employed time-fixed methods (75%). Studies using time-varying methods are of higher quality according to NOS. Studies using time-fixed methods overestimate additional LOS attributable to HCAI. Undertaking meta-analysis is challenging due to a variety of study designs and reporting styles. Study differences are further magnified by heterogeneous populations, case definitions, causative organisms, and susceptibilities.

CONCLUSION

Methodologies have evolved over the last 20 years but there is still a significant body of evidence reliant upon time-fixed methods. Robust estimates are required to inform investment in cost-effective IPC interventions.

Effect of methicillin-resistant Staphylococcus aureus in Japan

BACKGROUND

Methicillin-resistant Staphylococcus aureus (MRSA) is the most common antimicrobial-resistant organism identified in Japanese health care facilities. This study analyzed the clinical and economic burdens attributable to methicillin resistance in S aureus in Japanese hospitals.

METHODS

We retrospectively investigated data from 14,905 inpatients of 57 hospitals combined with data from nosocomial infection surveillance and administrative claim databases. The participants were inpatients with admission from April 1, 2014, to discharge on March 31, 2016. The outcomes were evaluated according to length of stay, hospital charges, and in-hospital mortality. We compared the disease burden of MRSA infections with methicillin-susceptible S aureus (MSSA) infections based on patients' characteristics and onset periods.

RESULTS

We categorized 7,188 and 7,717 patients into MRSA and MSSA groups, respectively. The adjusted effects of the MRSA group were 1.03-fold (95% confidence interval [CI] 1.01-1.05) and 1.04-fold (95% CI, 1.01-1.06), respectively, with an odds ratio of 1.14 (95% CI, 1.02-1.27).

CONCLUSIONS

The results of this study found that patient severity and onset delays were positively associated with both MRSA and burden and that the effect of methicillin resistance remained significant after adjustment.

Improving nested case-control studies to conduct a full competing-risks analysis for nosocomial infections

OBJECTIVE

Competing risks are a necessary consideration when analyzing risk factors for nosocomial infections (NIs). In this article, we identify additional information that a competing risks analysis provides in a hospital setting. Furthermore, we improve on established methods for nested case-control designs to acquire this information.

METHODS

Using data from 2 Spanish intensive care units and model simulations, we show how controls selected by time-dynamic sampling for NI can be weighted to perform risk-factor analysis for death or discharge without infection. This extension not only enables hazard rate analysis for the competing risk, it also enables prediction analysis for NI.

RESULTS

The estimates acquired from the extension were in good agreement with the results from the full (real and simulated) cohort dataset. The reduced dataset results averted any false interpretation common in a competing-risks setting.

CONCLUSIONS

Using additional information that is routinely collected in a hospital setting, a nested case-control design can be successfully adapted to avoid a competing risks bias. Furthermore, this adapted method can be used to reanalyze past nested case-control studies to enhance their findings.

Bias due to censoring of deaths when calculating extra length of stay for patients acquiring a hospital infection

Background

In many studies the information of patients who are dying in the hospital is censored when examining the change in length of hospital stay (cLOS) due to hospital-acquired infections (HIs). While appropriate estimators of cLOS are available in literature, the existence of the bias due to censoring of deaths was neither mentioned nor discussed by the according authors.

Methods

Using multi-state models, we systematically evaluate the bias when estimating cLOS in such a way. We first evaluate the bias in a mathematically closed form assuming a setting with constant hazards. To estimate the cLOS due to HIs non-parametrically, we relax the assumption of constant hazards and consider a time-inhomogeneous Markov model.

Results

In our analytical evaluation we are able to discuss challenging effects of the bias on cLOS. These are in regard to direct and indirect differential mortality. Moreover, we can make statements about the magnitude and direction of the bias. For real-world relevance, we illustrate the bias on a publicly available prospective cohort study on hospital-acquired pneumonia in intensive-care.

Conclusion

Based on our findings, we can conclude that censoring the death cases in the hospital and considering only patients discharged alive should be avoided when estimating cLOS. Moreover, we found that the closed mathematical form can be used to describe the bias for settings with constant hazards.

Electronic supplementary material

The online version of this article (10.1186/s12874-018-0500-3) contains supplementary material, which is available to authorized users.

Assessing Noninferiority in Treatment Trials for Severe Infectious Diseases: an Extension to the Entire Follow-Up Period Using a Cure-Death Multistate Model

In current and former clinical trials for the development of antibacterial drugs, various primary endpoints have been used, and treatment effects are evaluated mostly in noninferiority analyses at the end of follow-up, which varies between studies. A more convincing and highly patient-relevant statement would be a noninferiority assessment over the entire follow-up period with cure and death as coprimary endpoints, while preserving the desired alpha level for statistical testing. To account for the time-dynamic pattern of cure and death, we apply a cure-death multistate model. The endpoint of interest is "get cured and stay alive over time." Noninferiority between treatments over the entire follow-up period is studied by means of one-sided confidence bands provided by a flexible resampling technique. We illustrate the technique by applying it to a recently published study and establish noninferiority in being cured and alive over a time frame of interest for the entire population, patients with hospital-acquired pneumonia, but not for the subset of patients with ventilator-associated pneumonia. Our analysis improves the original results in the sense that our endpoint is more patient benefiting, a stronger noninferiority statement is demonstrated, and the time dependency of cure and death, competing events, and different follow-up times is captured. Multistate methodology combined with confidence bands adds a valuable statistical tool for clinical trials in the context of infection control. The framework is not restricted to the cure-death model but can be adapted to more complex multistate endpoints and equivalence or superiority analyses.

Intensive care unit (ICU)-acquired bacteraemia and ICU mortality and discharge: addressing time-varying confounding using appropriate methodology

BACKGROUND

Studies often ignore time-varying confounding or may use inappropriate methodology to adjust for time-varying confounding.

AIM

To estimate the effect of intensive care unit (ICU)-acquired bacteraemia on ICU mortality and discharge using appropriate methodology.

METHODS

Marginal structural models with inverse probability weighting were used to estimate the ICU mortality and discharge associated with ICU-acquired bacteraemia among patients who stayed more than two days at the general ICU of a London teaching hospital and remained bacteraemia-free during those first two days. For comparison, the same associations were evaluated with (i) a conventional Cox model, adjusting only for baseline confounders and (ii) a Cox model adjusting for baseline and time-varying confounders.

FINDINGS

Using the marginal structural model with inverse probability weighting, bacteraemia was associated with an increase in ICU mortality (cause-specific hazard ratio (CSHR): 1.29; 95% confidence interval (CI): 1.02-1.63) and a decrease in discharge (CSHR: 0.52; 95% CI: 0.45-0.60). By 60 days, among patients still in the ICU after two days and without prior bacteraemia, 8.0% of ICU deaths could be prevented by preventing all ICU-acquired bacteraemia cases. The conventional Cox model adjusting for time-varying confounders gave substantially different results [for ICU mortality, CSHR: 1.08 (95% CI: 0.88-1.32); for discharge, CSHR: 0.68 (95% CI: 0.60-0.77)].

CONCLUSION

In this study, even after adjusting for the timing of acquiring bacteraemia and time-varying confounding using inverse probability weighting for marginal structural models, ICU-acquired bacteraemia was associated with a decreased daily ICU discharge risk and an increased risk of ICU mortality.

EFFECT of daily antiseptic body wash with octenidine on nosocomial primary bacteraemia and nosocomial multidrug-resistant organisms in intensive care units: design of a multicentre, cluster-randomised, double-blind, cross-over study

INTRODUCTION

Nosocomial infections are serious complications that increase morbidity, mortality and costs and could potentially be avoidable. Antiseptic body wash is an approach to reduce dermal micro-organisms as potential pathogens on the skin. Large-scale trials with chlorhexidine as the antiseptic agent suggest a reduction of nosocomial infection rates. Octenidine is a promising alternative agent which could be more effective against Gram-negative organisms. We hypothesise that daily antiseptic body wash with octenidine reduces the risk of intensive care unit (ICU)-acquired primary bacteraemia and ICU-acquired multidrug-resistant organisms (MDRO) in a standard care setting.

METHODS AND ANALYSIS

EFFECT is a controlled, cluster-randomised, double-blind study. The experimental intervention consists in using octenidine-impregnated wash mitts for the daily routine washing procedure of the patients. This will be compared with using placebo wash mitts. Replacing existing washing methods is the only interference into clinical routine.Participating ICUs are randomised in an AB/BA cross-over design. There are two 15-month periods, each consisting of a 3-month wash-out period followed by a 12-month intervention and observation period. Randomisation determines only the sequence in which octenidine-impregnated or placebo wash mitts are used. ICUs are left unaware of what mitts packages they are using.The two coprimary endpoints are ICU-acquired primary bacteraemia and ICU-acquired MDRO. Endpoints are defined based on individual ward-movement history and microbiological test results taken from the hospital information systems without need for extra documentation. Data on clinical symptoms of infection are not collected. EFFECT aims at recruiting about 45 ICUs with about 225 000 patient-days per year.

ETHICS AND DISSEMINATION

The study was approved by the ethics committee of the University of Leipzig (number 340/16-ek) in November 2016. Findings will be published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

DRKS-ID: DRKS00011282.