Paul M. Brown

Risk for nocturnal hypoglycemia with biphasic insulin aspart 30 compared with biphasic human insulin 30 in adults with type 2 diabetes mellitus: a meta-analysis.

BACKGROUND Insulin is recommended as a second-line treatment after diet and metformin fail to reach and/or maintain glycemic targets considered to minimize the risk for long-term diabetic complications. Hypoglycemia and the fear of developing hypoglyce-mia, however, remain substantial barriers to the initiation and optimal use of insulin. OBJECTIVE The aim of this study was to compare biphasic insulin aspart 30 (BIAsp 30) with biphasic human insulin 30 (BHI 30) with respect to glycemic control and the risk for hypoglycemia using a meta-analysis of clinical trials comparing these insulins in patients with type 2 diabetes mellitus (T2DM). METHODS We included all published and unpublished, randomized, controlled trials in adult patients with T2DM (treatment duration > or = 12 weeks) for which individual patient data were available. All clinical databases and local trial registries of Novo Nordisk A/S (Soeborg, Denmark) were searched to identify clinical trials comparing the 2 products. The predefined primary end point of the study was the overall rate of nocturnal hypoglyce-mia (major, minor, and symptoms-only hypoglycemia occurring from 12:00-6:00 AM). Hypoglycemia was analyzed using a negative binomial distribution model, accounting for exposure time. Glycemic end points were analyzed at 12 to 16 weeks of treatment using ANCOVA, adjusting for baseline. Secondary safety end points were the rates of major hypoglycemia (hypoglycemia requiring third-party assistance), minor hypoglycemia (symptoms confirmed by plasma glucose [PG] <3.1 mmol/L), daytime hypoglycemia (major, minor, and symptoms-only hypoglycemia occurring from 6:01 AM-11:59 PM), overall hypoglycemia (the sum of all major, minor, and symptoms-only episodes), and change in weight from baseline to 12 to 16 weeks of treatment. Secondary efficacy end points were changes in glycosylated hemoglobin (HbA(1c)), fasting PG (FPG), postprandial PG increment (averaged over breakfast, lunch, and dinner), and insulin dose. RESULTS Nine randomized, parallel or crossover trials were included (N = 1674; male sex, 57%; mean [SD] age, 61.0 [10.6] years; body mass index, 26.7 [4.6] kg/m(2); HbA(1c), 8.1% [1.4%]; duration of diabetes, 10.9 [7.9] years). Rates of overall hypoglycemia were not significantly different (rate ratio [RR] = 1.08; 95% CI, 0.94-1.24; P = NS) between treatments. BIAsp 30 had a 50% lower rate of nocturnal hypoglycemia than BHI 30 (RR = 0.50; 95% CI, 0.38-0.67; P < 0.01), whereas the rate of daytime hypoglycemia was 24% lower for BHI 30 (RR = 1.24; 95% CI, 1.08-1.43; P < 0.01). The likelihood of major hypo-glycemia was significantly lower with BIAsp 30 compared with BHI 30 (odds ratio = 0.45; 95% CI, 0.22-0.93; P < 0.05). BIAsp 30 was associated with reduced PPG increment (averaged over breakfast, lunch and dinner) compared with BHI 30 (treatment difference, -0.31; 95% CI, -0.49 to -0.07; P < 0.01). There was a significantly larger reduction in FPG associated with BHI 30 (treatment difference, 0.63; 95% CI, 0.31-0.95; P < 0.01). However, no significant treatment difference was found for HbA(1c) (treatment difference, 0.04; 95% CI, -0.02 to 0.10; P = NS). CONCLUSION This meta-analysis found BIAsp 30 to be associated with a significantly lower rate of nocturnal and major hypoglycemia, but a significantly increased risk for daytime hypoglycemia, compared with BHI 30 at a similar level of HbA(1c) in patients with T2DM.

Composite End Points in Acute Heart Failure Research: Data Simulations Illustrate the Limitations

Composite end points are frequently used in clinical trials of investigational treatments for acute heart failure, eg, to boost statistical power and reduce the overall sample size. By incorporating multiple and varying types of clinical outcomes they provide a test for the overall efficacy of the treatment. Our objective is to compare the performance of popular composite end points in terms of statistical power and describe the uncertainty in these power estimates and issues concerning implementation. We consider several composites that incorporate outcomes of varying types (eg, time to event, categorical, and continuous). Data are simulated for 5 outcomes, and the composites are derived and compared. Power is evaluated graphically while varying the size of the treatment effects, thus describing the sensitivity of power to varying circumstances and eventualities such as opposing effects. The average z score offered the most power, although caution should be exercised when opposing effects are anticipated. Results emphasize the importance of an a priori assessment of power and scientific basis for construction, including the weighting of individual outcomes deduced from data simulations. The interpretation of a composite should be made alongside results from the individual components. The average z score offers the most power, but this should be considered in the research context and is not without its limitations.

The Prognostic Importance of Changes in Renal Function during Treatment for Acute Heart Failure Depends on Admission Renal Function

Background Worsening and improving renal function during acute heart failure have been associated with adverse outcomes but few studies have considered the admission level of renal function upon which these changes are superimposed. Objectives The objective of this study was to evaluate definitions that incorporate both admission renal function and change in renal function. Methods 696 patients with acute heart failure with calculable eGFR were classified by admission renal function (Reduced [R, eGFR<45 ml/min] or Preserved [P, eGFR≥45 ml/min]) and change over hospital admission (worsening [WRF]: eGFR ≥20% decline; stable [SRF]; and improving [IRF]: eGFR ≥20% increase). The primary outcome was all-cause mortality. The prevalence of Pres and Red renal function was 47.8% and 52.2%. The frequency of R-WRF, R-SRF, and R-IRF was 11.4%, 28.7%, and 12.1%, respectively; the incidence of P-WRF, P-SRF, and P-IRF was 5.7%, 35.3%, and 6.8%, respectively. Survival was shorter for patients with R-WRF compared to R-IRF (median survival times 13.9 months (95%CI 7.7–24.9) and 32.5 months (95%CI 18.8–56.1), respectively), resulting in an acceleration factor of 2.3 (p = 0.016). Thus, an increase compared with a decrease in renal function was associated with greater than two times longer survival among patients with Reduced renal function.

Insights into the importance of the electrocardiogram in patients with acute heart failure

Patients presenting to the emergency department (ED) with acute heart failure (AHF) are at an increased risk of morbidity and mortality. The electrocardiogram (ECG) is a routine investigation in patients with AHF used to identify potential causes and/or complications. It is unclear whether 12‐lead ECG characteristics can serve as a prognostic indicator in this population.

Composite End Points in Clinical Trials of Heart Failure Therapy: How Do We Measure the Effect Size?

Composite end points are popular outcomes in clinical trials of heart failure therapies. For example, a global rank composite is typically analyzed using a Mann–Whitney U test, and the results are summarized by the mean of ranks and a corresponding P value. The mean of ranks is uninformative, and a clinically meaningful estimate of the treatment effect is needed to communicate study results and facilitate an assessment of heterogeneity (the consistency of the effect across outcomes). The probability index is intuitive for clinicians, easy to calculate, and may be applied to various composites. We suggest a simple and familiar plot to assess heterogeneity across outcomes, which should be routine when analyzing composites. We think that the probability index provides an immediate and simple solution to an overt problem.

Frailty modelling for multitype recurrent events in clinical trials

Recurrent event outcomes are ubiquitous among clinical trial data which encourages a conventional approach to analysis. Yet a common feature of these data has received less attention, that is, survival times often comprise multiple types of events that may imply a disparity in cost and disease severity. Typically, we neglect this feature of the data by combining event-types or analyzing each type separately, thus ignoring any interdependence among them. This practice may reflect a dearth of readily available methods and software that more appropriately acknowledge the true data structure. We provide a review of the literature on multitype recurrent events and frailty modelling which reflects a renewed interest in the topic over the past decade and the emergence of software for estimation. Thus, a review of available methods seems timely, if not overdue.

Multitype Events and the Analysis of Heart Failure Readmissions: Illustration of a New Modeling Approach and Comparison With Familiar Composite End Points

Background— Heart failure–related hospital readmissions and mortality are often outcomes in clinical trials. Patients may experience multiple hospital readmissions over time with mortality acting as a dependent terminal event. Univariate composite end points are used for the analysis of readmissions. We may amend these approaches to include emergency department visits as a further outcome. An alternative multivariate modeling approach that categorizes hospital readmissions and emergency department visits as separate event types is proposed. Methods and Results— We seek to compare the modeling approach which handles event types as separate, correlated end points against composites that amalgamate them to create a unified end point. Using a heart failure data set for illustration, a model with random effects for event types is estimated. The time-to-first event, unmatched win-ratio, and days-alive-and-out-of-hospital composites are derived for comparison. The model provides supplementary statistics such as the correlation among event types and yields considerably more power than the competing composite end points. Conclusions— The effect on individual outcomes is lost when they are intermingled to form a univariate composite. Simultaneously modeling different outcomes provides an alternative or supplementary analysis that may yield greater statistical power and additional insights. Improvements in software have made the multitype events model easier to implement and thus a useful, more efficient option when analyzing heart failure hospital readmissions and emergency department visits.

Letter by Brown and Ezekowitz Regarding Article, “Development and Evolution of a Hierarchical Clinical Composite End Point for the Evaluation of Drugs and Devices for Acute and Chronic Heart Failure: A 20-Year Perspective”

We read with great interest the historical perspective of what is termed a hierarchical composite end point (HCE). A number of issues should be considered before adoption of this end point not fully elucidated by Packer.1 Not surprisingly, almost all the trials quoted have been neutral in the primary HCE outcome, and one needs to look at the actual components to understand the totality of the effect. An inherent attraction exists to capture the totality of effect in 1 place, but the HCE may not be it. First, the construction of the HCE can inadvertently place more emphasis on some outcomes over others (Figure). For example, a slight change to the definition of the HCE can impact the extent to which the mortality effect is represented in the overall result: when the mortality assessment time window is short, the influence or contribution of mortality on the composite is diminished relative to dyspnea, despite its obvious importance. The authors’ previous publication, which used an HCE as the primary end point, overwhelmed …

Characterization of hemodynamically stable acute heart failure patients requiring a critical care unit admission: Derivation, validation, and refinement of a risk score

Background Most patients with acute heart failure (AHF) admitted to critical care units (CCUs) are low acuity and do not require CCU‐specific therapies, suggesting that they could be managed in a lower‐cost ward environment. This study identified the predictors of clinical events and the need for CCU‐specific therapies in patients with AHF. Methods Model derivation was performed using data from patients in the ASCEND‐HF trial cohort (n = 7,141), and the Acute Heart Failure Emergency Management community‐based registry (n = 666) was used to externally validate the model and to test the incremental prognostic utility of 4 variables (heart failure etiology, troponin, B‐type natriuretic peptide [BNP], ejection fraction) using net reclassification index and integrated discrimination improvement. The primary outcome was an in‐hospital composite of the requirement for CCU‐specific therapies or clinical events. Results The primary composite outcome occurred in 545 (11.4%) derivation cohort participants (n = 4,767) and 7 variables were predictors of the primary composite outcome: body mass index, chronic respiratory disease, respiratory rate, resting dyspnea, hemoglobin, sodium, and blood urea nitrogen (c index = 0.633, Hosmer‐Lemeshow P = .823). In the validation cohort (n = 666), 87 (13.1%) events occurred (c index = 0.629, Hosmer‐Lemeshow P = .386) and adding ischemic heart failure, troponin, and B‐type natriuretic peptide improved model performance (net reclassification index 0.79, 95% CI 0.046‐0.512; integrated discrimination improvement 0.014, 95% CI 0.005‐0.0238). The final 10‐variable clinical prediction model demonstrated modest discrimination (c index = 0.702) and good calibration (Hosmer‐Lemeshow P = .547). Conclusions We derived, validated, and improved upon a clinical prediction model in an international trial and a community‐based cohort of AHF. The model has modest discrimination; however, these findings deserve further exploration because they may provide a more accurate means of triaging level of care for patients with AHF who need admission.