Background People with type 2 diabetes (T2D) and chronic kidney disease (CKD) are at high risk for heart failure (HF) and premature death from cardiovascular (CV) causes. The FLOW (Research Study To See How Semaglutide Works Compared to Placebo in People With Type 2 Diabetes and Chronic Kidney Disease), which enrolled participants with T2D and CKD, demonstrated that semaglutide, a glucagon-like peptide-1 receptor agonist, reduced the incidence of the primary composite outcome (persistent ≥50% decline in estimated glomerular filtration rate, persistent estimated glomerular filtration rate <15 mL/min/1.73 m2, kidney replacement therapy, and kidney or CV death) by 24%. Objectives This prespecified analysis examined the effects of semaglutide on HF outcomes in this high-risk population. Methods Participants were randomized (1:1) to once-weekly subcutaneous semaglutide 1 mg or placebo. The prespecified main outcome was a composite of HF events (new onset or worsening of HF leading to an unscheduled hospital admission or an urgent visit, with initiation of or intensified diuretic/vasoactive therapy) or CV death. HF data were collected by the investigator. CV death was adjudicated by an independent committee. Results A total of 3,533 randomized participants were followed for a median of 3.4 years. HF was present at baseline in 342 participants (19.4%) in the semaglutide group and 336 (19.0%) in the placebo group. In the overall trial population, semaglutide increased time to first HF events or CV death (HR: 0.73; 95% CI: 0.62-0.87; P = 0.0005), HF events alone (HR: 0.73; 95% CI: 0.58-0.92; P = 0.0068), and CV death alone (HR: 0.71; 95% CI: 0.56-0.89; P = 0.0036). The risk reduction for the composite HF outcome was similar in those with (HR: 0.73; 95% CI: 0.54-0.98; P = 0.0338) and without (HR: 0.72; 95% CI: 0.58-0.89; P = 0.0028) HF at baseline. The risk of HF outcomes (HF events or CV death) was generally higher in participants categorized as NYHA functional class III and those with the HF reduced ejection fraction subtype, regardless of treatment. Conclusions Semaglutide substantially reduced the risk of time to first composite outcome of HF events or CV death, as well as HF events and CV death alone, in a high-risk population with T2D and CKD. These effects were consistent regardless of history of HF. (A Research Study To See How Semaglutide Works Compared to Placebo in People With Type 2 Diabetes and Chronic Kidney Disease [FLOW]; NCT03819153)

Background: We aimed to test the accuracy of an electronic hand hygiene monitoring system (EHHMS) dur -ing daily clinical activities in different wards and with varying health care professions.Methods: The accuracy of an EHHMS (Sani Nudge) was assessed during real clinical conditions by comparing events registered by two observers in parallel with events registered by the EHHMS. The events were catego-rized as true-positive, false-positive, and false-negative registrations. Sensitivity and positive predictive value (PPV) were calculated.Results: A total of 103 events performed by 25 health care workers (9 doctors, 11 nurses, and 5 cleaning assistants) were included in the analyses. The EHHMS had a sensitivity of 100% and a PPV of 100% when mea-suring alcohol-based hand rub. When looking at the hand hygiene opportunities of all health care workers combined taking place in the patient rooms and working rooms, the sensitivity was 75% and the PPV 95%. For doctors' and nurses' taking care of patients in their beds the EHHMS had a sensitivity of 100% and a PPV of 94%.Conclusions: The objective accuracy measures demonstrate that this EHHMS can capture hand hygiene behavior under clinical conditions in different settings with clinical health care workers but show less accuracy with cleaning assistants.(c) 2022 The Author(s). Published by Elsevier Inc. on behalf of Association for Professionals in Infection Control and Epidemiology, Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Background: Hand hygiene (HH) by healthcare workers (HCWs) is one of the most important measures to prevent hospital-acquired infections. However, HCWs struggle to adhere to HH guidelines. We aimed to investigate the effect of a non-resource intensive intervention with group and individual feedback on HCWs HH in a real-life clinical practice during the COVID-19 pandemic.Methods: In 2021, an 11-month prospective, interventional study was conducted in two inpatient departments at a Danish university hospital. An automated hand hygiene monitoring system (Sani NudgeTM) was used to collect data. HH opportunities and alcohol-based hand rub events were measured. Data were provided as HH compliance (HHC) rates. We compared HHC across 1) a baseline period, 2) an intervention period with weekly feedback in groups, followed by 3) an intervention period with weekly individual feedback on emails, and 4) a follow-up period.Results: We analyzed data from physicians (N=65) and nurses (N=109). In total, 231,022 hygiene opportunities were analyzed. Overall, we observed no significant effect of feedback, regardless of whether it was provided to the group or individuals. We found a trend toward a higher HHC in staff restrooms than in medication rooms and patient rooms. The lowest HHC was found in patient rooms.Conclusions: The automated hand hygiene monitoring system enabled assessment of the interventions. We found no significant effect of group or individual feedback at the two departments. However, other factors may have influenced the results during the pandemic, such as time constraints, workplace culture, and the degree of leadership support.(c) 2023 The Authors. Published by Elsevier Ltd on behalf of The Healthcare Infection Society. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Background: Achieving high hand hygiene compliance among health care workers is a challenge, requiring effective interventions. This study investigated the impact of individualized feedback on hand hygiene compliance using an electronic monitoring system. Methods: A quasi-experimental intervention design with pretest-post-test was conducted in an orthopedic surgical ward. Participants served as their own controls. A 3-month baseline was followed by a 3-month intervention period. Hand hygiene events were recorded through sensors on dispensers, name tags, and near patient beds. Health care workers received weekly email feedback reports comparing their compliance with colleagues. Results: Nineteen health care workers (17 nurses, 2 doctors) were included. Hand hygiene compliance significantly improved by approximately 15% (P < .0001) across all rooms during the intervention. The most substantial improvement occurred in patient rooms (17%, P < .0001). Compliance in clean and contaminated rooms increased by 10% (P = .0068) and 5% (P = .0232). The average weekly email open rate for feedback reports was 46%. Conclusions: Individualized feedback via email led to significant improvements in hand hygiene compliance among health care workers. The self-directed approach proved effective, and continuous exposure to the intervention showed promising results. (c) 2023 The Author(s). Published by Elsevier Inc. on behalf of Association for Professionals in Infection Control and Epidemiology, Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Background: Hospital-acquired infections are the most frequent adverse events in health care and can be reduced by improving the hand hygiene compliance (HHC) of health care workers (HCWs). We aimed to investigate the effect of nudging with sensor lights on HCWs' HHC. Methods: An 11-month intervention study was conducted in 2 inpatient departments at a university hospital. An automated monitoring system (Sani NudgeTM) measured the HHC. Reminder and feedback nudges with lights were displayed on alcohol-based hand rub dispensers. We compared the baseline HHC with HHC during periods of nudging and used the follow-up data to establish if a sustained effect had been achieved. Results: A total of 91 physicians, 135 nurses, and 15 cleaning staff were enrolled in the study. The system registered 274,085 hand hygiene opportunities in patient rooms, staff restrooms, clean rooms, and unclean rooms. Overall, a significant, sustained effect was achieved by nudging with lights in relation to contact with patients and patient-near surroundings for both nurses and physicians. Furthermore, a significant effect was observed on nurses' HHC in restrooms and clean rooms. No significant effect was found for the cleaning staff. Conclusions: Reminder or feedback nudges with light improved and sustained physicians' and nurses' HHC, and constitute a new way of changing HCWs' hand hygiene behavior. (c) 2023 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

There is a need to establish validation standards that allow for comparison of automated hand hygiene sys-tems. To assess the accuracy of an innovative monitoring tool (Sani nudge), 2 test nurses performed clinical standard tasks while being observed by 2 infection preventionists. Data from the direct observations were compared with data obtained from the hand hygiene system (Sani nudge) using an independent-event approach. We identified 54 true-positive events (100% system accuracy) and 4 true-negative events (100% system accuracy). No false-positive or false-negative events were identified. We found this approach to be feasible and clinically useful to validate hand hygiene systems in the future. (c) 2021 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Ensuring the safety of healthcare workers is vital to overcome the ongoing COVID-19 pandemic. We here present an analysis of the social interactions between the healthcare workers at hospitals and nursing homes. Using data from an automated hand hygiene system, we inferred social interactions between healthcare workers to identify transmission paths of infection in hospitals and nursing homes. A majority of social interactions occurred in medication rooms and kitchens emphasising that health-care workers should be especially aware of following the infection prevention guidelines in these places. Using epidemiology simulations of disease at the locations, we found no need to quarantine all healthcare workers at work with a contagious colleague. Only 14.1% and 24.2% of the health-care workers in the hospitals and nursing homes are potentially infected when we disregard hand sanitization and assume the disease is very infectious. Based on our simulations, we observe a 41% and 26% reduction in the number of infected healthcare workers at the hospital and nursing home, when we assume that hand sanitization reduces the spread by 20% from people to people and 99% from people to objects. The analysis and results presented here forms a basis for future research to explore the potential of a fully automated contact tracing systems.

Background: Information about the long-term effects of hand hygiene (HH) interventions is needed. We aimed to investigate the change in HH compliance (HHC) of healthcare workers (HCWs) once a data-driven feedback intervention was stopped, and to assess if the COVID-19 pandemic influenced the HH behavior. Methods: We conducted an observational, extension trial in a surgical department between January 2019 -December 2020. Doctors (n = 19) and nurses (n = 53) were included and their HHC was measured using an electronic HH monitoring system (EHHMS). We compared the changes in HHC during 3 phases: (1) Intervention (data presentation meetings), (2) Prepandemic follow-up and (3) Follow-up during COVID-19. Results: The HHC during phase 1 (intervention), phase 2 (prepandemic follow-up) and phase 3 (follow-up during COVID-19) was 58%, 46%, and 34%, respectively. Comparison analyses revealed that the HHC was significantly lower in the prepandemic follow-up period (46% vs 58%, P < .0001) and in the follow-up period during COVID-19 (34% vs 58%, P < .0001) compared with the intervention period (phase 1). Conclusions: Despite the COVID-19 pandemic, the HHC of the HCWs significantly decreased over time once the data presentation meetings from management stopped. This study demonstrates that HCWs fall back into old HH routines once improvement initiatives are stopped. (c) 2021 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

We read with great interest the recent manuscript by Hansen et al. describing the validation of an automated hand hygiene monitoring system (AHHMS).1Hansen MB Wismath N Fritz E Heininger A. Assessing the clinical accuracy of a hand hygiene system: learnings from a validation study [e-pub ahead of print].Am J Infect Control. 2021; (Accessed April 5, 2021)https://doi.org/10.1016/j.ajic.2021.01.006Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar We agree with the authors’ statement that validation of system accuracy is critical to widespread acceptance and adoption. In the manuscript, the authors reported that “only a few studies have calculated the accuracy of such solutions, and they only focused on room entries and exits.” Through this statement, Hansen et al. are inferring that at the time of their publication, no AHHMS study had included soap and alcohol-based hand rub (ABHR) dispensers in the validation methodology. To this end, we respectfully disagree; numerous AHHMS studies have included both soap and ABHR dispenser validation in their methodology.2Cheng VC Tai JW Ho SK et al.Introduction of an electronic monitoring system for monitoring compliance with Moments 1 and 4 of the WHO “My 5 Moments for Hand Hygiene” methodology.BMC Infect Dis. 2011; 11: 151Crossref PubMed Scopus (75) Google Scholar, 3Sahud AG Bhanot N Radhakrishnan A Bajwa R Manyam H Post JC. An electronic hand hygiene surveillance device: a pilot study exploring surrogate markers for hand hygiene compliance.Infect Control Hosp Epidemiol. 2010; 31: 634-639Crossref PubMed Scopus (38) Google Scholar, 4Pineles LL Morgan DJ Limper HM et al.Accuracy of a radiofrequency identification (RFID) badge system to monitor hand hygiene behavior during routine clinical activities.Am J Infect Control. 2014; 42: 144-147Abstract Full Text Full Text PDF PubMed Scopus (56) Google Scholar, 5Srigley JA Furness CD Baker GR Gardam M. Quantification of the Hawthorne effect in hand hygiene compliance monitoring using an electronic monitoring system: a retrospective cohort study.BMJ Qual Saf. 2014; 23: 974-980Crossref PubMed Scopus (129) Google Scholar, 6Limper HM Slawsky L Garcia-Houchins S Mehta S Hershow RC Landon E. Assessment of an aggregate-level hand hygiene monitoring technology for measuring hand hygiene performance among healthcare personnel.Infect Control Hosp Epidemiol. 2017; 38: 348-352Crossref PubMed Scopus (14) Google Scholar, 7Michael H Einloth C Fatica C Janszen T Fraser TG. Durable improvement in hand hygiene compliance following implementation of an automated observation system with visual feedback.Am J Infect Control. 2017; 45: 311-313Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar, 8Larson EL Murray MT Cohen B et al.Behavioral interventions to reduce infections in pediatric long-term care facilities: the keep it clean for kids trial.Behav Med. 2018; 44: 141-150Crossref PubMed Scopus (9) Google Scholar, 9Doll ME Masroor N Cooper K et al.A comparison of the accuracy of two electronic hand hygiene monitoring systems.Infect Control Hosp Epidemiol. 2019; 40: 1194-1197Crossref PubMed Scopus (10) Google Scholar, 10Conway L Moore C Coleman BL McGeer A. Frequency of hand hygiene opportunities in patients on a general surgery service.Am J Infect Control. 2020; 48: 490-495Abstract Full Text Full Text PDF PubMed Scopus (4) Google Scholar In their background discussion, Hansen et al. outlined four criteria for AHHMS validation. They recommended that the method allow for comparison with direct observation, include testing during actual clinical practice, utilize an approach that is uncomplicated and time-efficient, and generate data to which standard statistical measures can be applied to estimate system accuracy. The authors concluded that to their knowledge “no currently described method meets these criteria.” While we agree that these are well-reasoned criteria, we disagree that there are no currently described methods that meet stated criteria. The authors themselves cite a Letter to the Editor by Limper et al.11Limper HM Garcia-Houchins S Slawsky L Hershow RC Landon E. A validation protocol: assessing the accuracy of hand hygiene monitoring technology.Infect Control Hosp Epidemiol. 2016; 37: 1002-1004Crossref PubMed Scopus (16) Google Scholar outlining an AHHMS validation protocol that indeed meets each of the four criteria outlined. However, Hansen et al. incorrectly stated that the protocol focuses only on the technical aspects and “does not take into account the behavior of healthcare workers.” In fact, Limper et al. state specifically that the process “must be tested in actual clinical practice to avoid overestimation or underestimation of accuracy.” They also include both a process and a map delineating their behavioral path validation approach. Of greatest importance is that the authors overlooked another publication by Limper et al.: Assessment of an Aggregate-Level Hand Hygiene Monitoring Technology for Measuring Hand Hygiene Performance Among Healthcare Personnel.6Limper HM Slawsky L Garcia-Houchins S Mehta S Hershow RC Landon E. Assessment of an aggregate-level hand hygiene monitoring technology for measuring hand hygiene performance among healthcare personnel.Infect Control Hosp Epidemiol. 2017; 38: 348-352Crossref PubMed Scopus (14) Google Scholar In this study, Limper et al. describe in detail how they carried out their validation protocol in three separate buildings across a 680-bed academic medical center. During the planned path validation phase, they recorded 4,872 independent events by purposely activating each room entry/exit activity monitor and each soap and ABHR dispenser to test for technical accuracy of the devices. During the behavioral path validation phase, trained study investigators recorded 5,539 independent events while observing healthcare workers during the course of their natural workflow. Data from each phase were compared to AHHMS data, and standard statistical measures were applied to estimate system accuracy. As stated previously, we agree with Hansen et al. that validation of AHHMS is critical to their acceptance and adoption. However, their manuscript described a simulated study (using practiced predefined test scenarios and technicians as pretend patients). To this end, the title may be misleading to those who do not read the entire publication. Further, the study was not robust enough to be valid statistically. Finally, performing a thorough literature review is a basic step in the research process to demonstrate an evidence-based, comprehensive understanding of the study topic. Hansen et al. misrepresented existing published research and failed to recognize numerous studies that have laid important groundwork in the support of AHHMS and the validation process. Authors’ responseAmerican Journal of Infection ControlVol. 49Issue 6PreviewThe authors would like to thank Moore et al. for their interest in our article. In their letter, they reference several interesting studies, but they do not meet all four methodological criteria suggested in our article. Importantly, the studies are laborious, have a complex setup or do not report on all four key epidemiological statistics: sensitivity, specificity, positive predictive value, and negative predictive value, as otherwise deemed necessary.1,2 Full-Text PDF

BACKGROUND Necrotizing soft-tissue infections (NSTIs) are rapidly progressing infections frequently complicated by septic shock and associated with high mortality. Early diagnosis is critical for patient outcome, but challenging due to vague initial symptoms. Here, we identified predictive biomarkers for NSTI clinical phenotypes and outcomes using a prospective multicenter NSTI patient cohort. METHODS Luminex multiplex assays were used to assess 36 soluble factors in plasma from NSTI patients with positive microbiological cultures (n = 251 and n = 60 in the discovery and validation cohorts, respectively). Control groups for comparative analyses included surgical controls (n = 20), non-NSTI controls (i.e., suspected NSTI with no necrosis detected upon exploratory surgery, n = 20), and sepsis patients (n = 24). RESULTS Thrombomodulin was identified as a unique biomarker for detection of NSTI (AUC, 0.95). A distinct profile discriminating mono- (type II) versus polymicrobial (type I) NSTI types was identified based on differential expression of IL-2, IL-10, IL-22, CXCL10, Fas-ligand, and MMP9 (AUC >0.7). While each NSTI type displayed a distinct array of biomarkers predicting septic shock, granulocyte CSF (G-CSF), S100A8, and IL-6 were shared by both types (AUC >0.78). Finally, differential connectivity analysis revealed distinctive networks associated with specific clinical phenotypes. CONCLUSIONS This study identifies predictive biomarkers for NSTI clinical phenotypes of potential value for diagnostic, prognostic, and therapeutic approaches in NSTIs. TRIAL REGISTRATION ClinicalTrials.gov NCT01790698. FUNDING Center for Innovative Medicine (CIMED); Region Stockholm; Swedish Research Council; European Union; Vinnova; Innovation Fund Denmark; Research Council of Norway; Netherlands Organisation for Health Research and Development; DLR Federal Ministry of Education and Research; and Swedish Children’s Cancer Foundation.

Background: Evidence-based practices to increase hand hygiene compliance (HHC) among health care workers are warranted. We aimed to investigate the effect of a multimodal strategy on HHC. Methods: During this 14-month prospective, observational study, an automated monitoring system was implemented in a 29-bed surgical ward. Hand hygiene opportunities and alcohol-based hand rubbing events were measured in patient and working rooms (medication, utility, storerooms, toilets). We compared baseline HHC of health care workers across periods with light-guided nudging from sensors on dispensers and data-driven performance feedback (multimodal strategy) using the Student's t test. Results: The doctors (n = 10) significantly increased their HHC in patient rooms (16% vs 42%, P< .0001) and working rooms (24% vs 78%, P= .0006) when using the multimodal strategy. The nurses (n = 26) also increased their HHC significantly from baseline in both patient rooms (27% vs 43%, P = .0005) and working rooms (39% vs 64%, P< .0001). The nurses (n = 9), who subsequently received individual performance feedback, further increased HHC, compared with the period when they received group performance feedback (patient rooms: 43% vs 55%, P< .0001 and working rooms: 64% vs 80%, P< .0001). Conclusions: HHC of doctors and nurses can be significantly improved with light-guided nudging and data driven performance feedback using an automated hand hygiene system. (c) 2020 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Aim: We assessed whether different complement factors and complement activation products were associated with poor outcome in patients with necrotizing soft-tissue infection (NSTI). Methods: We conducted a prospective, observational study in an intensive care unit where treatment of NSTI is centralized at a national level. In 135 NSTI patients and 65 control patients, admission levels of MASP-1, MASP-2, MASP-3, C4, C3, complement activation products C4c, C3bc, and terminal complement complex (TCC) were assessed. Results: The 90-day mortality was 23%. In a Cox regression model adjusted for sex, and SAPS II, a higher than median MASP-1 (HR 0.378, CI 95% [0.164–0.872], p = 0.0226) and C4 (HR 0.162, 95% CI [0.060–0.438], p = 0.0003), C4c/C4 ratio (HR 2.290 95% CI [1.078–4.867], p = 0.0312), C3bc (HR 2.664 95% CI [1.195–5.938], p = 0.0166), and C3bc/C3 ratio (HR 4.041 95% CI [1.673–9.758], p = 0.0019) were associated with 90-day mortality, while MASP-2, C4c, C3, and TCC were not. C4 had the highest ROC-AUC (0.748, [95% CI 0.649–0.847]), which was comparable to the AUC for SOFA score (0.753, [95% CI 0.649–0.857]), and SAPS II (0.862 [95% CI 0.795–0.929]). Conclusion: In adjusted analyses, high admission levels of the C4c/C4 ratio, C3bc, and the C3bc/C3 ratio were significantly associated with a higher risk of death after 90 days while high admission levels of MASP-1 and C4 were associated with lower risk. In this cohort, these variables are better predictors of mortality in NSTI than C-reactive protein and Procalcitonin. C4's ability to predict mortality was comparable to the well-established scoring systems SAPS score II and SOFA on day 1.

Aim: To investigate risk factors associated with kidney disorders in patients with type 2 diabetes (T2D) at high cardiovascular (CV) risk. Methods: In DEVOTE, a cardiovascular outcomes trial, 7637 patients were randomised to insulin degludec (degludec) or insulin glargine 100 units/mL (glargine U100), with standard of care. In these exploratory post hoc analyses, serious adverse event reports were searched using Standardised MedDRA® Queries related to chronic kidney disease (CKD) or acute kidney injury (AKI). Baseline predictors of CKD, AKI and change in estimated glomerular filtration rate (eGFR) were identified using stepwise selection and Cox or linear regression. Results: Over 2 years, eGFR (mL/min/1.73 m2) decline was small and similar between treatments (degludec: 2.70; glargine U100: 2.92). Overall, 97 and 208 patients experienced CKD and AKI events, respectively. A history of heart failure was a risk factor for CKD (hazard ratio [HR] 1.97 [95% confidence interval [CI] 1.41; 2.75]) and AKI (HR 2.28 [95% CI 1.64; 3.17]). A history of hepatic impairment was a significant predictor of CKD (HR 3.28 [95% CI 2.12; 5.07]) and change in eGFR (estimate: −8.59 [95% CI −10.20; −7.00]). Conclusion: Our findings indicate that traditional, non-modifiable risk factors for kidney disorders apply to insulin-treated patients with T2D at high CV risk. Trial registration: NCT01959529 (ClinicalTrials.gov).

Necrotizing soft-tissue infections (NSTIs) have multiple causes, risk factors, anatomical locations, and pathogenic mechanisms. In patients with NSTI, circulating metabolites may serve as a substrate having impact on bacterial adaptation at the site of infection. Metabolic signatures associated with NSTI may reveal the potential to be useful as diagnostic and prognostic markers and novel targets for therapy. This study used untargeted metabolomics analyses of plasma from NSTI patients (n = 34) and healthy (noninfected) controls (n = 24) to identify the metabolic signatures and connectivity patterns among metabolites associated with NSTI. Metabolite-metabolite association networks were employed to compare the metabolic profiles of NSTI patients and noninfected surgical controls. Out of 97 metabolites detected, the abundance of 33 was significantly altered in NSTI patients. Analysis of metabolite-metabolite association networks showed a more densely connected network: specifically, 20 metabolites differentially connected between NSTI and controls. A selected set of significantly altered metabolites was tested in vitro to investigate potential influence on NSTI group A streptococcal strain growth and biofilm formation. Using chemically defined media supplemented with the selected metabolites, ornithine, ribose, urea, and glucuronic acid, revealed metabolite-specific effects on both bacterial growth and biofilm formation. This study identifies for the first time an NSTI-specific metabolic signature with implications for optimized diagnostics and therapies.

INTRODUCTION:Asymmetric dimethylarginine (ADMA), an endogenous inhibitor of the nitric oxide system, may be associated with an adverse outcome in critically ill patients. The aim of the present review was to clarify if plasma ADMA and the arginine-to-ADMA ratio (arginine/ADMA) are associated with mortality in critically ill patients. METHODS:We searched PubMed, EMBASE and Web of Science/BIOSIS Previews on 31 July 2017 for studies published after 2000 including critically ill paediatric or adult patients and evaluating any association between all-cause mortality and admission ADMA and/or arginine/ADMA ratio. We pooled data from studies providing sufficient data in random effects meta-analyses. RESULTS:We identified 15 studies including a total of 1300 patients. These studies have a medium to high risk of bias and substantial clinical heterogeneity. After contacting authors for homogenous data, six studies including 705 patients could be included in a formal meta-analysis. This analysis revealed a strong association between high plasma ADMA upon admission and mortality (pooled odds ratio 3.13; 95% confidence interval (CI) 1.78-5.51). A significant association between ADMA/arginine ratio and mortality was found in two studies only (54 patients) out of a total of six studies (564 patients). CONCLUSIONS:A high plasma ADMA level upon admission is strongly associated with mortality in critically ill patients. However, there is no association between the arginine/ADMA ratio and mortality in this group of patients. The pathophysiological role of ADMA in circulatory collapse and its potential as a target for intervention remains to be explored.

Introduction The associations of chronic kidney disease (CKD) severity, cardiovascular disease (CVD), and insulin with the risks of major adverse cardiovascular events (MACE), mortality, and severe hypoglycemia in patients with type 2 diabetes (T2D) at high cardiovascular (CV) risk are not known. This secondary, pooled analysis of data from the DEVOTE trial examined whether baseline glomerular filtration rate (GFR) categories were associated with a higher risk of these outcomes. Methods DEVOTE was a treat-to-target, double-blind trial involving 7637 patients with T2D at high CV risk who were randomized to once-daily treatment with either insulin degludec (degludec) or insulin glargine 100 units/mL (glargine U100). Patients with estimated GFR data at baseline ( n = 7522) were analyzed following stratification into four GFR categories. Results The risks of MACE, CV death, and all-cause mortality increased with worsening baseline GFR category ( P < 0.05), with a trend towards higher rates of severe hypoglycemia. Patients with prior CVD, CKD (estimated GFR < 60 mL/min/m 2 ), or both were at higher risk of MACE, CV death, and all-cause mortality. Only CKD was associated with a higher rate of severe hypoglycemia, and the risk of MACE was higher in patients with CVD than in those with CKD ( P = 0.0003). There were no significant interactions between randomized treatment and GFR category. Conclusion The risks of MACE, CV death, and all-cause mortality were higher with lower baseline GFR and with prior CVD, CKD, or both. The relative effects of degludec versus glargine U100 on outcomes were consistent across baseline GFR categories, suggesting that the lower rate of severe hypoglycemia associated with degludec use versus glargine U100 use was independent of baseline GFR category. Funding Novo Nordisk.

PURPOSE:Necrotising soft-tissue infections (NSTI) are characterised by necrosis, fast progression, and high rates of morbidity and mortality, but our knowledge is primarily derived from small prospective studies and retrospective studies.METHODS:We performed an international, multicentre, prospective cohort study of adults with NSTI describing patient's characteristics and associations between baseline variables and microbiological findings, amputation, and 90-day mortality.RESULTS:We included 409 patients with NSTI; 402 were admitted to the ICU. Cardiovascular disease [169 patients (41%)] and diabetes [98 (24%)] were the most common comorbidities; 122 patients (30%) had no comorbidity. Before surgery, bruising of the skin [210 patients (51%)] and pain requiring opioids [172 (42%)] were common. The sites most commonly affected were the abdomen/ano-genital area [140 patients (34%)] and lower extremities [126 (31%)]. Monomicrobial infection was seen in 179 patients (44%). NSTI of the upper or lower extremities was associated with monomicrobial group A streptococcus (GAS) infection, and NSTI located to the abdomen/ano-genital area was associated with polymicrobial infection. Septic shock [202 patients (50%)] and acute kidney injury [82 (20%)] were common. Amputation occurred in 22% of patients with NSTI of an extremity and was associated with higher lactate level. All-cause 90-day mortality was 18% (95% CI 14-22); age and higher lactate levels were associated with increased mortality and GAS aetiology with decreased mortality.CONCLUSIONS:Patients with NSTI were heterogeneous regarding co-morbidities, initial symptoms, infectious localisation, and microbiological findings. Higher age and lactate levels were associated with increased mortality, and GAS infection with decreased mortality.

Background: Hand hygiene compliance (HHC) among health care workers remains suboptimal, and good monitoring systems are lacking. We aimed to evaluate HHC using an automated monitoring system. Methods: A prospective, observational study was conducted at 2 Danish university hospitals employing a new monitoring system (Sani nudge). Sensors were located on alcohol-based sanitizers, health care worker name tags, and patient beds measuring hand hygiene opportunities and sanitations. Results: In total, 42 nurses were included with an average HHC of 52% and 36% in hospitals A and B, respectively. HHC was lowest in patient rooms (hospital A: 45%; hospital B: 29%) and highest in staff toilets (hospital A: 72%; hospital B: 91%). Nurses sanitized after patient contact more often than before, and sanitizers located closest to room exits and in hallways were used most frequently. There was no association found between HHC level and the number of beds in patient rooms. The HHC level of each nurse was consistent over time, and showed a positive correlation between the number of sanitations and HHC levels (hospital A: r = 0.69; hospital B: r = 0.58). Conclusions: The Sani nudge system can be used to monitor HHC at individual and group levels, which increases the understanding of compliance behavior. (C) 2019 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.