Stavroula A. Chrysanthopoulou

Effect of Aminoglycoside and β-Lactam Combination Therapy versus β-Lactam Monotherapy on the Emergence of Antimicrobial Resistance: A Meta-analysis of Randomized, Controlled Trials

BACKGROUND The addition of an aminoglycoside to a beta -lactam therapy regimen has been suggested to have a beneficial effect in delaying or preventing the development of antimicrobial resistance. We studied the effect of aminoglycoside/ beta -lactam combination therapy versus beta-lactam monotherapy on the emergence of resistance. METHODS We performed a meta-analysis of randomized, controlled trials (RCTs) that compared aminoglycoside/ beta-lactam combination therapy with beta-lactam monotherapy and that reported data regarding the emergence of resistance (primary outcome) and/or development of superinfection, treatment failure, treatment failure attributable to emergence of resistance, treatment failure attributable to superinfection, all-cause mortality during treatment, and mortality due to infection. Data for this meta-analysis were identified from the PubMed database, Current Contents database, Cochrane central register of controlled trials, and references in relevant articles. RESULTS A total of 8 RCTs were included in the analysis. Beta -lactam monotherapy was not associated with a greater emergence of resistance than was the aminoglycoside/ beta-lactam combination (odds ratio [OR], 0.90; 95% confidence interval [CI], 0.56-1.47). Actually, beta -lactam monotherapy was associated with fewer superinfections (OR, 0.62; 95% CI, 0.42-0.93) and fewer treatment failures (OR, 0.62; 95% CI, 0.38-1.01). Rates of treatment failure attributable to emergence of resistance (OR, 3.09; 95% CI, 0.75-12.82), treatment failure attributable to superinfection (OR, 0.60; 95% CI, 0.33-1.10), all-cause mortality during treatment (OR, 0.70; 95% CI, 0.40-1.25), and mortality due to infection (OR, 0.74; 95% CI, 0.46-1.21) did not differ significantly between the 2 regimens. CONCLUSIONS Compared with beta-lactam monotherapy, the aminoglycoside/ beta-lactam combination was not associated with a beneficial effect on the development of antimicrobial resistance among initially antimicrobial-susceptible isolates.

Role of glycopeptides as part of initial empirical treatment of febrile neutropenic patients: a meta-analysis of randomised controlled trials

We did a meta-analysis of randomised controlled trials studying glycopeptides as part of the initial empirical treatment of febrile neutropenic patients with a beta-lactam and with or without an aminoglycoside. 14 randomised controlled trials that studied 2413 patients were included in the analysis. A better outcome regarding treatment success, without modification of the initial regimen, was accomplished with the inclusion of a glycopeptide in the empirical therapy; this better outcome applied to the full set of studied patients (OR=1.63, 95% CI 1.17-2.28), as well as in three important subsets of patients--those with microbiologically documented infections (2.03, 1.39-2.97), patients with bacteraemia (1.80, 1.23-2.63), and patients with severe neutropenia, defined as a white blood cell count below 100 cells/microL (2.24, 1.15-4.39). However, mortality was not different in the compared groups (0.67, 0.42-1.05). Overall treatment success was not different if a glycopeptide was added to the antimicrobial regimen in the case of continuation of fever 72 hours or more after the start of treatment (1.02, 0.68-1.52). Also, the inclusion of a glycopeptide in the empirical regimen did not lead to a difference regarding time to defervesence. Adverse effects (4.98, 2.91-8.55), including nephrotoxicity (2.10, 1.12-3.95), were more common in the group receiving a glycopeptide as part of the empirical treatment. In conclusion, our meta-analysis suggests that there are good reasons why glycopeptides should not be routinely used as part of the initial empirical treatment of febrile neutropenic patients.

Implementation and reporting of causal mediation analysis in 2015: a systematic review in epidemiological studies.

BackgroundCausal mediation analysis is often used to understand the impact of variables along the causal pathway of an occurrence relation. How well studies apply and report the elements of causal mediation analysis remains unknown.MethodsWe systematically reviewed epidemiological studies published in 2015 that employed causal mediation analysis to estimate direct and indirect effects of observed associations between an exposure on an outcome. We identified potential epidemiological studies through conducting a citation search within Web of Science and a keyword search within PubMed. Two reviewers independently screened studies for eligibility. For eligible studies, one reviewer performed data extraction, and a senior epidemiologist confirmed the extracted information. Empirical application and methodological details of the technique were extracted and summarized.ResultsThirteen studies were eligible for data extraction. While the majority of studies reported and identified the effects of measures, most studies lacked sufficient details on the extent to which identifiability assumptions were satisfied. Although most studies addressed issues of unmeasured confounders either from empirical approaches or sensitivity analyses, the majority did not examine the potential bias arising from the measurement error of the mediator. Some studies allowed for exposure-mediator interaction and only a few presented results from models both with and without interactions. Power calculations were scarce.ConclusionsReporting of causal mediation analysis is varied and suboptimal. Given that the application of causal mediation analysis will likely continue to increase, developing standards of reporting of causal mediation analysis in epidemiological research would be prudent.

Probabilistic bias analysis in pharmacoepidemiology and comparative effectiveness research: a systematic review.

We systematically reviewed pharmacoepidemiologic and comparative effectiveness studies that use probabilistic bias analysis to quantify the effects of systematic error including confounding, misclassification, and selection bias on study results.

Prevalence of Long-Term Opioid Use in Long-Stay Nursing Home Residents

Overall and long‐term opioid use among older adults have increased since 1999. Less is known about opioid use in older adults in nursing homes (NHs).

The impact of nondifferential exposure misclassification on the performance of propensity scores for continuous and binary outcomes: a simulation study

Purpose: To investigate the ability of the propensity score (PS) to reduce confounding bias in the presence of nondifferential misclassification of treatment, using simulations. Methods: Using an example from the pregnancy medication safety literature, we carried out simulations to quantify the effect of nondifferential misclassification of treatment under varying scenarios of sensitivity and specificity, exposure prevalence (10%, 50%), outcome type (continuous and binary), true outcome (null and increased risk), confounding direction, and different PS applications (matching, stratification, weighting, regression), and obtained measures of bias and 95% confidence interval coverage. Results: All methods were subject to substantial bias toward the null due to nondifferential exposure misclassification (range: 0%–47% for 50% exposure prevalence and 0%–80% for 10% exposure prevalence), particularly if specificity was low (<97%). PS stratification produced the least biased effect estimates. We observed that the impact of sensitivity and specificity on the bias and coverage for each adjustment method is strongly related to prevalence of exposure: as exposure prevalence decreases and/or outcomes are continuous rather than categorical, the effect of misclassification is magnified, producing larger biases and loss of coverage of 95% confidence intervals. PS matching resulted in unpredictably biased effect estimates. Conclusions: The results of this study underline the importance of assessing exposure misclassification in observational studies in the context of PS methods. Although PS methods reduce confounding bias, bias owing to nondifferential misclassification is of potentially greater concern.

Survivors of an Acute Coronary Syndrome With Lower Patient Activation Are More Likely to Experience Declines in Health-Related Quality of Life

Background:Patient activation comprises the knowledge, skills, and confidence for self-care and may lead to better health outcomes. Objectives:We examined the relationship between patient activation and changes in health-related quality of life (HRQOL) after hospitalization for an acute coronary syndrome (ACS). Methods:We studied patients from 6 medical centers in central Massachusetts and Georgia who had been hospitalized for an ACS between 2011 and 2013. At 1 month after hospital discharge, the patients completed the 6-item Patient Activation Measure and were categorized into 4 levels of activation. Multinomial logistic regression analyses compared activation level with clinically meaningful changes (≥3.0 points, generic; ≥10.0 points, disease-specific) in generic physical (SF-36v2 Physical Component Summary [PCS]), generic mental (SF-36v2 Mental Component Summary [MCS]), and disease-specific (Seattle Angina Questionnaire [SAQ]) HRQOL from 1 to 3 and 1 to 6 months after hospitalization, adjusting for potential sociodemographic and clinical confounders. Results:The patients (N = 1042) were, on average, 62 years old, 34% female, and 87% non-Hispanic white. A total of 10% were in the lowest level of activation. The patients with the lowest activation had 1.95 times (95% confidence interval, 1.05–3.62) and 2.18 times (95% confidence interval, 1.17–4.05) the odds of experiencing clinically significant declines in MCS and SAQ HRQOL, respectively, between 1 and 6 months than the most activated patients. The patient activation level was not associated with meaningful changes in PCS scores. Conclusions:Hospital survivors of an ACS with lower activation may be more likely to experience declines in mental and disease-specific HRQOL than more-activated patients, identifying a group at risk of poor outcomes.

The use of latent class analysis for identifying subtypes of depression: A systematic review

Depression is a significant public health problem but symptom remission is difficult to predict. This may be due to substantial heterogeneity underlying the disorder. Latent class analysis (LCA) is often used to elucidate clinically relevant depression subtypes but whether or not consistent subtypes emerge is unclear. We sought to critically examine the implementation and reporting of LCA in this context by performing a systematic review to identify articles detailing the use of LCA to explore subtypes of depression among samples of adults endorsing depression symptoms. PubMed, PsycINFO, CINAHL, Scopus, and Google Scholar were searched to identify eligible articles indexed prior to January 2016. Twenty-four articles reporting 28 LCA models were eligible for inclusion. Sample characteristics varied widely. The majority of articles used depression symptoms as the observed indicators of the latent depression subtypes. Details regarding model fit and selection were often lacking. No consistent set of depression subtypes was identified across studies. Differences in how models were constructed might partially explain the conflicting results. Standards for using, interpreting, and reporting LCA models could improve our understanding of the LCA results. Incorporating dimensions of depression other than symptoms, such as functioning, may be helpful in determining depression subtypes.