Umberto Picchini

Effects of levosimendan on right ventricular afterload in patients with acute respiratory distress syndrome: a pilot study.

Objective:Acute respiratory distress syndrome (ARDS) is frequently associated with increased pulmonary vascular resistance and thus with systolic load of the right ventricle. We hypothesized that levosimendan, a new calcium sensitizer with potential pulmonary vasodilator properties, improves hemodynamics by unloading the right ventricle in patients with ARDS. Design:Prospective, randomized, placebo-controlled, pilot study. Setting:Twenty-two-bed multidisciplinary intensive care unit of a university hospital. Patients:Thirty-five patients with ARDS in association with septic shock. Interventions:Patients were randomly allocated to receive a 24-hr infusion of either levosimendan 0.2 &mgr;g/kg/min (n = 18) or placebo (n = 17). Data from right heart catheterization, cardiac magnetic resonance, arterial and mixed venous oxygen tensions and saturations, and carbon dioxide tensions were obtained before and 24 hrs after drug infusion. Measurements and Main Results:At a mean arterial pressure between 70 and 80 mm Hg (sustained with norepinephrine infusion), levosimendan increased cardiac index (from 3.8 ± 1.1 to 4.2 ± 1.0 L/min/m2) and decreased mean pulmonary artery pressure (from 29 ± 3 to 25 ± 3 mm Hg) and pulmonary vascular resistance index (from 290 ± 77 to 213 ± 50 dynes/s/cm5/m2; each p < .05). Levosimendan also decreased right ventricular end-systolic volume and increased right ventricular ejection fraction (p < .05). In addition, levosimendan increased mixed venous oxygen saturation (from 63 ± 8 to 70 ± 8%; p < .01). Conclusions:This study provides evidence that levosimendan improves right ventricular performance through pulmonary vasodilator effects in septic patients with ARDS. A large multiple-center trial is needed to investigate whether levosimendan is able to improve the overall prognosis of patients with sepsis and ARDS.

Prophylactic fenoldopam for renal protection in sepsis: a randomized, double-blind, placebo-controlled pilot trial.

Objective:Acute renal failure is common in septic patients. Fenoldopam, a dopamine-1 receptor agonist, increases renal blood flow and may, therefore, reduce the risk of acute renal failure in such patients. Accordingly, we sought to determine the safety and efficacy of fenoldopam for the prevention of acute renal failure in septic patients. Design:Prospective, double-blind, placebo-controlled trial. Setting:Three multidisciplinary intensive care units at a university hospital. Patients:Three hundred septic patients with baseline serum creatinine concentrations <150 &mgr;mol/L. Interventions:We randomized patients to a continuous infusion of either fenoldopam (n = 150) at 0.09 &mgr;g·kg−1·min−1 or placebo (n = 150) while in the intensive care unit. The primary outcome measure was the incidence of acute renal failure, defined as a serum creatinine concentration increase to >150 &mgr;mol/L, during study drug infusion. Measurements and main results:The incidence of acute renal failure was significantly lower in the fenoldopam group compared with the control group (29 vs. 51 patients; p = .006). The odds ratio of developing acute renal failure for patients treated with fenoldopam was estimated to be 0.47 (p = .005). The difference in the incidence of severe acute renal failure (creatinine >300 &mgr;mol/L), however, failed to achieve statistical significance (10 vs. 21; p = .056). The length of intensive care unit stay in surviving patients was significantly lower in the fenoldopam group compared with the control group (10.64 ± 9.3 vs. 13.4 ± 14.0; p < .001). There were no complications of fenoldopam infusion. A direct effect of treatment on the probability of death, beyond its effect on acute renal failure, was not significant (odds ratio = 0.68, p = .1). Conclusions:Compared with placebo, low-dose fenoldopam resulted in a smaller increase in serum creatinine in septic patients. The clinical significance of this finding is uncertain. A large multiple-center trial is now needed to confirm these findings.

Terlipressin versus Norepinephrine to Counteract Anesthesia-induced Hypotension in Patients Treated with Renin-Angiotensin System Inhibitors: Effects on Systemic and Regional Hemodynamics

Background: Terlipressin has been suggested as the ideal drug to treat anesthesia-induced hypotension in patients under long-term renin-angiotensin system inhibitor treatment for arterial hypertension. The authors compared the effects of terlipressin and norepinephrine on systemic hemodynamic parameters and gastric mucosal perfusion using a laser Doppler flowmetry technique in patients treated with renin-angiotensin system inhibitors who experienced hypotension at induction of anesthesia. Methods: Thirty-two patients scheduled for carotid endarterectomy under general anesthesia and treated with renin-angiotensin system inhibitors had hypotension after induction of general anesthesia. They were randomized to receive 1 mg of terlipressin (n = 16) or norepinephrine infusion (n = 16) to counteract anesthesia-induced hypotension. A laser Doppler probe was introduced into the gastric lumen. All measurements were performed just before surgery, during hypotension, at 30 min, and at 4 h. Results: Terlipressin produced an increase in mean arterial pressure and a decrease in gastric mucosal perfusion detected by laser Doppler flowmetry (P < 0.05) over 30 min that were sustained for 4 h. During the infusion, norepinephrine produced an increase in mean arterial pressure and in gastric mucosal perfusion detected by laser Doppler flowmetry (P < 0.05). If compared to norepinephrine, terlipressin reduced oxygen delivery and oxygen consumption (P < 0.05) and increased arterial lactate concentrations (P < 0.05). Conclusion: This study showed the efficacy of terlipressin in the treatment of hypotension episodes in anesthetized patients chronically treated with renin-angiotensin system inhibitors, angiotensin converting-enzyme inhibitors, and angiotensin II receptor antagonists. However, the negative effects on gastric mucosal perfusion and the risk of iatrogenic oxygen supply dependency of terlipressin need to be taken into account.

Practical estimation of high dimensional stochastic differential mixed-effects models

Stochastic differential equations (SDEs) are established tools for modeling physical phenomena whose dynamics are affected by random noise. By estimating parameters of an SDE, intrinsic randomness of a system around its drift can be identified and separated from the drift itself. When it is of interest to model dynamics within a given population, i.e. to model simultaneously the performance of several experiments or subjects, mixed-effects modelling allows for the distinction of between and within experiment variability. A framework for modeling dynamics within a population using SDEs is proposed, representing simultaneously several sources of variation: variability between experiments using a mixed-effects approach and stochasticity in the individual dynamics, using SDEs. These stochastic differential mixed-effects models have applications in e.g. pharmacokinetics/pharmacodynamics and biomedical modelling. A parameter estimation method is proposed and computational guidelines for an efficient implementation are given. Finally the method is evaluated using simulations from standard models like the two-dimensional Ornstein-Uhlenbeck (OU) and the square root models.

Inference for SDE Models via Approximate Bayesian Computation

Models defined by stochastic differential equations (SDEs) allow for the representation of random variability in dynamical systems. The relevance of this class of models is growing in many applied research areas and is already a standard tool to model, for example, financial, neuronal, and population growth dynamics. However, inference for multidimensional SDE models is still very challenging, both computationally and theoretically. Approximate Bayesian computation (ABC) allows to perform Bayesian inference for models which are sufficiently complex that the likelihood function is either analytically unavailable or computationally prohibitive to evaluate. A computationally efficient ABC-MCMC algorithm is proposed, halving the running time in our simulations. Focus here is on the case where the SDE describes latent dynamics in state-space models; however, the methodology is not limited to the state-space framework. We consider simulation studies for a pharmacokinetics/pharmacodynamics model and for stochastic chemical reactions and we provide a Matlab package that implements our ABC-MCMC algorithm.

Parameters of the diffusion leaky integrate-and-fire neuronal model for a slowly fluctuating signal

Stochastic leaky integrate-and-fire (LIF) neuronal models are common theoretical tools for studying properties of real neuronal systems. Experimental data of frequently sampled membrane potential measurements between spikes show that the assumption of constant parameter values is not realistic and that some (random) fluctuations are occurring. In this article, we extend the stochastic LIF model, allowing a noise source determining slow fluctuations in the signal. This is achieved by adding a random variable to one of the parameters characterizing the neuronal input, considering each interspike interval (ISI) as an independent experimental unit with a different realization of this random variable. In this way, the variation of the neuronal input is split into fast (within-interval) and slow (between-intervals) components. A parameter estimation method is proposed, allowing the parameters to be estimated simultaneously over the entire data set. This increases the statistical power, and the average estimate over all ISIs will be improved in the sense of decreased variance of the estimator compared to previous approaches, where the estimation has been conducted separately on each individual ISI. The results obtained on real data show good agreement with classical regression methods.

A mathematical model of the euglycemic hyperinsulinemic clamp

BackgroundThe Euglycemic Hyperinsulinemic Clamp (EHC) is the most widely used experimental procedure for the determination of insulin sensitivity, and in its usual form the patient is followed under insulinization for two hours. In the present study, sixteen subjects with BMI between 18.5 and 63.6 kg/m2 were studied by long-duration (five hours) EHC.ResultsFrom the results of this series and from similar reports in the literature it is clear that, in obese subjects, glucose uptake rates continue to increase if the clamp procedure is prolonged beyond the customary 2 hours. A mathematical model of the EHC, incorporating delays, was fitted to the recorded data, and the insulin resistance behaviour of obese subjects was assessed analytically. Obese subjects had significantly less effective suppression of hepatic glucose output and higher pancreatic insulin secretion than lean subjects. Tissue insulin resistance appeared to be higher in the obese group, but this difference did not reach statistical significance.ConclusionThe use of a mathematical model allows a greater amount of information to be recovered from clamp data, making it easier to understand the components of insulin resistance in obese vs. normal subjects.

Maximum likelihood estimation of a time-inhomogeneous stochastic differential model of glucose dynamics

Stochastic differential equations (SDEs) are assuming an important role in the definition of dynamical models allowing for explanation of internal variability (stochastic noise). SDE models are well established in many fields, such as investment finance, population dynamics, polymer dynamics, hydrology and neuronal models. The metabolism of glucose and insulin has not yet received much attention from SDE modellers, except from a few recent contributions, because of methodological and implementation difficulties in estimating SDE parameters. Here, we propose a new SDE model for the dynamics of glycemia during a euglycemic hyperinsulinemic clamp experiment, introducing system noise in tissue glucose uptake and apply for its estimation a closed-form Hermite expansion of the transition densities of the solution process. The present work estimates the new model parameters using a computationally efficient approximate maximum likelihood approach. By comparison with other currently used methods, the estimation process is very fast, obviating the need to use clusters or expensive mainframes to obtain the quick answers needed for everyday iterative modelling. Furthermore, it can introduce the demonstrably essential concept of system noise in this branch of physiological modelling.

A general approach to the apparent permeability index

The apparent permeability index is widely used as part of a general screening process to study drug absorption, and is routinely obtained from in vitro or ex vivo experiments. A classical example, widely used in the pharmaceutical industry, is the in vitro Caco-2 cell culture model. The index is defined as the initial flux of compound through the membrane (normalized by membrane surface area and donor concentration) and is typically computed by adapting a straight line to the initial portion of the recorded amounts in the receiver compartment, possibly disregarding the first few points when lagging of the transfer process through the membrane is evident. Modeling the transfer process via a two-compartmental system yields an immediate analogue of the common Papp as the initial slope of the receiver quantity, but the two-compartment model often does not match observations well. A three-compartment model, describing the cellular layer as well as donor and receiver compartments, typically better represents the kinetics, but has the disadvantage of always having zero initial flow rate to the receiver compartment: in these circumstances the direct analogue of the Papp index is not informative since it is always zero. In the present work an alternative definition of an apparent permeability index is proposed for three-compartment models, and is shown to reduce to the classical formulation as the cellular layer’s volume tends towards zero. This new index characterizes the intrinsic permeability of the membrane to the compound under investigation, can be directly computed in a completely observer-independent fashion, and reduces to the usual Papp when the linear two-compartment representation is sufficient to accurately describe compound kinetics.

Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation

In recent years, dynamical modelling has been provided with a range of breakthrough methods to perform exact Bayesian inference. However, it is often computationally unfeasible to apply exact statistical methodologies in the context of large data sets and complex models. This paper considers a nonlinear stochastic differential equation model observed with correlated measurement errors and an application to protein folding modelling. An approximate Bayesian computation (ABC)-MCMC algorithm is suggested to allow inference for model parameters within reasonable time constraints. The ABC algorithm uses simulations of ‘subsamples’ from the assumed data-generating model as well as a so-called ‘early-rejection’ strategy to speed up computations in the ABC-MCMC sampler. Using a considerate amount of subsamples does not seem to degrade the quality of the inferential results for the considered applications. A simulation study is conducted to compare our strategy with exact Bayesian inference, the latter resulting two orders of magnitude slower than ABC-MCMC for the considered set-up. Finally, the ABC algorithm is applied to a large size protein data. The suggested methodology is fairly general and not limited to the exemplified model and data.