Rationale and design of a randomized, double-blind, placebo controlled multicenter trial to study efficacy, security, and long term effects of intermittent repeated levosimendan administration in patients with advanced heart failure: LAICA study.

BACKGROUND Advanced heart failure (HF) is associated with high morbidity and mortality; it represents a major burden for the health system. Episodes of acute decompensation requiring frequent and prolonged hospitalizations account for most HF-related expenditure. Inotropic drugs are frequently used during hospitalization, but rarely in out-patients. The LAICA clinical trial aims to evaluate the effectiveness and safety of monthly levosimendan infusion in patients with advanced HF to reduce the incidence of hospital admissions for acute HF decompensation. METHODS The LAICA study is a multicenter, prospective, randomized, double-blind, placebo-controlled, parallel group trial. It aims to recruit 213 out-patients, randomized to receive either a 24-h infusion of levosimendan at 0.1 μg/kg/min dose, without a loading dose, every 30 days, or placebo. RESULTS The main objective is to assess the incidence of admission for acute HF worsening during 12 months. Secondarily, the trial will assess the effect of intermittent levosimendan on other variables, including the time in days from randomization to first admission for acute HF worsening, mortality and serious adverse events. CONCLUSIONS The LAICA trial results could allow confirmation of the usefulness of intermittent levosimendan infusion in reducing the rate of hospitalization for HF worsening in advanced HF outpatients.

Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study.

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model.