Maaike S. M. van Mourik

Embryonic implantation: cytokines, adhesion molecules, and immune cells in establishing an implantation environment.

Successful implantation is an absolute requirement for the reproduction of species, including humans. The process by which a foreign blastocyst is accepted by the maternal endometrium is complex and requires interplay of many systems. Implantation occurs during the putative implantation window, in which the maternal endometrium is ready to accept the blastocyst, which on the other hand, also plays a specific role. It produces cytokines and chemokines and expresses adhesion molecules and certain classes of MHC molecules. We review the most important players in implantation. Concerning the cytokines, the establishment of controlled aggression is key; an excess of pro‐ or anti‐inflammation is detrimental to pregnancy outcome. Chemokines control the orientation of the embryo. The adhesion molecules are necessary to establish the required physical interaction between mother and blastocyst. Finally, immune cells and in particular, uterine NK and regulatory T cells are pivotal in inducing tolerance to the blastocyst. The aim of this review is to discuss mechanisms at play and their relative importance to the establishment of pregnancy.

Electronic implementation of a novel surveillance paradigm for ventilator-associated events. Feasibility and validation.

RATIONALE Accurate surveillance of ventilator-associated pneumonia (VAP) is hampered by subjective diagnostic criteria. A novel surveillance paradigm for ventilator-associated events (VAEs) was introduced. OBJECTIVES To determine the validity of surveillance using the new VAE algorithm. METHODS Prospective cohort study in two Dutch academic medical centers (2011-2012). VAE surveillance was electronically implemented and included assessment of (infection-related) ventilator-associated conditions (VAC, IVAC) and VAP. Concordance with ongoing prospective VAP surveillance was assessed, along with clinical diagnoses underlying VAEs and associated mortality of all conditions. Consequences of minor differences in electronic VAE implementation were evaluated. MEASUREMENTS AND MAIN RESULTS The study included 2,080 patients with 2,296 admissions. Incidences of VAC, IVAC, VAE-VAP, and VAP according to prospective surveillance were 10.0, 4.2, 3.2, and 8.0 per 1000 ventilation days, respectively. The VAE algorithm detected at most 32% of the patients with VAP identified by prospective surveillance. VAC signals were most often caused by volume overload and infections, but not necessarily VAP. Subdistribution hazards for mortality were 3.9 (95% confidence interval, 2.9-5.3) for VAC, 2.5 (1.5-4.1) for IVAC, 2.0 (1.1-3.6) for VAE-VAP, and 7.2 (5.1-10.3) for VAP identified by prospective surveillance. In sensitivity analyses, mortality estimates varied considerably after minor differences in electronic algorithm implementation. CONCLUSIONS Concordance between the novel VAE algorithm and VAP was poor. Incidence and associated mortality of VAE were susceptible to small differences in electronic implementation. More studies are needed to characterize the clinical entities underlying VAE and to ensure comparability of rates from different institutions.

Effects of selective digestive decontamination (SDD) on the gut resistome

OBJECTIVES Selective digestive decontamination (SDD) is an infection prevention measure for critically ill patients in intensive care units (ICUs) that aims to eradicate opportunistic pathogens from the oropharynx and intestines, while sparing the anaerobic flora, by the application of non-absorbable antibiotics. Selection for antibiotic-resistant bacteria is still a major concern for SDD. We therefore studied the impact of SDD on the reservoir of antibiotic resistance genes (i.e. the resistome) by culture-independent approaches. METHODS We evaluated the impact of SDD on the gut microbiota and resistome in a single ICU patient during and after an ICU stay by several metagenomic approaches. We also determined by quantitative PCR the relative abundance of two common aminoglycoside resistance genes in longitudinally collected samples from 12 additional ICU patients who received SDD. RESULTS The patient microbiota was highly dynamic during the hospital stay. The abundance of antibiotic resistance genes more than doubled during SDD use, mainly due to a 6.7-fold increase in aminoglycoside resistance genes, in particular aph(2″)-Ib and an aadE-like gene. We show that aph(2″)-Ib is harboured by anaerobic gut commensals and is associated with mobile genetic elements. In longitudinal samples of 12 ICU patients, the dynamics of these two genes ranged from a ∼10(4) fold increase to a ∼10(-10) fold decrease in relative abundance during SDD. CONCLUSIONS ICU hospitalization and the simultaneous application of SDD has large, but highly individualized, effects on the gut resistome of ICU patients. Selection for transferable antibiotic resistance genes in anaerobic commensal bacteria could impact the risk of transfer of antibiotic resistance genes to opportunistic pathogens.

Automated Surveillance for Healthcare-Associated Infections: Opportunities for Improvement

Surveillance of healthcare-associated infections is a cornerstone of infection prevention programs, and reporting of infection rates is increasingly required. Traditionally, surveillance is based on manual medical records review; however, this is very labor intensive and vulnerable to misclassification. Existing electronic surveillance systems based on classification algorithms using microbiology results, antibiotic use data, and/or discharge codes have increased the efficiency and completeness of surveillance by preselecting high-risk patients for manual review. However, shifting to electronic surveillance using multivariable prediction models based on available clinical patient data will allow for even more efficient detection of infection. With ongoing developments in healthcare information technology, implementation of the latter surveillance systems will become increasingly feasible. As with current predominantly manual methods, several challenges remain, such as completeness of postdischarge surveillance and adequate adjustment for underlying patient characteristics, especially for comparison of healthcare-associated infection rates across institutions.

Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review

Objective Measuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI. Methods Systematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995–2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics. Results 57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances. Conclusions Administrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative.

Isoniazid Prophylactic Therapy for the Prevention of Tuberculosis in HIV Infected Adults: A Systematic Review and Meta-Analysis of Randomized Trials

Background Infection with Human Immunodeficiency virus (HIV) is an important risk factor for Tuberculosis (TB). Anti-Retroviral Therapy (ART) has improved the prognosis of HIV and reduced the risk of TB infected patients. Isoniazid Preventive Therapy (IPT) aims to reduce the development of active TB in patients with latent TB. Objective Systematically review and synthesize effect estimates of IPT for TB prevention in adult HIV patients. Secondary objectives were to assess the effect of IPT on HIV disease progression, all-cause mortality and adverse drug reaction (ADR). Search Strategy Electronic databases were searched to identify relevant articles in English available by September 11th 2015. Selection Criteria Research articles comparing IPT to placebo or no treatment in HIV infected adults using randomized clinical trials. Data Analysis A qualitative review included study-level information on randomization and treatment allocation. Effect estimates were pooled using random-effects models to account for between-study heterogeneity. Main Results This review assessed ten randomized clinical trials that assigned 7619 HIV patients to IPT or placebo. An overall 35% of TB risk reduction (RR = 0.65, 95% CI (0.51, 0.84)) was found in all participants, however, larger benefit of IPT was observed in Tuberculin Skin Test (TST) positive participants, with pooled relative risk reduction of 52% [RR = 0.48; 95% CI (0.29, 0.82)] and with a prediction interval ranging from 0.13 to 1.81. There was no statistically significant effect of IPT on TB occurrence in TST negative or unknown participants. IPT also reduced the risk of HIV disease progression in all participants (RR = 0.69; 95% CI (0.48, 0.99)) despite no benefits observed in TST strata. All-cause mortality was not affected by IPT although participants who had 12 months of IPT tend to have a reduced risk (RR = 0.65; 95% CI(0.47, 0.90)). IPT had an elevated, yet statistically non-significant, risk of adverse drug reaction [RR = 1.20; 95% CI (1.20, 1.71)]. Only a single study assessed the effect of IPT in combination with ART in preventing TB and occurrence of multi-drug resistant tuberculosis. Conclusions IPT use substantially contributes in preventing TB in persons with HIV in general and in TST positive individuals in particular. More evidence is needed to explain discrepancies in the protective effect of IPT in these individuals.

Automated Detection of Healthcare Associated Infections: External Validation and Updating of a Model for Surveillance of Drain-Related Meningitis

Objective Automated surveillance of healthcare-associated infections can improve efficiency and reliability of surveillance. The aim was to validate and update a previously developed multivariable prediction model for the detection of drain-related meningitis (DRM). Design Retrospective cohort study using traditional surveillance by infection control professionals as reference standard. Patients Patients receiving an external cerebrospinal fluid drain, either ventricular (EVD) or lumbar (ELD) in a tertiary medical care center. Children, patients with simultaneous drains, <1 day of follow-up or pre-existing meningitis were excluded leaving 105 patients in validation set (2010–2011) and 653 in updating set (2004–2011). Methods For validation, the original model was applied. Discrimination, classification and calibration were assessed. For updating, data from all available years was used to optimally re-estimate coefficients and determine whether extension with new predictors is necessary. The updated model was validated and adjusted for optimism (overfitting) using bootstrapping techniques. Results In model validation, the rate of DRM was 17.4/1000 days at risk. All cases were detected by the model. The area under the ROC curve was 0.951. The positive predictive value was 58.8% (95% CI 40.7–75.4) and calibration was good. The revised model also includes Gram stain results. Area under the ROC curve after correction for optimism was 0.963 (95% CI 0.953– 0.974). Group-level prediction was adequate. Conclusions The previously developed multivariable prediction model maintains discriminatory power and calibration in an independent patient population. The updated model incorporates all available data and performs well, also after elaborate adjustment for optimism.

Comparative gut microbiota and resistome profiling of intensive care patients receiving selective digestive tract decontamination and healthy subjects

BackgroundThe gut microbiota is a reservoir of opportunistic pathogens that can cause life-threatening infections in critically ill patients during their stay in an intensive care unit (ICU). To suppress gut colonization with opportunistic pathogens, a prophylactic antibiotic regimen, termed “selective decontamination of the digestive tract” (SDD), is used in some countries where it improves clinical outcome in ICU patients. Yet, the impact of ICU hospitalization and SDD on the gut microbiota remains largely unknown. Here, we characterize the composition of the gut microbiota and its antimicrobial resistance genes (“the resistome”) of ICU patients during SDD and of healthy subjects.ResultsFrom ten patients that were acutely admitted to the ICU, 30 fecal samples were collected during ICU stay. Additionally, feces were collected from five of these patients after transfer to a medium-care ward and cessation of SDD. Feces from ten healthy subjects were collected twice, with a 1-year interval. Gut microbiota and resistome composition were determined using 16S rRNA gene phylogenetic profiling and nanolitre-scale quantitative PCRs.The microbiota of the ICU patients differed from the microbiota of healthy subjects and was characterized by lower microbial diversity, decreased levels of Escherichia coli and of anaerobic Gram-positive, butyrate-producing bacteria of the Clostridium clusters IV and XIVa, and an increased abundance of Bacteroidetes and enterococci. Four resistance genes (aac(6′)-Ii, ermC, qacA, tetQ), providing resistance to aminoglycosides, macrolides, disinfectants, and tetracyclines, respectively, were significantly more abundant among ICU patients than in healthy subjects, while a chloramphenicol resistance gene (catA) and a tetracycline resistance gene (tetW) were more abundant in healthy subjects.ConclusionsThe gut microbiota of SDD-treated ICU patients deviated strongly from the gut microbiota of healthy subjects. The negative effects on the resistome were limited to selection for four resistance genes. While it was not possible to disentangle the effects of SDD from confounding variables in the patient cohort, our data suggest that the risks associated with ICU hospitalization and SDD on selection for antibiotic resistance are limited. However, we found evidence indicating that recolonization of the gut by antibiotic-resistant bacteria may occur upon ICU discharge and cessation of SDD.

Automated Detection of External Ventricular and Lumbar Drain-Related Meningitis Using Laboratory and Microbiology Results and Medication Data

Objective Monitoring of healthcare-associated infection rates is important for infection control and hospital benchmarking. However, manual surveillance is time-consuming and susceptible to error. The aim was, therefore, to develop a prediction model to retrospectively detect drain-related meningitis (DRM), a frequently occurring nosocomial infection, using routinely collected data from a clinical data warehouse. Methods As part of the hospital infection control program, all patients receiving an external ventricular (EVD) or lumbar drain (ELD) (2004 to 2009; n = 742) had been evaluated for the development of DRM through chart review and standardized diagnostic criteria by infection control staff; this was the reference standard. Children, patients dying <24 hours after drain insertion or with <1 day follow-up and patients with infection at the time of insertion or multiple simultaneous drains were excluded. Logistic regression was used to develop a model predicting the occurrence of DRM. Missing data were imputed using multiple imputation. Bootstrapping was applied to increase generalizability. Results 537 patients remained after application of exclusion criteria, of which 82 developed DRM (13.5/1000 days at risk). The automated model to detect DRM included the number of drains placed, drain type, blood leukocyte count, C-reactive protein, cerebrospinal fluid leukocyte count and culture result, number of antibiotics started during admission, and empiric antibiotic therapy. Discriminatory power of this model was excellent (area under the ROC curve 0.97). The model achieved 98.8% sensitivity (95% CI 88.0% to 99.9%) and specificity of 87.9% (84.6% to 90.8%). Positive and negative predictive values were 56.9% (50.8% to 67.9%) and 99.9% (98.6% to 99.9%), respectively. Predicted yearly infection rates concurred with observed infection rates. Conclusion A prediction model based on multi-source data stored in a clinical data warehouse could accurately quantify rates of DRM. Automated detection using this statistical approach is feasible and could be applied to other nosocomial infections.

Surveillance for control of antimicrobial resistance

Antimicrobial resistance poses a growing threat to public health and the provision of health care. Its surveillance should provide up-to-date and relevant information to monitor the appropriateness of therapy guidelines, antibiotic formulary, antibiotic stewardship programmes, public health interventions, infection control policies, and antimicrobial development. In Europe, although the European Antimicrobial Resistance Surveillance Network provides annual reports on monitored resistant bacteria, national surveillance efforts are still fragmented and heterogeneous, and have substantial structural problems and issues with laboratory data. Most incidence and prevalence data cannot be linked with relevant epidemiological, clinical, or outcome data. Genetic typing, to establish whether trends of antimicrobial resistance are caused by spread of resistant strains or by transfer of resistance determinants among different strains and species, is not routinely done. Furthermore, laboratory-based surveillance using only clinical samples is not likely to be useful as an early warning system for emerging pathogens and resistance mechanisms. Insufficient coordination of surveillance systems of human antimicrobial resistance with animal surveillance systems is even more concerning. Because results from food surveillance are considered commercially sensitive, they are rarely released publicly by regulators. Inaccurate or incomplete surveillance data delay a translational approach to the threat of antimicrobial resistance and inhibit the identification of relevant target microorganisms and populations for research and the revitalisation of dormant drug-discovery programmes. High-quality, comprehensive, and real-time surveillance data are essential to reduce the burden of antimicrobial resistance. Improvement of national antimicrobial resistance surveillance systems and better alignment between human and veterinary surveillance systems in Europe must become a scientific and political priority, coordinated with international stakeholders within a global approach to reduce the burden of antimicrobial resistance.