Mark Donoghoe

Benefits and Safety of Long-Term Fenofibrate Therapy in People With Type 2 Diabetes and Renal Impairment The FIELD study

OBJECTIVE Diabetic patients with moderate renal impairment (estimated glomerular filtration rate [eGFR] 30–59 mL/min/1.73 m2) are at particular cardiovascular risk. Fenofibrate’s safety in these patients is an issue because it may elevate plasma creatinine. Furthermore, guidelines regarding fenofibrate dosing in renal impairment vary internationally. We investigated fenofibrate’s effects on cardiovascular and end-stage renal disease (ESRD) events, according to eGFR, in the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) Study. RESEARCH DESIGN AND METHODS Type 2 diabetic patients (aged 50–75 years) with eGFR ≥30 mL/min/1.73 m2 were randomly allocated to a fixed dose of fenofibrate (200 mg daily) (n = 4,895) or placebo (n = 4,900) for 5 years. Baseline renal function (Modification of Diet in Renal Disease equation) was grouped by eGFR (30–59, 60–89, and ≥90 mL/min/1.73 m2). The prespecified outcome was total cardiovascular events (composite of cardiovascular death, myocardial infarction, stroke, and coronary/carotid revascularization). Serious adverse events and instances of ESRD (plasma creatinine >400 μmol/L, dialysis, renal transplant, or renal death) were recorded. Analysis was by intention to treat. RESULTS Overall, fenofibrate reduced total cardiovascular events, compared with placebo (hazard ratio 0.89 [95% CI 0.80–0.99]; P = 0.035). This benefit was not statistically different across eGFR groupings (P = 0.2 for interaction) (eGFR 30–59 mL/min/1.73 m2: 0.68 [0.47–0.97], P = 0.035; eGFR ≥90 mL/min/1.73 m2: 0.85 [0.70–1.02], P = 0.08). ESRD rates were similar between treatment arms, without adverse safety signals of fenofibrate use in renal impairment. CONCLUSIONS Patients with type 2 diabetes and moderate renal impairment benefit from long-term fenofibrate, without excess drug-related safety concerns compared with those with no or mild renal impairment. Fenofibrate treatment should not be contraindicated in moderate renal impairment, suggesting that current guidelines may be too restrictive.

Decreased hypersensitivity reactions with carboplatin-pegylated liposomal doxorubicin compared to carboplatin-paclitaxel combination: Analysis from the GCIG CALYPSO relapsing ovarian cancer trial

OBJECTIVE To describe and analyze observed hypersensitivity reactions (HSR) from the randomized, multicenter phase III CALYPSO trial that evaluated the efficacy and safety of the combination of carboplatin and pegylated liposomal doxorubicin (CD) compared with standard carboplatin-paclitaxel (CP) in patients with platinum-sensitive relapsed ovarian cancer (ROC). METHODS HSR documented within case report forms and SAE reports were specifically analyzed. Analyses were based on the population with allergy of any grade and for grade >2 allergy. RESULTS Overall 976 patients were recruited to this phase III trial, with toxicity data available for 466 and 502 on the CD and CP arms, respectively. There was a 15.5% HSR rate associated with CD (2.4% grade >2) versus 33.1% with CP (8.8% grade >2), p<0.001. HSRs occurred more often during first cycle in the CD (46%) arm than in the CP arm (16%). Multivariate predictors of allergy were chemotherapy regimen and age; patients randomized to CD and patients ≥ 70 years old on CP had less allergy. Few patients (<6%) stopped treatment due to allergy. Allergy rates were higher in patients who did not receive prior supportive treatment; however there was no relationship between allergy and the type of carboplatin product received, or response rate. CONCLUSIONS Use of PLD with carboplatin instead of paclitaxel and older age were the only 2 factors predicting a low rate of HSRs in patients with ROC. CD has previously demonstrated superior progression-free survival and therapeutic index than CP. Taken together these data support the use of CD as a safe and effective therapeutic option for platinum-sensitive ROC.

Impact of metabolic syndrome and its components on cardiovascular disease event rates in 4900 patients with type 2 diabetes assigned to placebo in the FIELD randomised trial

BackgroundPatients with the metabolic syndrome are more likely to develop type 2 diabetes and may have an increased risk of cardiovascular disease (CVD) events.We aimed to establish whether CVD event rates were influenced by the metabolic syndrome as defined by the World Health Organisation (WHO), the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) and the International Diabetes Federation (IDF) and to determine which component(s) of the metabolic syndrome (MS) conferred the highest cardiovascular risk in in 4900 patients with type 2 diabetes allocated to placebo in the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial.Research design and methodsWe determined the influence of MS variables, as defined by NCEP ATPIII, IDF and WHO, on CVD risk over 5 years, after adjustment for CVD, sex, HbA1c, creatinine, and age, and interactions between the MS variables in a Cox proportional-hazards model.ResultsAbout 80% had hypertension, and about half had other features of the metabolic syndrome (IDF, ATPIII). There was no difference in the prevalence of metabolic syndrome variables between those with and without CVD at study entry. The WHO definition identified those at higher CVD risk across both sexes, all ages, and in those without prior CVD, while the ATPIII definition predicted risk only in those aged over 65 years and in men but not in women. Patients meeting the IDF definition did not have higher risk than those without IDF MS.CVD risk was strongly influenced by prior CVD, sex, age (particularly in women), baseline HbA1c, renal dysfunction, hypertension, and dyslipidemia (low HDL-c, triglycerides > 1.7 mmol/L). The combination of low HDL-c and marked hypertriglyceridemia (> 2.3 mmol/L) increased CVD risk by 41%. Baseline systolic blood pressure increased risk by 16% per 10 mmHg in those with no prior CVD, but had no effect in those with CVD. In those without prior CVD, increasing numbers of metabolic syndrome variables (excluding waist) escalated risk.ConclusionAbsence of the metabolic syndrome (by the WHO definition) identifies diabetes patients without prior CVD, who have a lower risk of future CVD events. Hypertension and dyslipidemia increase risk.

Associations between the use of metformin, sulphonylureas, or diet alone and cardiovascular outcomes in 6005 people with type 2 diabetes in the FIELD study

AIMS We aimed to determine the associations between metformin or sulphonylurea monotherapy at study entry into the FIELD diabetes trial and (1) metabolic risk factors, (2) risk of a first major cardiovascular (CVD) outcome, and (3) the effect of each therapy on the risk-modifying effect of fenofibrate. METHODS Patients receiving metformin or sulphonylureas without insulin therapy were compared for the relative risk of CVD outcomes, adjusted for differences in baseline characteristics likely to affect risk. RESULTS Metformin-treated patients were likely to be younger, female, or obese. Metformin was associated with higher levels of lipids (other than LDL-C) and homocysteine (P<0.001). Sulphonylurea-treated patients had a longer history of diabetes and more CVD and microvascular disease. Sulphonylurea treatment was associated with higher plasma creatinine and lower plasma HDL-C (P<0.001). The risks of all CVD outcomes were higher for those on sulphonylureas than diet alone, but were nonsignificant after adjustment for the duration and intensity of diabetes and severity of risk factors. Metformin and sulphonylureas did not significantly influence the benefits of fenofibrate on CVD outcomes. CONCLUSIONS Apparent differences in the risk of CVD outcomes associated with oral hypoglycemics therapy were largely abolished by adjustment for diabetes and CVD risk factors.

Opposite associations between alanine aminotransferase and γ-glutamyl transferase levels and all-cause mortality in type 2 diabetes: Analysis of the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study

AIMS Reported associations between liver enzymes and mortality may not hold true in type 2 diabetes, owing to a high prevalence of non-alcoholic fatty liver disease, which has been linked to cardiovascular disease and mortality in its own right. Our study aimed to determine whether alanine aminotransferase (ALT) or γ-glutamyl transferase (GGT) levels predict mortality in type 2 diabetes, and to examine possible mechanisms. METHODS Data from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study were analyzed to examine the relationship between liver enzymes and all-cause and cause-specific mortality over 5years. RESULTS Over 5years, 679 (6.9%) individuals died. After adjustment, for every standard deviation increase in ALT (13.2U/L), the HR for death on study was 0.85 (95% CI 0.78-0.93), p<0.001. Conversely, GGT >70U/L, compared with GGT ≤70U/L, had HR 1.82 (1.48-2.24), p<0.001. For cause-specific mortality, lower ALT was associated with a higher risk of cardiovascular death only, whereas GGT >70U/L was associated with higher risks of death due to cardiovascular disease, cancer and non-cancer/non-cardiovascular causes. The relationship for ALT persisted after adjustment for indirect measures of frailty but was attenuated by elevated hsCRP. CONCLUSIONS As in the general population, ALT has a negative, and GGT a positive, correlation with mortality in type 2 diabetes when ALT is less than two times the upper limit of normal. The relationship for ALT appears specific for death due to cardiovascular disease. Links of low ALT with frailty, as a potential mechanism for relationships seen, were neither supported nor conclusively refuted by our analysis and other factors are also likely to be important in those with type 2 diabetes.

Flexible Regression Models for Rate Differences, Risk Differences and Relative Risks

Abstract Generalized additive models (GAMs) based on the binomial and Poisson distributions can be used to provide flexible semi-parametric modelling of binary and count outcomes. When used with the canonical link function, these GAMs provide semi-parametrically adjusted odds ratios and rate ratios. For adjustment of other effect measures, including rate differences, risk differences and relative risks, non-canonical link functions must be used together with a constrained parameter space. However, the algorithms used to fit these models typically rely on a form of the iteratively reweighted least squares algorithm, which can be numerically unstable when a constrained non-canonical model is used. We describe an application of a combinatorial EM algorithm to fit identity link Poisson, identity link binomial and log link binomial GAMs in order to estimate semi-parametrically adjusted rate differences, risk differences and relative risks. Using smooth regression functions based on B-splines, the method provides stable convergence to the maximum likelihood estimates, and it ensures that the estimates always remain within the parameter space. It is also straightforward to apply a monotonicity constraint to the smooth regression functions. We illustrate the method using data from a clinical trial in heart attack patients.

Stable computational methods for additive binomial models with application to adjusted risk differences

Risk difference is an important measure of effect size in biostatistics, for both randomised and observational studies. The natural way to adjust risk differences for potential confounders is to use an additive binomial model, which is a binomial generalised linear model with an identity link function. However, implementations of the additive binomial model in commonly used statistical packages can fail to converge to the maximum likelihood estimate (MLE), necessitating the use of approximate methods involving misspecified or inflexible models. A novel computational method is proposed, which retains the additive binomial model but uses the multinomial-Poisson transformation to convert the problem into an equivalent additive Poisson fit. The method allows reliable computation of the MLE, as well as allowing for semi-parametric monotonic regression functions. The performance of the method is examined in simulations and it is used to analyse two datasets from clinical trials in acute myocardial infarction. Source code for implementing the method in R is provided as supplementary material (see Appendix A).

Pulse Oximeter Saturation Targeting and Oximeter Changes in the Benefits of Oxygen Saturation Targeting (BOOST)-II Australia and BOOST-II UK Oxygen Trials

&NA; Infants in the Australian and UK Benefits of Oxygen Saturation Targeting‐II trials treated using revised oximeters spent more time within their planned pulse oximeter saturation target ranges than infants treated using the original oximeters (P < .001). This may explain the larger mortality difference seen with revised oximeters. If so, average treatment effects from the Neonatal Oxygen Prospective Meta‐analysis trials may be underestimates.

The importance of censoring in competing risks analysis of the subdistribution hazard

BackgroundThe analysis of time-to-event data can be complicated by competing risks, which are events that alter the probability of, or completely preclude the occurrence of an event of interest. This is distinct from censoring, which merely prevents us from observing the time at which the event of interest occurs. However, the censoring distribution plays a vital role in the proportional subdistribution hazards model, a commonly used method for regression analysis of time-to-event data in the presence of competing risks.MethodsWe present the equations that underlie the proportional subdistribution hazards model to highlight the way in which the censoring distribution is included in its estimation via risk set weights. By simulating competing risk data under a proportional subdistribution hazards model with different patterns of censoring, we examine the properties of the estimates from such a model when the censoring distribution is misspecified. We use an example from stem cell transplantation in multiple myeloma to illustrate the issue in real data.ResultsModels that correctly specified the censoring distribution performed better than those that did not, giving lower bias and variance in the estimate of the subdistribution hazard ratio. In particular, when the covariate of interest does not affect the censoring distribution but is used in calculating risk set weights, estimates from the model based on these weights may not reflect the correct likelihood structure and therefore may have suboptimal performance.ConclusionsThe estimation of the censoring distribution can affect the accuracy and conclusions of a competing risks analysis, so it is important that this issue is considered carefully when analysing time-to-event data in the presence of competing risks.

Estimation of adjusted rate differences using additive negative binomial regression.

Rate differences are an important effect measure in biostatistics and provide an alternative perspective to rate ratios. When the data are event counts observed during an exposure period, adjusted rate differences may be estimated using an identity-link Poisson generalised linear model, also known as additive Poisson regression. A problem with this approach is that the assumption of equality of mean and variance rarely holds in real data, which often show overdispersion. An additive negative binomial model is the natural alternative to account for this; however, standard model-fitting methods are often unable to cope with the constrained parameter space arising from the non-negativity restrictions of the additive model. In this paper, we propose a novel solution to this problem using a variant of the expectation-conditional maximisation-either algorithm. Our method provides a reliable way to fit an additive negative binomial regression model and also permits flexible generalisations using semi-parametric regression functions. We illustrate the method using a placebo-controlled clinical trial of fenofibrate treatment in patients with type II diabetes, where the outcome is the number of laser therapy courses administered to treat diabetic retinopathy. An R package is available that implements the proposed method. Copyright