Eleanor Pullenayegum

Higher vs lower positive end-expiratory pressure in patients with acute lung injury and acute respiratory distress syndrome: systematic review and meta-analysis.

CONTEXT Trials comparing higher vs lower levels of positive end-expiratory pressure (PEEP) in adults with acute lung injury or acute respiratory distress syndrome (ARDS) have been underpowered to detect small but potentially important effects on mortality or to explore subgroup differences. OBJECTIVES To evaluate the association of higher vs lower PEEP with patient-important outcomes in adults with acute lung injury or ARDS who are receiving ventilation with low tidal volumes and to investigate whether these associations differ across prespecified subgroups. DATA SOURCES Search of MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials (1996-January 2010) plus a hand search of conference proceedings (2004-January 2010). STUDY SELECTION Two reviewers independently screened articles to identify studies randomly assigning adults with acute lung injury or ARDS to treatment with higher vs lower PEEP (with low tidal volume ventilation) and also reporting mortality. DATA EXTRACTION Data from 2299 individual patients in 3 trials were analyzed using uniform outcome definitions. Prespecified effect modifiers were tested using multivariable hierarchical regression, adjusting for important prognostic factors and clustering effects. RESULTS There were 374 hospital deaths in 1136 patients (32.9%) assigned to treatment with higher PEEP and 409 hospital deaths in 1163 patients (35.2%) assigned to lower PEEP (adjusted relative risk [RR], 0.94; 95% confidence interval [CI], 0.86-1.04; P = .25). Treatment effects varied with the presence or absence of ARDS, defined by a value of 200 mm Hg or less for the ratio of partial pressure of oxygen to fraction of inspired oxygen concentration (P = .02 for interaction). In patients with ARDS (n = 1892), there were 324 hospital deaths (34.1%) in the higher PEEP group and 368 (39.1%) in the lower PEEP group (adjusted RR, 0.90; 95% CI, 0.81-1.00; P = .049); in patients without ARDS (n = 404), there were 50 hospital deaths (27.2%) in the higher PEEP group and 44 (19.4%) in the lower PEEP group (adjusted RR, 1.37; 95% CI, 0.98-1.92; P = .07). Rates of pneumothorax and vasopressor use were similar. CONCLUSIONS Treatment with higher vs lower levels of PEEP was not associated with improved hospital survival. However, higher levels were associated with improved survival among the subgroup of patients with ARDS.

The effect of oral antidiabetic agents on A1C levels: a systematic review and meta-analysis.

OBJECTIVE Previous reviews of the effect of oral antidiabetic (OAD) agents on A1C levels summarized studies with varying designs and methodological approaches. Using predetermined methodological criteria, we evaluated the effect of OAD agents on A1C levels. RESEARCH DESIGN AND METHODS The Excerpta Medica (EMBASE), the Medical Literature Analysis and Retrieval System Online (MEDLINE), and the Cochrane Central Register of Controlled Trials databases were searched from 1980 through May 2008. Reference lists from systematic reviews, meta-analyses, and clinical practice guidelines were also reviewed. Two evaluators independently selected and reviewed eligible studies. RESULTS A total of 61 trials reporting 103 comparisons met the selection criteria, which included 26,367 study participants, 15,760 randomized to an intervention drug(s), and 10,607 randomized to placebo. Most OAD agents lowered A1C levels by 0.5−1.25%, whereas thiazolidinediones and sulfonylureas lowered A1C levels by ∼1.0–1.25%. By meta-regression, a 1% higher baseline A1C level predicted a 0.5 (95% CI 0.1–0.9) greater reduction in A1C levels after 6 months of OAD agent therapy. No clear effect of diabetes duration on the change in A1C with therapy was noted. CONCLUSIONS The benefit of initiating an OAD agent is most apparent within the first 4 to 6 months, with A1C levels unlikely to fall more than 1.5% on average. Pretreated A1C levels have a modest effect on the fall of A1C levels in response to treatment.

The Effect of Oral Antidiabetic Agents on Glycated Hemoglobin Levels: A Systematic Review and Meta-Analysis

OBJECTIVE Previous reviews of the effect of oral antidiabetic (OAD) agents on A1C levels summarized studies with varying designs and methodological approaches. Using predetermined methodological criteria, we evaluated the effect of OAD agents on A1C levels. RESEARCH DESIGN AND METHODS The Excerpta Medica (EMBASE), the Medical Literature Analysis and Retrieval System Online (MEDLINE), and the Cochrane Central Register of Controlled Trials databases were searched from 1980 through May 2008. Reference lists from systematic reviews, meta-analyses, and clinical practice guidelines were also reviewed. Two evaluators independently selected and reviewed eligible studies. RESULTS A total of 61 trials reporting 103 comparisons met the selection criteria, which included 26,367 study participants, 15,760 randomized to an intervention drug(s), and 10,607 randomized to placebo. Most OAD agents lowered A1C levels by 0.5−1.25%, whereas thiazolidinediones and sulfonylureas lowered A1C levels by ∼1.0–1.25%. By meta-regression, a 1% higher baseline A1C level predicted a 0.5 (95% CI 0.1–0.9) greater reduction in A1C levels after 6 months of OAD agent therapy. No clear effect of diabetes duration on the change in A1C with therapy was noted. CONCLUSIONS The benefit of initiating an OAD agent is most apparent within the first 4 to 6 months, with A1C levels unlikely to fall more than 1.5% on average. Pretreated A1C levels have a modest effect on the fall of A1C levels in response to treatment.

Potentiating Cancer Immunotherapy Using an Oncolytic Virus

Oncolytic viruses (OVs) are highly immunogenic and this limits their use in immune-competent hosts. Although immunosuppression may improve viral oncolysis, this gain is likely achieved at the cost of antitumoral immunity. We have developed a strategy wherein the immune response against the OV leads to enhanced therapeutic outcomes. We demonstrate that immunization with an adenoviral (Ad) vaccine before treatment with an oncolytic vesicular stomatitis virus (VSV) expressing the same tumor antigen (Ag) leads to significantly enhanced antitumoral immunity. Intratumoral replication of VSV was minimally attenuated in Ad-immunized hosts but extending the interval between treatments reduced the attenuating effect and further increased antitumoral immunity. More importantly, our combination approach shifted the immune response from viral Ags to tumor Ags and further reduced OV replication in normal tissues, leading to enhancements in both efficacy and safety. These studies also highlight the benefits of using a replicating, OV to boost a pre-existing antitumoral immune response as this approach generated larger responses versus tumor Ag in tumor-bearing hosts than could be achieved in tumor-free hosts. This strategy should be applicable to other vector combinations, tumor Ags, and tumor targets.

Analysis of Health Utility Data When Some Subjects Attain the Upper Bound of 1: Are Tobit and CLAD Models Appropriate?

BACKGROUND Health utility data often show an apparent truncation effect, where a proportion of individuals achieve the upper bound of 1. The Tobit model and censored least absolute deviations (CLAD) have both been used as analytic solutions to this apparent truncation effect. These models assume that the observed utilities are censored at 1, and hence that the true utility can be greater than 1.We aimed to examine whether the Tobit and CLAD models yielded acceptable results when this censoring assumption was not appropriate. METHODS Using health utility (captured through EQ5D) data from a diabetes study, we conducted a simulation to compare the performance of the Tobit, CLAD, ordinary least squares (OLS), two-part and latent class estimators in terms of their bias and estimated confidence intervals. We also illustrate the performance of semiparametric and nonparametric bootstrap methods. RESULTS When the true utility was conceptually bounded above at 1, the Tobit and CLAD estimators were both biased. The OLS estimator was asymptotically unbiased and, while the model-based and semiparametric bootstrap confidence intervals were too narrow, confidence intervals based on the robust standard errors or the nonparametric bootstrap were acceptable for sample sizes of 100 and larger. Two-part and latent class models also yielded unbiased estimates. CONCLUSIONS When the intention of the analysis is to inform an economic evaluation, and the utilities should be bounded above at 1, CLAD, and Tobit methods were biased. OLS coupled with robust standard errors or the nonparametric bootstrap is recommended as a simple and valid approach.

Despite 2009 guidelines, few women report being counseled correctly about weight gain during pregnancy

OBJECTIVE The purpose of this study was to determine the information that pregnant women report receiving when being counseled about weight gain and the risks of inappropriate gain. STUDY DESIGN With the use of a self-administered questionnaire at prenatal clinics in Hamilton, Ontario, Canada, a cross-sectional survey was conducted of women who had had at least 1 prenatal visit, who could read English, and who had a live singleton gestation. RESULTS Three hundred ten women completed the survey, which was a 93.6% response rate. Although 28.5% (95% confidence interval, 23.5-33.6%) reported that their health care provider had made a recommendation about how much weight they should gain, only 12.0% (95% confidence interval, 8-16.1%) of the women reported having achieved the recommended weight gain in accordance with the 2009 guidelines. One quarter of the women reported being told that there were risks with inappropriate gain. CONCLUSION Despite the recent 2009 publication of the gestational weight gain guidelines, only 12% of women reported being counseled correctly, which suggests an urgent need for improved patient education.

The relative efficacy of nine osteoporosis medications for reducing the rate of fractures in post-menopausal women

BackgroundIn the absence of head-to-head trials, indirect comparisons of randomized placebo-controlled trials may provide a viable option to assess relative efficacy. The purpose was to estimate the relative efficacy of reduction of fractures in post-menopausal women, and to assess robustness of the results.MethodsA systematic literature review of multiple databases identified randomized placebo-controlled trials with nine drugs for post-menopausal women. Odds ratio and 95% credibility intervals for the rates of hip, non-vertebral, vertebral, and wrist fractures for each drug and between drugs were derived using a Bayesian approach. A drug was ranked as the most efficacious if it had the highest posterior odds ratio, or had the highest effect size.Results30 studies including 59,209 patients reported fracture rates for nine drugs: alendronate (6 studies), denosumab (1 study), etidronate (8 studies), ibandronate (4 studies), raloxifene (1 study), risedronate (7 studies), strontium (2 study), teriparatide (1 study), and zoledronic acid (1 study). The drugs with the highest probability of reducing non-vertebral fractures was etidronate and teriparatide while the drugs with the highest probability of reducing vertebral, hip or wrist fractures were teriparatide, zoledronic acid and denosumab. The drugs with the largest effect size for vertebral fractures were zoledronic acid, teriparatide and denosumab, while the drugs with the highest effect size for non-vertebral, hip or wrist fractures were alendronate or risedronate. Estimates were consistent between Bayesian and classical approaches.ConclusionTeriparatide, zoledronic acid and denosumab have the highest probabilities of being most efficacious for non-vertebral and vertebral fractures, and having the greatest effect sizes. The estimates from indirect comparisons were robust to differences in methodology.

Comparing EQ-5D Valuation Studies: A Systematic Review and Methodological Reporting Checklist

Background. There has been a growing interest around the world in developing country-specific scoring algorithms for the EQ-5D. This study systematically reviews all existing EQ-5D valuation studies to highlight their strengths and limitations, explores heterogeneity in observed utilities using meta-regression, and proposes a methodological checklist for reporting EQ-5D valuation studies. Methods. We searched Medline, EMBASE, the National Health Service Economic Evaluation Database (NHS EED) via Wiley’s Cochrane Library, and Wiley’s Health Economic Evaluation Database from inception through November 2012, as well as bibliographies of key papers and the EuroQol Plenary Meeting Proceedings from 1991 to 2012 for English-language reports of EQ-5D valuation studies. Two reviewers independently screened the titles and abstracts for relevance. Three reviewers performed data extraction and compared the characteristics and scoring algorithms developed in the included valuation studies. Results. Of the 31 studies included in the review, 19 used the time trade-off (TTO) technique, 10 used the visual analogue scale (VAS) technique, and 2 used both TTO and VAS. Most studies included respondents from the general population selected by random or quota sampling and used face-to-face interviews or postal surveys. Studies valued between 7 and 198 total states, with 1–23 states valued per respondent. Different model specifications have been proposed for scoring. Some sample or demographic factors, including gender, education, percentage urban population, and national health care expenditure, were associated with differences in observed utilities for moderate or severe health states. Conclusions. EQ-5D valuation studies conducted to date have varied widely in their design and in the resulting scoring algorithms. Therefore, we propose the Checklist for Reporting Valuation Studies of the EQ-5D (CREATE) for those conducting valuation studies.

Prevalence and predictors of exclusive breastfeeding at hospital discharge.

OBJECTIVE: To estimate the population-based prevalence and predictors of exclusive breastfeeding at hospital discharge in singleton and twin term newborns. METHODS: We studied all hospital births in the province of Ontario, Canada, between April 1, 2009, and March 31, 2010, to perform a retrospective cohort study. We included live singleton and twin births, at term (37 0/7 weeks of gestation to 41 6/7 weeks of gestation), with information about feeding at maternal–newborn discharge. Descriptive statistics were performed and logistic regression was used to identify factors related to exclusive breastfeeding. RESULTS: Our study population consisted of 92,364 newborns, of whom 56,865 (61.6%) were exclusively breastfed at discharge. Older, nonsmoking, higher-income mothers with no pregnancy complications or reproductive assistance were more likely to breastfeed. Mothers of twins were less likely to exclusively breastfeed (adjusted odds ratio [OR] 0.30, 95% confidence interval [CI] 0.25–0.36) as were women who did not attend prenatal classes (adjusted OR 0.80, 95% CI 0.76–0.83). Compared with patients of obstetricians (57%), women cared for by midwives (87%, adjusted OR 4.49, 95% CI 4.16–4.85) and family physicians (67%, adjusted OR 1.54, 95% CI 1.47–1.61) were more likely to exclusively breastfeed. Breastfeeding after a planned (50%, adjusted OR 0.56, 95% CI 0.52–0.60) or unplanned (48%, adjusted OR 0.48, 95% CI 0.44–0.51) cesarean delivery was less common than after a spontaneous vaginal birth (68%). Neonates born at 39, 38, and 37 weeks of gestation (compared with 41 weeks of gestation) were increasingly less likely to breastfeed (adjusted ORs 0.93, 95% CI 0.89–0.98; 0.84, 95% CI 0.80–0.88; and 0.71, 95% CI 0.67–0.76). CONCLUSION: This large population-based study found that fewer than two thirds of term newborns are exclusively breastfed at hospital discharge, substantially lower than previously reported. LEVEL OF EVIDENCE: II

Comparing methods to estimate treatment effects on a continuous outcome in multicentre randomized controlled trials: A simulation study

BackgroundMulticentre randomized controlled trials (RCTs) routinely use randomization and analysis stratified by centre to control for differences between centres and to improve precision. No consensus has been reached on how to best analyze correlated continuous outcomes in such settings. Our objective was to investigate the properties of commonly used statistical models at various levels of clustering in the context of multicentre RCTs.MethodsAssuming no treatment by centre interaction, we compared six methods (ignoring centre effects, including centres as fixed effects, including centres as random effects, generalized estimating equation (GEE), and fixed- and random-effects centre-level analysis) to analyze continuous outcomes in multicentre RCTs using simulations over a wide spectrum of intraclass correlation (ICC) values, and varying numbers of centres and centre size. The performance of models was evaluated in terms of bias, precision, mean squared error of the point estimator of treatment effect, empirical coverage of the 95% confidence interval, and statistical power of the procedure.ResultsWhile all methods yielded unbiased estimates of treatment effect, ignoring centres led to inflation of standard error and loss of statistical power when within centre correlation was present. Mixed-effects model was most efficient and attained nominal coverage of 95% and 90% power in almost all scenarios. Fixed-effects model was less precise when the number of centres was large and treatment allocation was subject to chance imbalance within centre. GEE approach underestimated standard error of the treatment effect when the number of centres was small. The two centre-level models led to more variable point estimates and relatively low interval coverage or statistical power depending on whether or not heterogeneity of treatment contrasts was considered in the analysis.ConclusionsAll six models produced unbiased estimates of treatment effect in the context of multicentre trials. Adjusting for centre as a random intercept led to the most efficient treatment effect estimation across all simulations under the normality assumption, when there was no treatment by centre interaction.