Alain Mallet

Heart Rate and Cardiac Rhythm Relationships With Bisoprolol Benefit in Chronic Heart Failure in CIBIS II Trial

Background —&bgr;-Blockade–induced benefit in heart failure (HF) could be related to baseline heart rate and treatment-induced heart rate reduction, but no such relationships have been demonstrated. Methods and Results —In CIBIS II, we studied the relationships between baseline heart rate (BHR), heart rate changes at 2 months (HRC), nature of cardiac rhythm (sinus rhythm or atrial fibrillation), and outcomes (mortality and hospitalization for HF). Multivariate analysis of CIBIS II showed that in addition to &bgr;-blocker treatment, BHR and HRC were both significantly related to survival and hospitalization for worsening HF, the lowest BHR and the greatest HRC being associated with best survival and reduction of hospital admissions. No interaction between the 3 variables was observed, meaning that on one hand, HRC-related improvement in survival was similar at all levels of BHR, and on the other hand, bisoprolol-induced benefit over placebo for survival was observed to a similar extent at any level of both BHR and HRC. Bisoprolol reduced mortality in patients with sinus rhythm (relative risk 0.58, P <0.001) but not in patients with atrial fibrillation (relative risk 1.16, P =NS). A similar result was observed for cardiovascular mortality and hospitalization for HF worsening. Conclusions —BHR and HRC are significantly related to prognosis in heart failure. &bgr;-Blockade with bisoprolol further improves survival at any level of BHR and HRC and to a similar extent. The benefit of bisoprolol is questionable, however, in patients with atrial fibrillation.

A new algorithm for haplotype‐based association analysis: the Stochastic‐EM algorithm

It is now widely accepted that haplotypic information can be of great interest for investigating the role of a candidate gene in the etiology of complex diseases. In the absence of family data, haplotypes cannot be deduced from genotypes, except for individuals who are homozygous at all loci or heterozygous at only one site. Statistical methodologies are therefore required for inferring haplotypes from genotypic data and testing their association with a phenotype of interest. Two maximum likelihood algorithms are often used in the context of haplotype‐based association studies, the Newton‐Raphson (NR) and the Expectation‐Maximisation (EM) algorithms. In order to circumvent the limitations of both algorithms, including convergence to local minima and saddle points, we here described how a stochastic version of the EM algorithm, referred to as SEM, could be used for testing haplotype‐phenotype association. Statistical properties of the SEM algorithm were investigated through a simulation study for a large range of practical situations, including small/large samples and rare/frequent haplotypes, and results were compared to those obtained by use of the standard NR algorithm. Our simulation study indicated that the SEM algorithm provides results similar to those of the NR algorithm, making the SEM algorithm of great interest for haplotype‐based association analysis, especially when the number of polymorphisms is quite large.

Alternative Approaches to Estimation of Population Pharmacokinetic Parameters: Comparison with the Nonlinear Mixed-Effect Model

Individual pharmacokinetic parameters can be viewed as independent realizations of a random variable. The probability density function of the variable is assumed to be specified by its first two moments (mean vector and covariance matrix), and these moments then characterize the distribution of the parameters in the population. The following methods are presented for estimation of population characteristics from a set of pharmacokinetic measurements in a sample of subjects: The Global Two-Stage Approach (GTS) uses estimates (and their covariances) of individual parameters obtained after separate fitting of each individuals data. The Iterated Two-Stage Approach (ITS) makes the GTS procedure iterative, using refined bayesian estimates of individual parameters at each step. The Nonlinear Filtering Approach (NLF) also relies on individual parameter estimates produced by using an optimal filter on each subjects data. The three methods give exact results (maximum likelihood estimates), as does NONMEM (the Nonlinear Mixed-Effect Model Approach), when the individual pharmacokinetic model is linear with respect to the parameters and when the distributions of the pharmacokinetic parameters and of the measurement noise in the individual data are both multivariate normal. When the individual pharmacokinetic model is statistically nonlinear (the usual case), the methods differ with respect to: (1) their strategy for handling nonlinearity, (2) their ability to deal with any type of data (experimental and/or routine), and (3) their sensitivity to the amplitude of random effects. With regard to computational aspects, both the computer memory storage requirements and the amount of computation required for the GTS approach are much smaller than for the three other methods. Contrasting considerations as well as results of simulations suggest that GTS, ITS, and, in future, NLF may be valuable alternatives to NONMEM or modifications of it for estimation of population characteristics of pharmacokinetic parameters.

Prognostic Value of Bisoprolol-Induced Hemodynamic Effects in Heart Failure During the Cardiac Insufficiency BIsoprolol Study (CIBIS)

BACKGROUND To further evaluate the mechanism of beta-blocker-induced benefits in heart failure, the relationships between bisoprolol-induced hemodynamic effects and survival were studied during the Cardiac Insufficiency BIsoprolol Study (CIBIS). METHODS AND RESULTS In 557 patients studied, bisoprolol significantly reduced heart rate (-16.3+/-15.3 versus -1.6+/-13.4 bpm, respectively; P<.001) compared with placebo at 2 months after inclusion in the study. Heart rate change over time had the highest predictive value for survival (P<.01). Left ventricular fractional shortening (LVFS) significantly increased in the bisoprolol group compared with the placebo group 5 months after inclusion (+0.04+/-0.06 versus -0.001+/-0.05, respectively; P<.001; n=160). LVFS change over time was also significantly correlated with further survival (P<.02 by Cox analysis). Using a nonparametric approach, we demonstrated a significant interaction between study treatment group and LVFS over time. Patients who demonstrated improvement of LVFS over time (82% and 51% of patients in the bisoprolol and the placebo groups, respectively; P<.02) were at lower risk, but the hazard did not further decrease with a further increase of fractional shortening, and there was no significant difference between study treatment groups. Finally, it could be demonstrated that each of the three factors (heart rate change over time, LVFS change over time, and bisoprolol treatment) made a specific contribution to mortality rate. CONCLUSIONS Preservation of left ventricular function appears to play a key role in the bisoprolol-induced beneficial effects on prognosis in heart failure. Short-term beta-blocker-induced cardiac effects could provide a means to identify those patients who will experience improved survival over the long term.

Clinicopathological forms and prognostic index in Budd-Chiari syndrome

BACKGROUND A recent study in patients with Budd-Chiari syndrome showed the value of a prognostic index including age, Pugh score, ascites and serum creatinine. Surgical portosystemic shunt did not appear to improve survival. AIMS To validate these findings in an independent sample; to evaluate a classification into three forms according to the presence of features of acute injury, chronic lesions, or both of them (types I, II or III, respectively); and to assess whether taking into account this classification would alter our previous conclusions. METHODS Multivariate Cox model survival analysis, first on 69 new patients; second, on these 69 and 54 previous patients, all diagnosed since 1985. RESULTS Previous prognostic index had a significant prognostic value (P<0.0001) which was further improved by taking into account type III form (P<0.001). Type III form was associated with the poorest outcome. No significant impact of surgical shunting on survival was disclosed. CONCLUSIONS The prognosis of Budd-Chiari syndrome can be based on age, Pugh score, ascites, serum creatinine and the presence of features indicating acute injury superimposed on chronic lesions (type III form). The idea that surgical shunting has no significant impact on survival is reinforced by these findings.

Anticoagulant (Fluindione)-Aspirin Combination in Patients with High-Risk Atrial Fibrillation

Background: A combination of low-dose aspirin with anticoagulants may provide better protection against thromboembolic events compared to anticoagulants alone in high-risk patients with atrial fibrillation. Objective: Evaluation of the preventive efficacy against nonfatal thromboembolic events and vascular deaths of the combination of the oral anticoagulant fluindione and aspirin (100 mg) in patients with high-risk atrial fibrillation. Methods: A multicenter, placebo-controlled, double-blind, randomized trial was conducted at 49 investigating centers in France. Atrial fibrillation patients with a previous thromboembolic event or older than 65 years and with either a history of hypertension, a recent episode of heart failure or decreased left ventricular function were included in the study. Patients were treated with fluindione plus placebo (i.e. anticoagulant alone) or fluindione plus aspirin (i.e. combination therapy), with an international normalized ratio target of between 2 and 2.6. The combined primary endpoint was stroke (ischemic or hemorrhagic), myocardial infarction, systemic arterial emboli or vascular death. The secondary endpoint was the incidence of hemorrhagic complications. Results: The 157 participants (average age 74 years; 52% women; 42% with paroxysmal atrial fibrillation) were followed for an average of 0.84 years. Three nonfatal thromboembolic events were observed (1 in the anticoagulation group, 2 in the combination group) and 6 patients died (3 in the anticoagulation group, 3 in the combination group), none of them from a thromboembolic complication. However, 3 deaths were secondary to severe hemorrhagic complications (1 in the anticoagulation group, 2 in the combination group). Nonfatal hemorrhagic complications occurred more often in the combination group (n = 10, 13.1%) compared to the anticoagulation group (n = 1, 1.2%) (p = 0.003). Conclusion: The combination of aspirin with anticoagulant is associated with increased bleeding in elderly atrial fibrillation patients. The effect on thromboembolism and the overall balance of benefit to risk could not be accurately assessed in this study due to the limited number of ischemic events.

Nonparametric maximum likelihood estimation for population pharmacokinetics, with application to cyclosporine

A new method, nonparametric maximum likelihood (NPML), for statistical analysis of population kinetic data is proposed. NPML provides a discrete estimate of the whole probability density function of the pharmacokinetic parameters. This permits a straightforward derivation of usual population characteristics. To illustrate the application of the NPML method, a population analysis of cyclosporine RIA measured plasma levels in 188 bone marrow transplant patients after intravenous infusion, is presented. The capability of NPML to extract population information from sparse individual data is also outlined.

Robust Optimal Design for the Estimation of Hyperparameters in Population Pharmacokinetics

The expectation of the determinant of the inverse of the population Fisher information matrix is proposed as a criterion to evaluate and optimize designs for the estimation of population pharmacokinetic (PK) parameters. Given a PK model, a measurement error model, a parametric distribution of the parameters and a prior distribution representing the belief about the hyperparameters to be estimated, the EID criterion is minimized in order to find the optimal population design. In this approach, a group is defined as a number of subjects to whom the same sampling schedule (i.e., the number of samples and their timing) is applied. The constraints, which are defined a priori, are the number of groups, the size of each group and the number of samples per subject in each group. The goal of the optimization is to determine the optimal sampling times in each group. This criterion is applied to a one-compartment open model with first-order absorption. The error model is either homoscedastic or heteroscedastic with constant coefficient of variation. Individual parameters are assumed to arise from a lognormal distribution with mean vectorMand covariance matrixC. Uncertainties about theMandCare accounted for by a prior distribution which is normal forMand Wishart forC. Sampling times are optimized by using a stochastic gradient algorithm. Influence of the number of different sampling schemes, the number of subjects per sampling schedule, the number of samples per subject in each sampling scheme, the uncertainties onMandCand the assumption about the error model and the dose have been investigated.

APriori lithium dosage regimen using population characteristics of pharmacokinetic parameters

The important problem of initiation of long-term lithium treatments tackled by means of the selection of an a prioridosage regimen based on the presumed efficacy of lithium and absence of toxicity. The pharmacokinetics of Li+ion is represented by a four-compartment open model including the supposed first-order processes for the release of the active compound from the dosage form and its absorption. Experimental protocols for measurements of serum concentrations and of urinary amounts after single and multiple dosing to healthy volunteers were derived with several oral dosage forms. Estimation of the pharmacokinetic parameters for each subject made it possible to validate the model for the various dosage forms. The interindividual variability of these parameters is taken into account by estimating the characteristics of the statistical distribution for the whole population. A dosage regimen is considered optimum when serum concentration profiles at steady state range from the threshold of efficacy (0.8 mmol/liter) to the threshold of toxicity (2.0 mmol/liter). When the number of daily intakes is fixed, the search for the optimum dose for the whole population is effected by minimizing the expected value of the random variable which characterizes the risks of excursion out of the therapeutic range. By this means universal dosages are shown to be unsatisfactory. However, certain dosage regimens individualized with respect to the renal clearance value of lithium and based on two or three daily intakes can give excellent results even when conventional dosage forms are used.

Comparison of four sib-pair linkage methods for analyzing sibships with more than two affecteds : Interest of the binomial maximum likelihood approach

Family samples collected for sib‐pair linkage studies usually include some sibships with more than two affecteds (multiplex sibships). Several methods have been proposed to take into account these multiplex sibships, and four of them are discussed in this work. Two methods, which are the most widely used, are based on the number of alleles shared by the sib‐pairs constitutive of the multiplex sibship, with the first using the total number of these shared alleles (“all possible pairs” method) and the second considering a weighted number of these alleles (weighted method). The two other approaches considered the sibship as a whole, with in particular a likelihood method based on a binomial distribution of parental alleles among affected offspring. We theoretically show that, in the analysis of sibships with two affecteds, this likelihood method is expected to be more powerful than the classical mean test when a common asymptotic type I error is used. The variation of the sibship informativeness (assessed by the proportion of heterozygous parents) according to the number of affected sibs is investigated under various genetic models. Simulations under the null hypothesis of no linkage indicate that the “all possible pairs” is anticonservative, especially for type I errors ≤ 0.001, whereas the weighted method generally provides satisfactory results. The likelihood method shows very consistent results in terms of type I errors, whatever the sample size, and provides power levels similar to those of the other methods. This binomial likelihood approach, which accounts in a natural way for multiplex sibships and provides a simple likelihood‐ratio test for linkage involving a single parameter, appears to be a quite interesting alternative to analyze sib‐pair studies. Genet. Epidemiol. 15:371–390,1998.