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BackgroundThe response in levels of very-low-density (VLDL) and low-density (LDL) lipoproteins varies substantially among hyperlipidemic patients during treatment with HMGCoA reductase inhibitors. Apolipoprotein E genotype and gender are known to contribute to the regulation of steady state levels of plasma lipoproteins. This study explores the effect of these and other potential determinants of the response of VLDL and LDL to treatment with reductase inhibitors.MethodsUsing mixed linear statistical models, the response of lipoprotein lipid values was studied in 142 hyperlipidemic individuals who were treated with reductase inhibitors. Patients received one or more of the following drugs individually for a total of 623 treatment observations: lovastatin, pravastatin, simvastatin, or atorvastatin. For evaluation of the effects of treatment in the aggregate, actual doses were expressed as equivalent doses of atorvastatin, using factors based on random assignment comparisons in 16 reported studies. The analysis factors considered were apolipoprotein E genotype, baseline average triglycerides >170 mg/dL (vs less), and gender.ResultsPresence of an apo epsilon4 allele was associated with a trend toward greater reduction of triglyceride levels and a diminished ability of the reductase inhibitors to reduce LDL cholesterol levels. Gender had only minimal effect on the response of either LDL cholesterol or triglycerides. However, the effect of elevated baseline triglycerides on the response of both triglycerides and LDL cholesterol was striking and was exerted in opposite directions. The triglyceride-lowering effect of reductase inhibitors was greater in patients with initial triglyceride levels above 170 mg/dL (P=0.0001). The effect was even greater in patients with initial triglyceride levels over 250 mg/dL (P=0.015). Conversely, for LDL cholesterol levels, elevated baseline triglycerides were associated with a significantly decreased response to the drugs (P=0.0015).ConclusionsThese findings indicate that baseline triglyceride levels are an important predictor of response of plasma lipoproteins to HMGCoA reductase inhibitors, perhaps reflecting fundamental differences in mechanism underlying the hyperlipidemic phenotype.

Objective: We aimed to describe the incidence, pathogens, and antimicrobial susceptibility of central line-associated bloodstream infections (CLABSI) in adult intensive care units (ICU)., Design: State surveillance data from 2011 to 2022 were analyzed to identify patient and device days and CLABSI events. Pathogen data were analyzed to determine the most common organisms and patterns of antimicrobial resistance grouped into 3-year time epochs., Setting: Adult ICU in Victoria, Australia., Participants: Healthcare organizations participating in CLABSI state surveillance., Results: 608 events were reported over 751,350 device days. Overall, CLABSI incidence was 0.81 per 1,000 central-line days, with a 49.3% rate reduction from 2011 to 2022 (1.39 to 0.70 per 1,000 central-line days). Overall device utilization ratio was 0.57, with a 15.4% reduction from 2011 to 2022 (0.67 vs 0.56). Of 690 pathogens, the most common by rank order were coagulase-negative Staphylococci (CNS), Candida species, Staphylococcus aureus , and Enterococcus faecalis . The proportion of CNS-causing events increased by 69.0% from 2011 to 2022; this trend was not observed for other organisms. For every increase in epoch, a 33% decrease in methicillin-resistant S. aureus (MRSA), 4% increase in vancomycin-resistant Enterococcus faecium , and 12% increase in ceftriaxone-resistant Escherichia coli pathogens were observed., Conclusions: We demonstrate a decreasing incidence of CLABSI in Victorian adult ICU and an increasing burden of infections due to CNS. No significant time trend increases in antimicrobial-resistant organisms, including MRSA, vancomycin-resistant E. faecium , and ceftriaxone-resistant E. coli were observed. These findings are relevant for identifying priorities for CLABSI prevention in Victorian adult ICU.

Background: The genetic basis of hypertriglyceridemia (HTG) is complex and includes variants in Lipase Maturation Factor 1 (LMF1), an endoplasmic reticulum (ER)-chaperone involved in the post-translational activation of lipoprotein lipase (LPL)., Objective: The objective of this study was to identify and functionally characterize biallelic LMF1 variants in patients with HTG., Methods: Genomic DNA sequencing was used to identify biallelic LMF1 variants in HTG patients without deleterious variants in LPL, apolipoprotein C-II (APOC2), glycosylphosphatidylinositol-anchored high-density lipoprotein binding protein 1 (GPIHBP1) or apolipoprotein A-V (APOA5). LMF1 variants were functionally evaluated by in silico analyses and assessing their impact on LPL activity, LMF1 protein expression and specific activity in transiently transfected HEK293 cells., Results: We identified four homozygous LMF1 variants in patients with severe HTG: two novel rare variants (p.Asn147Lys and p.Pro246Arg) and two low-frequency variants (p.Arg354Trp and p.Arg364Gln) previously reported at heterozygosity. We demonstrate that all four variants reduce the secretion of enzymatically active LPL by impairing the specific activity of LMF1, whereas p.Asn147Lys also diminishes LMF1 protein expression., Conclusion: This study extends the role of LMF1 as a genetic determinant in severe HTG and demonstrates that rare and low-frequency LMF1 variants can underlie this condition through distinct molecular mechanisms. The clinical phenotype of patients affected by partial loss of LMF1 function is consistent with Multifactorial Chylomicronemia Syndrome (MCS) and suggests that secondary factors and additional genetic determinants contribute to HTG in these subjects., Competing Interests: Conflict of Interest None., (Copyright © 2024. Published by Elsevier Inc.)

Chewing tobacco use poses serious health risks; yet it has not received as much attention as other tobacco-related products. This study synthesizes existing evidence regarding the health impacts of chewing tobacco while accounting for various sources of uncertainty. We conducted a systematic review and meta-analysis of chewing tobacco and seven health outcomes, drawing on 103 studies published from 1970 to 2023. We use a Burden of Proof meta-analysis to generate conservative risk estimates and find weak-to-moderate evidence that tobacco chewers have an increased risk of stroke, lip and oral cavity cancer, esophageal cancer, nasopharynx cancer, other pharynx cancer, and laryngeal cancer. We additionally find insufficient evidence of an association between chewing tobacco and ischemic heart disease. Our findings highlight a need for policy makers, researchers, and communities at risk to devote greater attention to chewing tobacco by both advancing tobacco control efforts and investing in strengthening the existing evidence base., (© 2024. The Author(s).)

Despite a gradual decline in smoking rates over time, exposure to secondhand smoke (SHS) continues to cause harm to nonsmokers, who are disproportionately children and women living in low- and middle-income countries. We comprehensively reviewed the literature published by July 2022 concerning the adverse impacts of SHS exposure on nine health outcomes. Following, we quantified each exposure-response association accounting for various sources of uncertainty and evaluated the strength of the evidence supporting our analyses using the Burden of Proof Risk Function methodology. We found all nine health outcomes to be associated with SHS exposure. We conservatively estimated that SHS increases the risk of ischemic heart disease, stroke, type 2 diabetes and lung cancer by at least around 8%, 5%, 1% and 1%, respectively, with the evidence supporting these harmful associations rated as weak (two stars). The evidence supporting the harmful associations between SHS and otitis media, asthma, lower respiratory infections, breast cancer and chronic obstructive pulmonary disease was weaker (one star). Despite the weak underlying evidence for these associations, our results reinforce the harmful effects of SHS on health and the need to prioritize advancing efforts to reduce active and passive smoking through a combination of public health policies and education initiatives., (© 2024. The Author(s).)

Background: African American smokers have 2.5 times higher risk for stroke compared with nonsmokers (higher than other races). About 50% of the African American population carry 1 or 2 genetic variants (G1 and G2; rare in other races) of the apolipoprotein L1 gene ( APOL1 ). Studies showed these variants may be associated with stroke. However, the role of the APOL1 risk variants in tobacco-related stroke is unknown., Methods and Results: In a cross-sectional study, we examined whether APOL1 risk variants modified the relationship between tobacco smoking and stroke prevalence in 513 African American adults recruited at University of California, San Francisco. Using DNA, plasma, and questionnaires we determined APOL1 variants, smoking status, and stroke prevalence. Using logistic regression models, we examined the association between smoking ( ever versus never smokers) and stroke overall, and among carriers of APOL1 risk variants (1 or 2 risk alleles), and noncarriers, separately. Among participants, 41% were ever ( current and past ) smokers, 54% were carriers of the APOL1 risk variants, and 41 had a history of stroke. The association between smoking and stroke differed by APOL1 genotype ( P interaction term =0.014). Among carriers, ever versus never smokers had odds ratio (OR) 2.46 (95% CI, 1.08-5.59) for stroke ( P =0.034); OR 2.00 (95% CI, 0.81-4.96) among carriers of 1 risk allele, and OR 4.72 (95% CI, 0.62-36.02) for 2 risk alleles. Among noncarriers, smoking was not associated with a stroke., Conclusions: Current and past smokers who carry APOL1 G1 and/or G2 risk variants may be more susceptible to stroke among the African American population.

Smoking is a major cause of premature and preventable death. Tobacco exposure has a detrimental effect on many organs and contributes to multiple diseases including chronic obstructive pulmonary disease (COPD), cardiovascular disease, cancer, and diabetes. Cytokines are inflammatory biomarkers that are mechanistically associated with smoking. Machine Learning algorithms allow for the quantitative assessment of the contributions of individual cytokines to tobacco-related diseases. The mapping of cytokines to disease can facilitate and direct treatment modalities. By the application of k Nearest Neighbor (k-NN) and Random Forest machine learning algorithms on 63 plasma cytokines we have demonstrated the classification of smoking. To ensure optimal results, performance improvement techniques such as k-fold cross validation and hyper parameter tuning are employed. Separability efficiency achieved by the models is evaluated using the Area Under the Receiver Operating Characteristic (AUROC) metric. The most significant cytokines that enabled the classification are identified and presented. The statistically significant difference for AUROC score of k-NN and Random Forest has been ascertained using the 2-sample independent t test. A reasonably good classification performance was achieved by k-NN algorithm with an AUROC metric of .87, and a 95% CI of (.823,.917). Random forest exceeded k-NN algorithm's performance, with a perfect AUROC score of 1 and a 95% CI of (1,1). From among the ten most prominent cytokines that contributed to the classification, the ones common to both algorithms are: LIF, IL22, G-CSF/CSF-3, TRAIL. AUROC scores for k-NN and Random Forest are significantly different (p-value = 5.105e-16). The discovery and transference of biomarkers such as cytokines from the platform of molecular investigation to clinical practice, can facilitate precision medicine-based therapeutic interventions., Competing Interests: Competing interests The authors report no conflicts of interest.

Cigarette smoking is a preventable epidemic that is a leading cause of death. It increases the risk of coronary heart disease, stroke, lung cancer, chronic obstructive lung diseases etc., multifold. Smoking tobacco is not only injurious to oneself but also to those who are exposed second hand. Smoking induces endothelial dysfunction via inflammatory cytokines that can be quantified precisely. Cytokines can be leveraged as powerful predictive biomarkers for identifying risk of potential diseases. Current advances in biomarker research are providing substantive evidence of the roles of cytokines in disease. This is driving precision-based diagnosis and translational therapeutic interventions. Innovative machine algorithms (ML) are pioneering transformative changes in the field of medical research. This research implements the Neural Networks (NN) algorithm to classify smokers versus non-smokers using 63 cytokines as predictor features. In addition to the fact that NN is a generative algorithm, which makes it a very powerful tool to achieve the objective of this differentiation, techniques like cross validation and hyperparameter tuning improve the efficacy of the algorithm. The study identified the 10 most impactful predictor features that contributed to the classification and then used these to characterize smokers versus non-smokers. Primarily, the study constructed and investigated two classifiers, of which the first implemented NN using the entire set of 63 cytokines and the second using 10 most informative cytokines. The performance of the first classifier, implemented using 63 cytokines, evaluated by area under receiver operating characteristic (AUROC), was extremely good with an AUROC score of .949 and 95% Confidence Interval (CI) (.923,.974). The second classifier that used the 10 most impactful cytokines with regard to the classification, demonstrated an exemplary performance, with an AUROC score of .995 and a 95% CI (.991,1). The 10 most impactful cytokines from the aspect of smoker versus non-smoker differentiation, listed in order of importance, include: I-TAC, IL-22, IL-2R, IL-3, HGF, IL-18, G-CSF-CSF-3, MIF, SDF-1alpha, MMP-1. To gain a deeper understanding of the effect of smoking on cytokine levels, a 2-sample independent t test was performed, ascertaining the statistical significance of the 63 cytokine levels in smokers versus non-smokers. Machine Learning using biomarkers such as cytokines will enhance the ability to predict the advent of a disease and its outcome, and lead to novel treatment strategies., Competing Interests: Competing interests The authors report no conflicts of interest.

Coronary artery disease (CAD) is a leading cause of mortality in the world. It is important to be able to proactively assess the risk of the disease, using novel biomarkers like cytokines that are indicators of inflammation in addition to traditional predictors of risk. Atherosclerosis, the primary cause of CAD, is an inflammatory disease involving cytokines. Identifying which cytokines are specifically altered can advance diagnosis and personalized treatment. Emerging research demonstrates that cytokines are transported on high density lipoproteins (HDL). Therefore, it is important to explore the roles of HDL-associated cytokines in vascular inflammation. Machine Learning (ML) algorithms are enhancing pioneering research from the standpoint of precision medicine. This technology can materially enable the translation of scientific research to clinical practice. In this study we implemented logistic regression and the derived regularized techniques using age and multidimensional cytokine biomarkers with the objective of identification of individuals "At Risk" for CAD. These techniques were further empowered by k-fold cross validation and hyper parameter tuning. Of the numerous algorithms investigated, the three most prominent ones, assessed based on area under receiver operating characteristic (AUROC) score are as follows: logistic regression, least absolute shrinkage, and selection operator (LASSO) regression with feature selection and ridge regression with feature selection. Logistic regression demonstrated an AUROC score of .85 with a 95% Confidence Interval CI (.804, .897), LASSO regression achieved a better AUROC score of .875 with a 95% CI (.832, .917) and finally ridge regression with feature selection exhibited the highest AUROC score of .878 with a 95% CI (.837, .92). The 2-sample independent t test proved that the three techniques were statistically significantly different from each other. With regard to the best classification demonstrated by ridge regression with feature selection, the most prominent biomarkers identified for the best classification achieved by ridge regression by feature selection, in the order of importance are as follows: Age, IL-7, RANTES, IFN-gamma, IL-3, GM-CSF, IL-15, IP-10, GCSF, IL-12. The identification and quantification of cytokines transported by HDL provide novel mechanistic insights that can inform the assessment of risk and therapeutic intervention in CAD., Competing Interests: Competing interests The authors report no conflicts of interest.

Objective: We explored the utility of the standardized infection ratio (SIR) for surgical site infection (SSI) reporting in an Australian jurisdiction., Design: Retrospective chart review., Setting: Statewide SSI surveillance data from 2013 to 2019., Patients: Individuals who had cardiac bypass surgery (CABG), colorectal surgery (COLO), cesarean section (CSEC), hip prosthesis (HPRO), or knee prosthesis (KPRO) procedures., Methods: The SIR was calculated by dividing the number of observed infections by the number of predicted infections as determined using the National Healthcare Safety Network procedure-specific risk models. In line with a minimum precision criterion, an SIR was not calculated if the number of predicted infections was <1., Results: A SIR >0 (≥1 observed SSI, predicted number of SSI ≥1, no missing covariates) could be calculated for a median of 89.3% of reporting quarters for CABG, 75.0% for COLO, 69.0% for CSEC, 0% for HPRO, and 7.1% for KPRO. In total, 80.6% of the reporting quarters, when the SIR was not calculated, were due to no observed infections or predicted infections <1, and 19.4% were due to missing covariates alone. Within hospitals, the median percentage of quarters during which zero infections were observed was 8.9% for CABG, 20.0% for COLO, 25.4% for CSEC, 67.3% for HPRO, and 71.4% for KPRO., Conclusions: Calculating an SIR for SSIs is challenging for hospitals in our regional network, primarily because of low event numbers and many facilities with predicted infections <1. Our SSI reporting will continue to use risk-indexed rates, in tandem with SIR values when predicted number of SSI ≥1., Competing Interests: All authors report no conflicts of interest relevant to this article., (© The Author(s) 2023.)

Background: For older persons, vaccination mitigates the harmful impact of vaccine preventable infections. Our study objectives were to evaluate in the Victorian public sector residential aged care services (PSRACS) (1) the existence of local vaccination policies and admission assessment practices, (2) the current documented status of resident influenza, pneumococcal and herpes zoster vaccination uptake and (3) changes in documented resident vaccination uptake over time., Methods: Standardised data were annually reported by all PSRACS between 2018 and 2022. The influenza, pneumococcal and herpes zoster vaccination status of each resident was classified as vaccinated, declined, contraindicated or unknown. Annual trends in vaccination status were assessed using Spearman's correlation., Results: In 2022, most PSRACS reported an influenza immunisation policy existed (87.1%) and new residents were assessed for their influenza vaccination status (97.2%); fewer PSRACS reported the same for pneumococcal disease (73.1% and 78.9%) and herpes zoster (69.3% and 75.6%). The median resident influenza, pneumococcal and herpes zoster (70-79 years old) vaccination uptake was 86.8%, 32.8% and 19.3% respectively. The median unknown status was 6.9%, 63.0% and 76.0% respectively. Statistical evidence of an increase in annual uptake was observed for the herpes zoster (all resident) surveillance module (r s  = 0.900, p = 0.037)., Conclusions: Our study showed local influenza vaccination policies and practices exist and influenza vaccination uptake was consistently high. Pneumococcal and herpes zoster vaccination uptake were lower. Quality improvement strategies that at least determine the status of those residents classified as unknown are required., (Copyright © 2023 Australasian College for Infection Prevention and Control. Published by Elsevier B.V. All rights reserved.)

Objective To determine the proportion of staff employed in smaller Victorian public acute healthcare facilities with evidence of immunity to hepatitis B. Methods For optimal long-term immunity, a completed hepatitis B vaccination course and post vaccination hepatitis B surface antibody (anti-HBs) level ≥10 mIU/mL is desirable for all high-risk staff employed in healthcare facilities. For the financial years 2016/17-2019/20, a standardised surveillance module developed by the Victorian Healthcare Associated Infection Surveillance System (VICNISS) Coordinating Centre was completed by the smaller Victorian public acute healthcare facilities (individual hospitals with Results A total of 88 healthcare facilities reported hepatitis B immunity status of high-risk (Category A) staff (n  = 29 920) at least once over 5 years; 55 healthcare facilities reported more than once. The aggregate proportion with evidence of optimal immunity was 66.3%. Healthcare facilities with 100-199 Category A staff employed reported the lowest evidence of optimal immunity (59.6%). Of all Category A staff with no evidence of optimal immunity, the majority had 'unknown' status (19.8%), with only 0.6% overall who declined vaccination. Conclusions Our study found evidence of optimal staff hepatitis B immunity in only two-thirds of Category A staff working in surveyed healthcare facilities.

Background: Variation of infection rates between hospitals must be identified; differences may highlight opportunities for quality improvement in healthcare delivery to specific hospitals groups., Aims: To analyse burden, time trends and risks of healthcare-associated (HA) Staphylococcus aureus bloodstream infections (SABSI) in patients admitted to Victorian metropolitan and non-metropolitan public acute care hospitals., Methods: SABSI surveillance data submitted between 1 July 2010 and 30 June 2020 by all 118 Victorian public acute care hospitals were analysed. Aligned with the Australian Statistical Geography Standard Remoteness Structure, these hospitals were classified as metropolitan (major cities) or non-metropolitan (inner regional, outer regional, remote or very remote)., Results: Most SABSI were community associated: 66.9% and 75.0% of metropolitan (n = 9441) and non-metropolitan (n = 2756) hospital SABSI respectively. The overall HA-SABSI rate was statistically higher in metropolitan hospitals (1.13 per 10 000 occupied bed days (OBD)) compared with non-metropolitan hospitals (0.82 per 10 000 OBD; P < 0.001). In metropolitan and non-metropolitan hospitals, there was a statistically significant decline in the overall HA-SABSI rate (incidence rate ratio = 0.96; 95% confidence interval: 0.95-0.97; P < 0.001; and incidence rate ratio = 0.98; 95% confidence interval: 0.97-1.00; P = 0.044, respectively). In metropolitan and non-metropolitan hospitals, HA-SABSI were frequently associated with central venous (52.8%) and peripheral intravenous (62.2%) catheter use respectively., Conclusion: To reduce risks for SABSI and improve patient outcomes, hospital infection prevention and control programmes should be tailored according to local epidemiology. In common geographic locations, networking of hospitals should be considered as a means of strengthening delivery of these programmes., (© 2021 Royal Australasian College of Physicians.)

Background and Objective: Inappropriate antimicrobial use can lead to adverse consequences, including antimicrobial resistance. The objective of our study was to describe patterns of prophylactic antimicrobial prescribing in Australian residential aged-care facilities and thereby provide insight into antimicrobial stewardship strategies that might be required., Methods: Annual point prevalence data submitted by participating residential aged-care facilities as part of the Aged Care National Antimicrobial Prescribing Survey between 2016 and 2020 were extracted. All antimicrobials except anti-virals were counted; methenamine hippurate was classified as an antibacterial agent., Results: The overall prevalence of residents prescribed one or more prophylactic antimicrobial on the survey day was 3.7% (n = 4643, 95% confidence interval 3.6-3.8). Of all prescribed antimicrobials (n = 15,831), 27.1% (n = 4871) were for prophylactic use. Of these prophylactic antimicrobials, 87.8% were anti-bacterials and 11.4% antifungals; most frequently, cefalexin (28.7%), methenamine hippurate (20.1%) and clotrimazole (8.8%). When compared with prescribing of all antimicrobial agents, prophylactic antimicrobials were less commonly prescribed for pro re nata administration (7.0% vs 20.3%) and more commonly prescribed greater than 6 months (52.9% vs 34.1%). The indication and review or stop date was less frequently documented (67.5% vs 73.8% and 20.9% vs 40.7%, respectively). The most common body system for which a prophylactic antimicrobial was prescribed was the urinary tract (54.3%). Of all urinary tract indications (n = 2575), about two thirds (n = 1681, 65.3%) were for cystitis and 10.6% were for asymptomatic bacteriuria., Conclusions: Our results clearly identified immediate antimicrobial stewardship strategies that aim to improve prophylactic antimicrobial prescribing in Australian residential-aged care facilities are required., (© 2022. The Author(s).)

As a leading behavioral risk factor for numerous health outcomes, smoking is a major ongoing public health challenge. Although evidence on the health effects of smoking has been widely reported, few attempts have evaluated the dose-response relationship between smoking and a diverse range of health outcomes systematically and comprehensively. In the present study, we re-estimated the dose-response relationships between current smoking and 36 health outcomes by conducting systematic reviews up to 31 May 2022, employing a meta-analytic method that incorporates between-study heterogeneity into estimates of uncertainty. Among the 36 selected outcomes, 8 had strong-to-very-strong evidence of an association with smoking, 21 had weak-to-moderate evidence of association and 7 had no evidence of association. By overcoming many of the limitations of traditional meta-analyses, our approach provides comprehensive, up-to-date and easy-to-use estimates of the evidence on the health effects of smoking. These estimates provide important information for tobacco control advocates, policy makers, researchers, physicians, smokers and the public., (© 2022. The Author(s).)

Low levels of high density lipoprotein-cholesterol (HDL-C) are associated with an elevated risk of arteriosclerotic coronary heart disease. Heritability of HDL-C levels is high. In this research discovery study, we used whole-exome sequencing to identify damaging gene variants that may play significant roles in determining HDL-C levels. We studied 204 individuals with a mean HDL-C level of 27.8 ± 6.4 mg/dl (range: 4-36 mg/dl). Data were analyzed by statistical gene burden testing and by filtering against candidate gene lists. We found 120 occurrences of probably damaging variants (116 heterozygous; four homozygous) among 45 of 104 recognized HDL candidate genes. Those with the highest prevalence of damaging variants were ABCA1 (n = 20), STAB1 (n = 9), OSBPL1A (n = 8), CPS1 (n = 8), CD36 (n = 7), LRP1 (n = 6), ABCA8 (n = 6), GOT2 (n = 5), AMPD3 (n = 5), WWOX (n = 4), and IRS1 (n = 4). Binomial analysis for damaging missense or loss-of-function variants identified the ABCA1 and LDLR genes at genome-wide significance. In conclusion, whole-exome sequencing of individuals with low HDL-C showed the burden of damaging rare variants in the ABCA1 and LDLR genes is particularly high and revealed numerous occurrences in HDL candidate genes, including many genes identified in genome-wide association study reports. Many of these genes are involved in cancer biology, which accords with epidemiologic findings of the association of HDL deficiency with increased risk of cancer, thus presenting a new area of interest in HDL genomics., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Aim: The aim of this study was to evaluate clinical practice about peripheral intravenous catheter (PIVC) insertion, maintenance and removal in a cohort of Victorian hospitals., Design: A standardized PIVC audit tool was developed, and results from point prevalent surveys were conducted., Methods: Hospitalized patients requiring a PIVC insertion were eligible for audit. Audit data submitted between 2015 and 2019 were extracted for the current study., Results: 3566 PIVC insertions in 15 Victorian public hospitals were evaluated. 57.6% of PIVCs were inserted in wards, 18.7% in operating theatres and 11.6% in Emergency Departments (ED). 45.2% were inserted by nurses and 38.2% by medical staff. The preferred site for insertion was the dorsum of the hand and forearm (58.8%). 22.6% did not report a visual infusion phlebitis score at least daily, and 48% did not document a daily dressing assessment. Reasons for PIVC removal included no longer required (63%) and phlebitis (4.8%). No bloodstream infections were reported., (© 2022 The Authors. Nursing Open published by John Wiley & Sons Ltd.)

Smoking is a major cause of cardiac and pulmonary disease, cancer, and other inflammation related diseases. Smoking impairs lipid and lipoprotein metabolism. The observed modification and reduction in levels of HDL in smokers has adverse effects on atheroprotective properties. It has been hypothesized that HDL transports inflammatory cytokines which accelerate tobacco-related diseases. To investigate the role of HDL in the transport of inflammatory cytokines and their detrimental effects on the immune response, it is paramount to compare cytokine levels in HDL for Smoker versus Nonsmoker groups. We isolated HDL from plasma using selected affinity immunosorption of apolipoprotein A-I-bearing lipoproteins, followed by quantitative ELISA of cytokines. We implemented a powerful stacked ensemble Machine Learning algorithm, namely Super Learner (SL) with base-learners: Decision Tree classifier, AdaBoost classifier, Bagging classifier, Extra Tree classifier, Logistic Regression and Random Forest classifier and meta learner: Logistic Regression. Prediction Accuracy metric was used to ascertain the separability efficacy of Smoker versus Nonsmoker based on cytokine levels. Super Learner composed of a Logistic Regression meta learner, achieved a 100% prediction accuracy, outperforming all the base learners. Machine learning-enabled Precision Medicine allows the investigation of the role of novel biomarkers such as HDL-transported cytokines which have a potential to generate valuable molecular insights. The discovery that cytokines are transported by HDL presents a new dimension in understanding inflammatory disorders and the potential for therapeutic intervention. The outstanding classification and prediction performance of Ensemble learning can be leveraged to revolutionize the biomarker discoveries, enabling insight that can lead to novel treatment modalities., Competing Interests: Competing interests The authors report no conflicts of interest.

Presently, the role of cytokines in severe illness like COPD, cancer, cardiac disease associated with smoking is being explored to enable preemptive diagnosis and delivery of treatment interventions. We are investigating the connection between the elevation of inflammatory plasma cytokine in smokers versus nonsmokers. Disease indicator cytokines can be used to monitor the progression of disease which can help in the crucial task of prognosis and definitive diagnosis. Powerful and versatile Machine Learning algorithms can be leveraged to extract insights that cannot be obtained manually. We have applied Support Vector Machine (SVM) on 65 plasma cytokines and other traditional biomarkers to differentiate smokers and nonsmokers. To optimize the classification separability, we have used the following techniques: Principal component analysis (PCA), 10-fold cross validation and variable importance. The primary metric of evaluation is Area Under Receiver Operating Curve (AUROC), though we have additionally recorded and compared prediction accuracy across classifiers. The results are very promising. The AUROC classification accuracy achieved by SVM using the selected predictor feature variables is 89.2% with a 95%CI (85.4%,93.1%). The most prominent cytokines, contributing to the classification, in the order of importance are: I-TAC, Age, TG, G-CSF-CSF-3, MDC-CCL22, Eotaxin-3, LIF, IL-2, Eotaxin-2, MIP-3alpha. The AUROC classification accuracy improved to 93% with a 95% CI (90.1%,99.5%) upon choosing the five most prominent cytokines. The versatile prowess of Machine Learning algorithms such as Support Vector Machine can translate pioneering molecular discoveries into actionable insights that can be applied in the field of translational and precision medicine to save life., Competing Interests: Competing interests The authors report no conflicts of interest.

Diabetes mellitus is a complex disease. We are increasingly gaining a better understanding of its mechanisms at the molecular level. From these new insights, better therapeutic approaches should emerge. Diabetes mellitus is a syndrome with many associated subphenotypes. These include mitochondrial disorders, lipodystrophies, and inflammatory disorders involving cytokines. Levels of sphingosine-1-phosphate, which has recently been shown to play a role in glucose homeostasis, are low in diabetics, whereas levels of ceramides are increased. Major phenotypes associated with diabetes mellitus are dyslipidemias, notably hypertriglyceridemia and low high-density lipoprotein cholesterol levels. Both diabetes and dyslipidemia are strongly associated with increased risk for atherosclerotic vascular disease., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Australian residential aged care facilities (RACFs) are encouraged to participate in an annual Aged Care National Antimicrobial Prescribing Survey. This data source was analysed to describe patterns of topical antimicrobial prescribing and thereby provide insight into antimicrobial stewardship (AMS) changes that might be required., Methods: 2018 and 2019 survey data was analysed., Results: The overall prevalence of the 52,431 audited residents (629 facilities) who were prescribed 1 or more topical antimicrobials was 2.9%. Of all prescribed antimicrobials (n=4899), 33.0% were for topical application. Most frequently prescribed topical antifungals were clotrimazole (85.3%) and miconazole (9.1%), and antibacterials chloramphenicol (64.1%) and mupirocin (21.8%). Tinea (38.3%) and conjunctivitis (23.8%) were the 2 most common indications. Topical antimicrobials were sometimes prescribed for pro re nata administration (38.8%) and greater than 6 months (11.3%). The review or stop date was not always documented (38.7%)., Conclusions: To reduce the possibility of adverse consequences associated with antimicrobial use, antimicrobial stewardship programs in Australian residential aged care facilities should at least ensure mupirocin is appropriately used, first line antimicrobial therapy is prescribed for tinea, chloramphenicol is prescribed for conjunctivitis only if necessary, pro re nata orders for prescriptions are discouraged and to avoid prolonged duration of prescriptions, review or stop dates are always documented., (Crown Copyright © 2021. Published by Elsevier Inc. All rights reserved.)

Background: As per the 2017 WHO fact sheet, Coronary Artery Disease (CAD) is the primary cause of death in the world, and accounts for 31% of total fatalities. The unprecedented 17.6 million deaths caused by CAD in 2016 underscores the urgent need to facilitate proactive and accelerated pre-emptive diagnosis. The innovative and emerging Machine Learning (ML) techniques can be leveraged to facilitate early detection of CAD which is a crucial factor in saving lives. The standard techniques like angiography, that provide reliable evidence are invasive and typically expensive and risky. In contrast, ML model generated diagnosis is non-invasive, fast, accurate and affordable. Therefore, ML algorithms can be used as a supplement or precursor to the conventional methods. This research demonstrates the implementation and comparative analysis of K Nearest Neighbor (k-NN) and Random Forest ML algorithms to achieve a targeted "At Risk" CAD classification using an emerging set of 35 cytokine biomarkers that are strongly indicative predictive variables that can be potential targets for therapy. To ensure better generalizability, mechanisms such as data balancing, repeated k-fold cross validation for hyperparameter tuning, were integrated within the models. To determine the separability efficacy of "At Risk" CAD versus Control achieved by the models, Area under Receiver Operating Characteristic (AUROC) metric is used which discriminates the classes by exhibiting tradeoff between the false positive and true positive rates., Results: A total of 2 classifiers were developed, both built using 35 cytokine predictive features. The best AUROC score of .99 with a 95% Confidence Interval (CI) (.982,.999) was achieved by the Random Forest classifier using 35 cytokine biomarkers. The second-best AUROC score of .954 with a 95% Confidence Interval (.929,.979) was achieved by the k-NN model using 35 cytokines. A p-value of less than 7.481e-10 obtained by an independent t-test validated that Random Forest classifier was significantly better than the k-NN classifier with regards to the AUROC score. Presently, as large-scale efforts are gaining momentum to enable early, fast, reliable, affordable, and accessible detection of individuals at risk for CAD, the application of powerful ML algorithms can be leveraged as a supplement to conventional methods such as angiography. Early detection can be further improved by incorporating 65 novel and sensitive cytokine biomarkers. Investigation of the emerging role of cytokines in CAD can materially enhance the detection of risk and the discovery of mechanisms of disease that can lead to new therapeutic modalities.

Background We previously showed that levels of prebeta-1 high-density lipoprotein (HDL), the principal acceptor of cholesterol effluxed from cells, including artery wall macrophages, are positively associated with coronary heart disease (CHD) and myocardial infarction (MI) risk. Methods and Results In a multiethnic follow-up cohort of 1249 individuals from University of California-San Francisco clinics, we determined the degree to which prebeta-1 HDL levels, both absolute and percentage of apolipoprotein AI, are associated with CHD and history of MI. Independent, strong, positive associations were found. Meta-analysis revealed for the absolute prebeta-1 HDL for the top tertile versus the lowest, unadjusted odds ratios of 1.90 (95% CI, 1.40-2.58) for CHD and 1.79 (95% CI, 1.35-2.36) for MI. For CHD, adjusting for established risk factors, the top versus bottom tertiles, quintiles, and deciles yielded sizable odds ratios of 2.37 (95% CI, 1.74-3.25, P <0.001), 3.20 (95% CI, 2.07-4.94, P <0.001), and 4.00 (95% CI, 2.11-7.58, P <0.001), respectively. Men and women were analyzed separately in a combined data set of 2507 individuals. The odds ratios for CHD and MI risk were similar. Higher levels of prebeta-1 HDL were associated with all 5 metabolic syndrome features. Addition of prebeta-1 HDL to these 5 features resulted in significant improvements in risk-prediction models. Conclusions Analysis of 2507 subjects showed conclusively that levels of prebeta-1 HDL are strongly associated with a history of CHD or MI, independently of traditional risk factors. Addition of prebeta-1 HDL can significantly improve clinical assessment of risk of CHD and MI.

Supplemental Digital Content is available in the text., Competing Interests: Dr Hegele reports personal fees from Acasti, personal fees from Akcea—Ionis, personal fees from Amgen, personal fees from HLS Therapeutics, personal fees from Novartis, personal fees from Pfizer, personal fees from Regeneron, personal fees from Sanofi, outside the submitted work. Dr Rader reports being on Scientific Advisory Boards for Alnylam Pharmaceuticals, Novartis, Pfizer, and Verve; receiving research funding and non-financial support from Regeneron Pharmaceuticals; being Co-founder of VascularStrategies and Staten Biotechnology; and serving as an uncompensated Chief Scientific Advisor for the Familial Hypercholesterolemia Foundation. The other authors report no conflicts of interest., (Copyright © 2021 The Author(s). Published by Wolters Kluwer on behalf of European Society for Pediatric Gastroenterology, Hepatology, and Nutrition and North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition.)

Background: Susceptibility to severe hypertriglyceridemia (HTG), defined as plasma triglyceride (TG) levels ≥10 mmol/L (880 mg/dL), is conferred by both heterozygous rare variants in five genes involved in TG metabolism and numerous common single-nucleotide polymorphisms (SNPs) associated with TG levels., Objective: To date, these genetic susceptibility factors have been comprehensively assessed primarily in severe HTG patients of European ancestry. Here, we expand our analysis to HTG patients of East Asian and Hispanic ancestry., Methods: The genomic DNA of 336, 63 and 199 severe HTG patients of European, East Asian and Hispanic ancestry, respectively, was evaluated using a targeted next-generation sequencing panel to screen for: 1) rare variants in LPL, APOA5, APOC2, GPIHBP1 and LMF1; 2) common, small-to-moderate effect SNPs, quantified using a polygenic score; and 3) common, large-effect polymorphisms, APOA5 p.G185C and p.S19W., Results: While the proportion of individuals with high polygenic scores was similar, frequency of rare variant carriers varied across ancestries. Compared with ancestry-matched controls, Hispanic patients were the most likely to have a rare variant (OR = 5.02; 95% CI 3.07-8.21; p < 0.001), while European patients were the least likely (OR = 2.56; 95% CI 1.58-4.13; p < 0.001). The APOA5 p.G185C polymorphism, exclusive to East Asians, was significantly enriched in patients compared with controls (OR = 10.1; 95% CI 5.6-18.3; p < 0.001), showing the highest enrichment among the measured genetic factors., Conclusion: While TG-associated rare variants and common SNPs are both found in statistical excess in severe HTG patients of different ancestral backgrounds, the overall genetic profiles of each ancestry group were distinct., (Copyright © 2020 National Lipid Association. Published by Elsevier Inc. All rights reserved.)

Objective: To determine the proportion of healthcare workers (HCWs) in smaller Victorian public healthcare facilities with documented evidence of measles immunity., Methods: A cross-sectional survey, developed by the Victorian Healthcare Associated Surveillance System Coordinating Centre, was completed by all eligible facilities. HCWs were reported as having evidence or no evidence of measles immunity. Those without evidence of immunity were sub-classified as incomplete, declined or unknown status., Results: Seventy-five facilities reported measles immunity status of 17,522 employed HCWs. Of these, 11,751 (67.1%) had documented evidence of immunity. The proportion with evidence of immunity was lowest (45.6%) in facilities with ≤50 HCWs. The majority of HCWs without evidence of immunity (88.2%) had 'unknown' status. Declination or incomplete status comprised very low overall proportions (0.3% and 3.6%, respectively)., Conclusions: Reported evidence of HCW measles immunity was moderate in surveyed facilities, with a large proportion having unknown status. HCW immunisation programs in some facilities require refinement to appropriately support public health responses to measles cases and prevention of occupational acquisition of measles. Implications for public health: Non-immune HCWs are at increased risk for acquiring and transmitting measles. Timely access to accurate HCW immunisation records is required to ensure that public health responses are effective., (© 2020 The Authors.)

Introduction: While annual influenza vaccination of healthcare workers (HCWs) is recommended, uptake is often suboptimal. We sought to evaluate influenza vaccination uptake by HCWs in Victorian public healthcare facilities, where non-mandatory programs are used., Methods: All participating facilities completed an annual survey (2014-2019) recording HCW influenza vaccination status. Uptake in high-risk departments (emergency and intensive care units) was evaluated for the 2019 season., Results: The proportion of vaccinated HCWs increased annually, from 72.2% (2014) to 87.7% (2019), with pre-set targets generally achieved. In 2019, 110,324 HCWs in 107 facilities were vaccinated (87.7%). Of those without documented vaccination, 7591 (6.0%) declined and 7906 (6.3%) had unknown status. Uptake was higher in high-risk departments (91.4%)., Conclusion: Increasing annual influenza vaccination uptake by HCWs in Victorian public healthcare facilities has been achieved in the context of performance monitoring targets. Small proportions declined or had unknown status. Future policies should focus on these HCWs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

To make accurate determinations regarding potential and actual impact of HPV vaccine programs, precise estimates of genotype-specific contributions to disease are required for pre- and post-vaccine populations. Definitive determination of lesion-specific genotypes, particularly where multiple genotypes are detected in a sample, can be technically demanding and resource intensive; therefore, most prevalence studies use mathematical algorithms to adjust for multiple genotype detections. There are currently several algorithms, which can produce genotype estimates within a wide range of variability. The use of these for cervical cytology samples has recently been assessed for accuracy against a definitive reference standard, but none have yet been assessed for multiple-genotype-containing whole biopsy specimens. Using laser capture microdissection (LCM) on biopsy samples, lesion-specific genotype prevalence data were generated for a cohort of 516 young Australian women (aged 18-32 years) with cervical intraepithelial neoplasia grade 3 or adenocarcinoma in situ. Using whole tissue section genotype data from the same cohort, including 71 (13.7%) with multiple genotypes, lesion-associated genotype prevalence was estimated using four different attribution algorithms. The proportion of lesions attributable to HPV16 and HPV18 by LCM were 58.4% and 5%, respectively; hierarchical, proportional, single type/minimum and any type/maximum attribution estimates were comparable across genotypes. For analyses utilising whole tissue biopsy cervical specimens, attribution estimates are appropriate for estimating the proportional contribution of individual genotypes to lesions in a population., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2020. Published by Elsevier Ltd. All rights reserved.)