Left ventricular mass index and ventricular contractility improvement in patients with severe obesity following rapid weight loss after bariatric surgery

BackgroundObesity is a well-known risk factor for heart disease, resulting in a broad spectrum of cardiovascular changes. Left ventricular mass (LVM) and contractility are recognized markers of cardiac function.ObjectivesTo determine the changes of LVM and contractility after bariatric surgery (BaS).SettingUniversity hospital, United StatesMethodsTo determine the cardiac changes in ventricular mass, ventricular contractility, and left ventricular shortening fraction (LVSF), we retrospectively reviewed the 2-dimensional echocardiographic parameters of patients with obesity who underwent BaS at our institution. We compared these results before and after BaS.ResultsA total of 40 patients met the inclusion criteria. The majority were females (57.5%; n = 23), with an average age of 63.5 ± 12.1. The excess body mass index (BMI) lost at 12 months was 48.9 ± 28.9%. The percent total weight loss after BaS was 16.46 ± 9.9%. The left ventricular mass was 234.9 ± 88.1 grams before and 181.5 ± 52.7 grams after BaS (P = .002). The LVM index was 101.3 ± 38.3 g/m² before versus 86.7 ± 26.6 g/m² after BaS (P = .005). The LVSF was 31% ± 8.8% before and 36.3% ± 8.2% after BaS (P = .007). We found a good correlation between the decrease in LVM index and the BMI after BaS (P = .03).ConclusionRapid weight loss results in a decrease of the LVM index, as well as improvement in the left ventricular muscle contractility. Our results suggest that there is left ventricular remodeling and an improvement of heart dynamics following bariatric surgery. Further studies are needed to better assess these findings.     

Predicting unconsciousness after propofol administration: qCON,BIS, and ALPHA band frequency power

During anesthesia induction with propofol the level of arousal progressively decreases until reaching loss of consciousness (LOC). In addition, there is a shift of alpha activity from parieto-occipital to frontal zones, defined as anteriorization. Whilst monitoring LOC and anteriorization would be useful to improve propofol dosage and patient safety, the current devices for anesthetic depth monitoring are unable to detect these events. The aim of this study was to observe LOC and anteriorization during anesthesia induction with propofol by applying electrodes placed in the frontal and parietal areas. Bispectral index (BIS) and quantium consciousness index (qCON) monitors were simultaneously employed. BIS? and qCON sensors were placed in the frontal and parieto-occipital regions of 10 alopecic patients who underwent anesthesia with propofol, alfentanil, and remifentanil. The initial biophase target of propofol was 2.5 mcg/mL which was gradually increased until reaching LOC. Wilcoxon signed-rank test was used to study differences in alpha power and qCON/BIS indices along the study; and Pk value to evaluate predictive capability of anteriorization of BIS, qCON, and alpha waves. Parietal BIS and qCON values became significantly higher than frontal values 15 min after loss of eye reflex. Anteriorization was observed with both monitors. Pk values for BIS and qCON were strongly predictive of frontal alpha absolute power. During anesthesia induction with propofol it is possible to identify anteriorization with BIS and qCON in the frontal and parieto-occipital regions. Both indices showed different patterns which need to be further studied.

     

Improvement of glucose metabolism following rapid weight loss after bariatric surgery and its impact on reduction of visceral abdominal fat versus free fat muscle

BackgroundBody fat distribution is highly associated with metabolic disturbances. Skeletal muscle plays an important role in glucose metabolism, as it serves as an important organ for glucose storage in the form of glycogen. In fact, low muscle mass has been associated with metabolic syndrome, type 2 diabetes (T2D), systemic inflammation, and decreased survival.ObjectivesTo compare the relationship between visceral abdominal fat (VAF) and fat free mass (FFM) with the improved glucose metabolism after bariatric surgery.SettingUniversity hospital, United States.MethodsA retrospective review was performed of all patients who underwent bariatric surgery between 2011 and 2017 at a university hospital in the United States. In severely obese patients with T2D, we measured the VAF via abdominal computed tomography scan and we calculated the FFM preoperatively and at a 12-month follow-up. Data collected included baseline demographic characteristics and perioperative parameters, such as treatment for hypertension (HTN) and T2D, body mass index (BMI), glycated hemoglobin (HbA1C), glucose, and lipid profile.ResultsA total of 25 patients met the inclusion criteria. The average age was 52.5 ± 11.6 years. The initial BMI was 41.41 ± 5.7 kg/m² and the postoperative BMI was 31.7 ± 6.9 kg/m² (P < .0001). The preoperative VAF volume was 184.6 ± 90.2 cm³ and the postoperative VAF volume was 93.8 ± 46.8 cm³ at the 12-month follow-up (P < .0001). The preoperative FFM was 55.2 ± 11.4 kg and the postoperative FFM was 49.1 ± 12 kg (P < .072). The preoperative HbA1C was 5.8% ± .9%, which decreased postoperatively to 5.3% ± .4% at the 12-month follow-up (P < .013).ConclusionBariatric surgery has been demonstrated to be an effective treatment modality for severe obesity and T2D. Our results suggest that at 12 months, there is a reduction in VAF and HbA1C without a significant loss of FFM. Further prospective studies are needed to better understand these findings.     

Drug dosage individualization based on a random-effects linear model

This article investigates drug dosage individualization when the patient population can be described with a random-effects linear model of a continuous pharmacokinetic or pharmacodynamic response. Specifically, we show through both decision-theoretic arguments and simulations that a published clinical algorithm may produce better individualized dosages than some traditional methods of therapeutic drug monitoring. Since empirical evidence suggests that the linear model may adequately describe drugs and patient populations, and linear models are easier to handle than the nonlinear models traditionally used in population pharmacokinetics, our results highlight the potential applicability of linear mixed models to dosage computations and personalized medicine.     

Estimating the size of the effects of co-medications on plasma clozapine concentrations using a model that controls for clozapine doses and confounding variables

AIM: The purpose of this study was to estimate the effect sizes of drug interactions on plasma clozapine concentrations, adjusting for potentially confounding factors such as smoking. METHODS: The estimation was performed by using a mixed model, and a combination of unpublished (N=83) and published (N=172) data that included patients taking phenobarbital, valproic acid, fluvoxamine, fluoxetine, paroxetine, sertraline, citalopram and reboxetine, and patients not taking co-medications. RESULTS: The 255 patients provided a total of 415 steady-state trough plasma clozapine concentrations. Each patient provided 1 to 15 measures of plasma clozapine concentrations. Total plasma clozapine concentration, defined as the sum of plasma clozapine and norclozapine concentrations, was also investigated. A random intercept linear model of the natural log of plasma clozapine concentration with the natural log of dose and other variables as independent variables was built. The model confirmed that phenobarbital induces clozapine metabolism (effect size, E=-28%), and that fluoxetine (E=+42%), fluvoxamine (E=+263%) and paroxetine (E=+30%) inhibit it. Valproic acid appeared to inhibit clozapine metabolism in non-smokers (effect size, E=+16%), whereas it appeared to induce clozapine metabolism in smokers (E=-22%). The effect sizes of smoking on plasma clozapine concentration were -20% in patients not taking valproic acid, and -46% in patients taking valproic acid. Thus, smoking induces clozapine metabolism, and this induction may be stronger when the patient is taking valproic acid. The effect sizes allowed the computation of clozapine dose-correction factors for phenobarbital, 1.4 [95% confidence interval, CI, (1.1, 1.7)]; paroxetine, 0.77 (0.67, 0.89); fluoxetine, 0.70 (0.64, 0.78); fluvoxamine, 0.28 (0.22, 0.35); and valproic acid [0.86 (0.75, 1.0) in non-smokers, and 1.3 (0.96, 1.73) in smokers]. Sertraline, reboxetine and citalopram had no obvious effects. DISCUSSION: The results for total plasma clozapine concentrations are similar to those for plasma clozapine concentrations. The main limitations of this study were that the computed effect sizes reflect only the doses and treatment-durations of the co-medications studied, and that the substantial "noise" of the clinical environment may make it difficult to detect the effects of some variables, particularly those with small effect sizes. Gender was not significant probably due to its relatively small effect size in the studied population, and age was not significant probably due to the limited age variability. CONCLUSION: This article contributes to the clozapine literature by describing a possible interaction between taking valproic acid and smoking, which modifies plasma clozapine concentrations, by estimating the effect sizes of other compounds on plasma clozapine concentrations after correcting for confounders, and by providing dose-correction factors for clinicians.     

Percutaneous Retrieval of a Migrated Hepatogastrostomy Stent

CardioVascular and Interventional Radiology -     

Drug Dosage Individualization Based on a Random-Effects Linear Model

This article investigates drug dosage individualization when the patient population can be described with a random-effects linear model of a continuous pharmacokinetic or pharmacodynamic response. Specifically, we show through both decision-theoretic arguments and simulations that a published clinical algorithm may produce better individualized dosages than some traditional methods of therapeutic drug monitoring. Since empirical evidence suggests that the linear model may adequately describe drugs and patient populations, and linear models are easier to handle than the nonlinear models traditionally used in population pharmacokinetics, our results highlight the potential applicability of linear mixed models to dosage computations and personalized medicine.     

A new integrated system combining atomic force microscopy and micro-electrode array for measuring the mechanical properties of living cardiac myocytes

In this paper we present a new experimental set-up which combines the surface characterization capabilities of atomic force microscopy at the sub-micrometer scale with non-invasive electrophysiological measurements obtained by using planar micro-electrode arrays. In order to show the potential of the combined measurements we studied the changes in cell topography and elastic properties of cardiac muscle cells as during the contraction-relaxation cycle. The onset of each beating cycle was precisely identified by the use of the extracellular potential signal, allowing us to combine nanomechanical measurements from multiple cardiomyocyte contractions in order to analyze the time-dependent variation of cell morphology and elasticity. Moreover, by estimating the elastic modulus at different indentation depths in a single location on the cell membrane, we observed a dynamic mechanical behavior that could be related to the underlying myofibrillar structure dynamics.     

Estimating the effects of co-medications on plasma olanzapine concentrations by using a mixed model

The purpose of this study was to estimate the effect sizes of drug interactions on plasma olanzapine concentrations while adjusting for potentially confounding factors such as smoking. The estimation was performed by using a mixed model, data from a series of previously published studies of lamotrigine, oxcarbazepine, topiramate, and mirtazapine, and unpublished data from patients under clinical therapeutic drug monitoring (TDM). The total sample included 163 adult patients (age>or=18 years) who provided both steady-state plasma olanzapine concentrations and smoking information. They provided a total of 360 olanzapine concentrations (1 to 11 measures per patient). Smoking and concomitant carbamazepine or lamotrigine use were found to have significant effects on median plasma olanzapine concentrations. The effects of lamotrigine on plasma olanzapine concentrations were modified by smoking. After adjusting for olanzapine dose and carbamazepine intake, plasma olanzapine concentrations were 10% lower in non-smokers who were taking lamotrigine than in non-smokers who were not taking lamotrigine; olanzapine concentrations were 35% higher in smokers who were taking lamotrigine than in smokers who were not taking lamotrigine; olanzapine concentrations were 41% lower in smokers who were not taking lamotrigine than in non-smokers who were not taking lamotrigine; and olanzapine concentrations were 11% lower in smokers who were taking lamotrigine than in non-smokers who were taking lamotrigine. After adjusting for olanzapine dose and taking carbamazepine, the correction factor comparing smokers taking lamotrigine versus non-smokers who were not taking lamotrigine was 1.3. Gender, age, and concomitant use of mirtazapine, valproic acid, lamotrigine, topiramate, lorazepam, citalopram or oxcarbazepine did not have significant effects on olanzapine concentrations. The main limitation of this clinical design is the unavoidable substantial "noise" that characterizes (uncontrolled) clinical environments, which may make it difficult to detect the effects of some variables. Other limitations were the small sample size of some drug sub-samples and the lack of testing for plasma olanzapine metabolites.