Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach

Background: Relapsing-remitting dynamics are a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). A clinical relapse in MS reflects an acute focal inflammatory event in the central nervous system that affects signal conduction by damaging myelinated axons. Those events are evident in T1-weighted post-contrast magnetic resonance imaging (MRI) as contrast enhancing lesions (CEL). CEL dynamics are considered unpredictable and are characterized by high intra- and inter-patient variability. Here, a population approach (nonlinear mixed-effects models) was applied to analyse of CEL progression, aiming to propose a model that adequately captures CEL dynamics. Methods and Findings: We explored several discrete distribution models to CEL counts observed in nine MS patients undergoing a monthly MRI for 48 months. All patients were enrolled in the study free of immunosuppressive drugs, except for intravenous methylprednisolone or oral prednisone taper for a clinical relapse. Analyses were performed with the nonlinear mixed-effect modelling software NONMEM 7.2. Although several models were able to adequately characterize the observed CEL dynamics, the negative binomial distribution model had the best predictive ability. Significant improvements in fitting were observed when the CEL counts from previous months were incorporated to predict the current month's CEL count. The predictive capacity of the model was validated using a second cohort of fourteen patients who underwent monthly MRIs during 6-months. This analysis also identified and quantified the effect of steroids for the relapse treatment. Conclusions: The model was able to characterize the observed relapsing-remitting CEL dynamic and to quantify the inter-patient variability. Moreover, the nature of the effect of steroid treatment suggested that this therapy helps resolve older CELs yet does not affect newly appearing active lesions in that month. This model could be used for design of future longitudinal studies and clinical trials, as well as for the evaluation of new therapies

Growth of screen-detected abdominal aortic aneurysms in men: a Bayesian analysis

There is considerable interindividual variability in the growth of abdominal aortic aneurysms (AAAs), but an individual’s growth observations, risk factors, and biomarkers could potentially be used to tailor surveillance. To assess the potential for tailoring surveillance, this study determined the accuracy of individualized predictions of AAA size at the next surveillance observation. A hierarchical Bayesian model was fitted to a total of 1,732 serial ultrasound measurements from 299 men in whom ultrasound screening identified an AAA. The data were best described by a nonlinear model with a constant first derivative of the AAA growth rate with size. The area under the receiver operating characteristic (ROC) curves for predicting whether an AAA was ≥40 or ≥50 mm at the next observation were 0.922 and 0.979, respectively, and the median root mean squared error was 2.52 mm. These values were nearly identical for models with or without plasma D-dimer effects

Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach

Background: Relapsing-remitting dynamics are a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). A clinical relapse in MS reflects an acute focal inflammatory event in the central nervous system that affects signal conduction by damaging myelinated axons. Those events are evident in T1-weighted post-contrast magnetic resonance imaging (MRI) as contrast enhancing lesions (CEL). CEL dynamics are considered unpredictable and are characterized by high intra- and inter-patient variability. Here, a population approach (nonlinear mixed-effects models) was applied to analyse of CEL progression, aiming to propose a model that adequately captures CEL dynamics. Methods and Findings: We explored several discrete distribution models to CEL counts observed in nine MS patients undergoing a monthly MRI for 48 months. All patients were enrolled in the study free of immunosuppressive drugs, except for intravenous methylprednisolone or oral prednisone taper for a clinical relapse. Analyses were performed with the nonlinear mixed-effect modelling software NONMEM 7.2. Although several models were able to adequately characterize the observed CEL dynamics, the negative binomial distribution model had the best predictive ability. Significant improvements in fitting were observed when the CEL counts from previous months were incorporated to predict the current month's CEL count. The predictive capacity of the model was validated using a second cohort of fourteen patients who underwent monthly MRIs during 6-months. This analysis also identified and quantified the effect of steroids for the relapse treatment. Conclusions: The model was able to characterize the observed relapsing-remitting CEL dynamic and to quantify the inter-patient variability. Moreover, the nature of the effect of steroid treatment suggested that this therapy helps resolve older CELs yet does not affect newly appearing active lesions in that month. This model could be used for design of future longitudinal studies and clinical trials, as well as for the evaluation of new therapies

Effect of age, weight, and CYP2C19 genotype on escitalopram exposure.

The purpose of this study was to characterize escitalopram population pharmacokinetics (PK) in patients treated for major depression in a cross-national, US-Italian clinical trial. Data from the 2 sites participating in this trial, conducted at Pittsburgh (United States) and Pisa (Italy), were used. Patients received 5, 10, 15, or 20 mg of escitalopram daily for a minimum of 32 weeks. Nonlinear mixed effects modeling was used to model the PK characteristics of escitalopram. One- and 2-compartment models with various random effect implementations were evaluated during model development. Objective function values and goodness-of-fit plots were used as model selection criteria. CYP2C19 genotype, age, weight, body mass index, sex, race, and clinical site were evaluated as possible covariates. In total, 320 plasma concentrations from 105 Pittsburgh patients and 153 plasma concentrations from 67 Pisa patients were available for the PK model development. A 1-compartmental model with linear elimination and proportional error best described the data. Apparent clearance (CL/F) and volume of distribution (V/F) for escitalopram without including any covariates in the patient population were 23.5 L/h and 884 L, respectively. CYP2C19 genotype, weight, and age had a significant effect on CL/F, and patient body mass index affected estimated V/F. Patients from Pisa, Italy, had significantly lower clearances than patients from Pittsburgh that disappeared after controlling for patient CYP2C19 genotype, age, and weight. Postprocessed individual empirical Bayes estimates on clearance for the 172 patients show that patients without allele CYP2C19(*)2 or (*)3 (n = 82) cleared escitalopram 33.7% faster than patients with heterogeneous or homogeneous (*)2 or (*)3 ((*)17/(*)2, (*)17/(*)3, (*)1/(*)2, (*)1/(*)3, (*)2/(*)2, (*)2/(*)3, and (*)3/(*)3, n = 46). CL/F significantly decreased with increasing patient age. Patients younger than 30 years (n = 45) cleared escitalopram 20.7% and 42.7% faster than patients aged 30 to 50 years (n = 84) and older than 50 years of age (n = 43), respectively. CYP2C19 genotype, age, and weight strongly influenced the CL/F of escitalopram. These variables may affect patient tolerance of this antidepressant and may provide important information in the effort to tailor treatments to patients' individual needs

Building a climate of trust during organizational change: The mediating role of justice perceptions and emotion

Over the years, research has shown that, although there are various factors which contribute to failed change, one of the key reasons people resist change is due to the inability of leaders to convince employees to support change and to commit the energy and effort necessary to implement it. Senior management can ensure an organization is change-ready by developing and maintaining a supportive culture and climate that positively influence the emotional health and welfare of employees. Despite the obvious importance of leadership to change efforts, little previous research has investigated, holistically and in the context of major change, the relationship between senior management actions and employee responses. Furthermore, the change literature largely ignores the role that emotions play in employee responses to change initiatives. This chapter addresses both areas, and develops a model of organizational change from a justice and emotions perspective, which depicts employees’ justice perceptions related to senior executives as affecting trust directly and indirectly, through associated emotional responses