Efficacy of a loading dose of IV salbutamol in children with severe acute asthma admitted to a PICU:a randomized controlled trial

The optimal dose regimen for intravenous (IV) treatment in children with severe acute asthma (SAA) is still a matter of debate. We assessed the efficacy of adding a salbutamol loading dose to continuous infusion with salbutamol in children admitted to a pediatric intensive care unit (PICU) with SAA. This multicentre, placebo-controlled randomized trial in the PICUs of four tertiary care children’s hospitals included children (2–18 years) with SAA admitted between 2017 and 2019. Children were randomized to receive either a loading dose IV salbutamol (15 mcg/kg, max. 750 mcg) or normal saline while on continuous salbutamol infusion. The primary outcome was the asthma score (Qureshi) 1 h after the intervention. Analysis of covariance models was used to evaluate sensitivity to change in asthma scores. Serum concentrations of salbutamol were obtained. Fifty-eight children were included (29 in the intervention group). Median baseline asthma score was 12 (IQR 10–13) in the intervention group and 11 (9–12) in the control group (p = 0.032). The asthma score 1 h after the intervention did not differ significantly between the groups (p = 0.508, β-coefficient = 0.283). The median increase in salbutamol plasma levels 10 min after the intervention was 13 μg/L (IQR 5–24) in the intervention group and 4 μg/L (IQR 0–7) in the control group (p = 0.001). Side effects were comparable between both groups. Conclusion: We found no clinical benefit of adding a loading dose IV salbutamol to continuous infusion of salbutamol, in children admitted to the PICU with SAA. Clinically significant side effects from the loading dose were not encountered.What is Known:• Pediatric asthma guidelines struggle with an evidence-based approach for the treatment of SAA beyond the initial steps of oxygen suppletion, repetitive administration of inhaled β2-agonists, and systemic steroids.• During an SAA episode, effective delivery of inhaled drugs is unpredictable due to severe airway obstruction.What is New:• This study found no beneficial effect of an additional loading dose IV salbutamol in children admitted to the PICU.• This study found no clinically significant side effects from the loading dose

A Decade of Experience With Alemtuzumab Therapy for Severe or Glucocorticoid-Resistant Kidney Transplant Rejection

Alemtuzumab is used as lymphocyte-depleting therapy for severe or glucocorticoid-resistant kidney transplant rejection. However, the long-term efficacy and toxicity of alemtuzumab therapy are unclear. Therefore, all cases of alemtuzumab anti-rejection therapy between 2012 and 2022 in our institution were investigated. Graft survival, graft function, lymphocyte depletion, serious infections, malignancies, and patient survival were analyzed and compared with a reference cohort of transplanted patients who did not require alemtuzumab anti-rejection therapy. A total of 225 patients treated with alemtuzumab were identified and compared with a reference cohort of 1,668 patients. Over 60% of grafts was salvaged with alemtuzumab therapy, but graft survival was significantly poorer compared to the reference cohort. The median time of profound T- and B lymphocyte depletion was 272 and 344 days, respectively. Serious infection rate after alemtuzumab therapy was 54.1/100 person-years. The risk of death (hazard ratio 1.75, 95%-CI 1.28–2.39) and infection-related death (hazard ratio 2.36, 95%-CI 1.35–4.11) were higher in the alemtuzumab-treated cohort. In conclusion, alemtuzumab is an effective treatment for severe kidney transplant rejection, but causes long-lasting lymphocyte depletion and is associated with frequent infections and worse patient survival outcomes.</p

Population Pharmacokinetics of Imipenem in Critically Ill Patients: A Parametric and Nonparametric Model Converge on CKD-EPI Estimated Glomerular Filtration Rate as an Impactful Covariate

Background: Population pharmacokinetic (popPK) models for antibiotics are used to improve dosing strategies and individualize dosing by therapeutic drug monitoring. Little is known about the differences in results of parametric versus nonparametric popPK models and their potential consequences in clinical practice. We developed both parametric and nonparametric models of imipenem using data from critically ill patients and compared their results. Methods: Twenty-six critically ill patients treated with intravenous imipenem/cilastatin were included in this study. Median estimated glomerular filtration rate (eGFR) measured by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was 116 mL/min/1.73 m2 (interquartile range 104–124) at inclusion. The usual dosing regimen was 500 mg/500 mg four times daily. On average, five imipenem levels per patient (138 levels in total) were drawn as peak, intermediate, and trough levels. Imipenem concentration-time profiles were analyzed using parametric (NONMEM 7.2) and nonparametric (Pmetrics 1.5.2) popPK software. Results: For both methods, data were best described by a model with two distribution compartments and the CKD-EPI eGFR equation unadjusted for body surface area as a covariate on the elimination rate constant (Ke). The parametric population parameter estimates were Ke 0.637 h−1 (between-subject variability [BSV]: 19.0% coefficient of variation [CV]) and central distribution volume (Vc) 29.6 L (without BSV). The nonparametric values were Ke 0.681 h−1 (34.0% CV) and Vc 31.1 L (42.6% CV). Conclusions: Both models described imipenem popPK well; the parameter estimates were comparable and the included covariate was identical. However, estimated BSV was higher in the nonparametric model. This may have consequences for estimated exposure during dosing simulations and should be further investigated in simulation studies

Cross-disorder and disorder-specific deficits in social functioning among schizophrenia and Alzheimer's disease patients

BACKGROUND: Social functioning is often impaired in schizophrenia (SZ) and Alzheimer's disease (AD). However, commonalities and differences in social dysfunction among these patient groups remain elusive.MATERIALS AND METHODS: Using data from the PRISM study, behavioral (all subscales and total score of the Social Functioning Scale) and affective (perceived social disability and loneliness) indicators of social functioning were measured in patients with SZ (N = 56), probable AD (N = 50) and age-matched healthy controls groups (HC, N = 29 and N = 28). We examined to what extent social functioning differed between disease and age-matched HC groups, as well as between patient groups. Furthermore, we examined how severity of disease and mood were correlated with social functioning, irrespective of diagnosis.RESULTS: As compared to HC, both behavioral and affective social functioning seemed impaired in SZ patients (Cohen's d's 0.81-1.69), whereas AD patients mainly showed impaired behavioral social function (Cohen's d's 0.65-1.14). While behavioral indices of social functioning were similar across patient groups, SZ patients reported more perceived social disability than AD patients (Cohen's d's 0.65). Across patient groups, positive mood, lower depression and anxiety levels were strong determinants of better social functioning (p's &lt;0.001), even more so than severity of disease.CONCLUSIONS: AD and SZ patients both exhibit poor social functioning in comparison to age- and sex matched HC participants. Social dysfunction in SZ patients may be more severe than in AD patients, though this may be due to underreporting by AD patients. Across patients, social functioning appeared as more influenced by mood states than by severity of disease.</p

Pharmacokinetic role of protein binding of mycophenolic acid and its glucuronide metabolite in renal transplant recipients

Mycophenolic acid (MPA), the active compound of mycophenolate mofetil (MMF), is used to prevent graft rejection in renal transplant recipients. MPA is glucuronidated to the metabolite MPAG, which exhibits enterohepatic recirculation (EHC). MPA binds for 97% and MPAG binds for 82% to plasma proteins. Low plasma albumin concentrations, impaired renal function and coadministration of cyclosporine have been reported to be associated with increased clearance of MPA. The aim of the study was to develop a population pharmacokinetic model describing the relationship between MMF dose and total MPA (tMPA), unbound MPA (fMPA), total MPAG (tMPAG) and unbound MPAG (fMPAG). In this model the correlation between pharmacokinetic parameters and renal function, plasma albumin concentrations and cotreatment with cyclosporine was quantified. tMPA, fMPA, tMPAG and fMPAG concentration–time profiles of renal transplant recipients cotreated with cyclosporine (n = 48) and tacrolimus (n = 45) were analyzed using NONMEM. A 2- and 1-compartment model were used to describe the pharmacokinetics of fMPA and fMPAG. The central compartments of fMPA and fMPAG were connected with an albumin compartment allowing competitive binding (bMPA and bMPAG). tMPA and tMPAG were modeled as the sum of the bound and unbound concentrations. EHC was modeled by transport of fMPAG to a separate gallbladder compartment. This transport was decreased in case of cyclosporine cotreatment (P < 0.001). In the model, clearance of fMPAG decreased when creatinine clearance (CrCL) was reduced (P < 0.001), and albumin concentration was correlated with the maximum number of binding sites available for MPA and MPAG (P < 0.001). In patients with impaired renal function cotreated with cyclosporine the model adequately described that increasing fMPAG concentrations decreased tMPA AUC due to displacement of MPA from its binding sites. The accumulated MPAG could also be reconverted to MPA by the EHC, which caused increased tMPA AUC in patients cotreated with tacrolimus. Changes in CrCL had hardly any effect on fMPA exposure. A decrease in plasma albumin concentration from 0.6 to 0.4 mmol/l resulted in ca. 38% reduction of tMPA AUC, whereas no reduction in fMPA AUC was seen. In conclusion, a pharmacokinetic model has been developed which describes the relationship between dose and both total and free MPA exposure. The model adequately describes the influence of renal function, plasma albumin and cyclosporine co-medication on MPA exposure. Changes in protein binding due to altered renal function or plasma albumin concentrations influence tMPA exposure, whereas fMPA exposure is hardly affected

Relationships between social withdrawal and facial emotion recognition in neuropsychiatric disorders

Background: Emotion recognition constitutes a pivotal process of social cognition. It involves decoding social cues (e.g., facial expressions) to maximise social adjustment. Current theoretical models posit the relationship between social withdrawal factors (social disengagement, lack of social interactions and loneliness) and emotion decoding. Objective: To investigate the role of social withdrawal in patients with schizophrenia (SZ) or probable Alzheimer's disease (AD), neuropsychiatric conditions associated with social dysfunction. Methods: A sample of 156 participants was recruited: schizophrenia patients (SZ; n = 53), Alzheimer's disease patients (AD; n = 46), and two age-matched control groups (SZc, n = 29; ADc, n = 28). All participants provided self-report measures of loneliness and social functioning, and completed a facial emotion detection task. Results: Neuropsychiatric patients (both groups) showed poorer performance in detecting both positive and negative emotions compared with their healthy counterparts (p < .01). Social withdrawal was associated with higher accuracy in negative emotion detection, across all groups. Additionally, neuropsychiatric patients with higher social withdrawal showed lower positive emotion misclassification. Conclusions: Our findings help to detail the similarities and differences in social function and facial emotion recognition in two disorders rarely studied in parallel, AD and SZ. Transdiagnostic patterns in these results suggest that social withdrawal is associated with heightened sensitivity to negative emotion expressions, potentially reflecting hypervigilance to social threat. Across the neuropsychiatric groups specifically, this hypervigilance associated with social withdrawal extended to positive emotion expressions, an emotional-cognitive bias that may impact social functioning in people with severe mental illness

Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019: A Systematic Analysis for the Global Burden of Disease Study 2019.

The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.Funding/Support: The Institute for Health Metrics and Evaluation received funding from the Bill & Melinda Gates Foundation and the American Lebanese Syrian Associated Charities. Dr Aljunid acknowledges the Department of Health Policy and Management of Kuwait University and the International Centre for Casemix and Clinical Coding, National University of Malaysia for the approval and support to participate in this research project. Dr Bhaskar acknowledges institutional support from the NSW Ministry of Health and NSW Health Pathology. Dr Bärnighausen was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, which is funded by the German Federal Ministry of Education and Research. Dr Braithwaite acknowledges funding from the National Institutes of Health/ National Cancer Institute. Dr Conde acknowledges financial support from the European Research Council ERC Starting Grant agreement No 848325. Dr Costa acknowledges her grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Fundação para a Ciência e Tecnologia, IP under the Norma Transitória grant DL57/2016/CP1334/CT0006. Dr Ghith acknowledges support from a grant from Novo Nordisk Foundation (NNF16OC0021856). Dr Glasbey is supported by a National Institute of Health Research Doctoral Research Fellowship. Dr Vivek Kumar Gupta acknowledges funding support from National Health and Medical Research Council Australia. Dr Haque thanks Jazan University, Saudi Arabia for providing access to the Saudi Digital Library for this research study. Drs Herteliu, Pana, and Ausloos are partially supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. Dr Hugo received support from the Higher Education Improvement Coordination of the Brazilian Ministry of Education for a sabbatical period at the Institute for Health Metrics and Evaluation, between September 2019 and August 2020. Dr Sheikh Mohammed Shariful Islam acknowledges funding by a National Heart Foundation of Australia Fellowship and National Health and Medical Research Council Emerging Leadership Fellowship. Dr Jakovljevic acknowledges support through grant OI 175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. Dr Katikireddi acknowledges funding from a NHS Research Scotland Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and the Scottish Government Chief Scientist Office (SPHSU17). Dr Md Nuruzzaman Khan acknowledges the support of Jatiya Kabi Kazi Nazrul Islam University, Bangladesh. Dr Yun Jin Kim was supported by the Research Management Centre, Xiamen University Malaysia (XMUMRF/2020-C6/ITCM/0004). Dr Koulmane Laxminarayana acknowledges institutional support from Manipal Academy of Higher Education. Dr Landires is a member of the Sistema Nacional de Investigación, which is supported by Panama’s Secretaría Nacional de Ciencia, Tecnología e Innovación. Dr Loureiro was supported by national funds through Fundação para a Ciência e Tecnologia under the Scientific Employment Stimulus–Institutional Call (CEECINST/00049/2018). Dr Molokhia is supported by the National Institute for Health Research Biomedical Research Center at Guy’s and St Thomas’ National Health Service Foundation Trust and King’s College London. Dr Moosavi appreciates NIGEB's support. Dr Pati acknowledges support from the SIAN Institute, Association for Biodiversity Conservation & Research. Dr Rakovac acknowledges a grant from the government of the Russian Federation in the context of World Health Organization Noncommunicable Diseases Office. Dr Samy was supported by a fellowship from the Egyptian Fulbright Mission Program. Dr Sheikh acknowledges support from Health Data Research UK. Drs Adithi Shetty and Unnikrishnan acknowledge support given by Kasturba Medical College, Mangalore, Manipal Academy of Higher Education. Dr Pavanchand H. Shetty acknowledges Manipal Academy of Higher Education for their research support. Dr Diego Augusto Santos Silva was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil Finance Code 001 and is supported in part by CNPq (302028/2018-8). Dr Zhu acknowledges the Cancer Prevention and Research Institute of Texas grant RP210042

Limited sampling strategies for therapeutic drug monitoring of mycophenolate mofetil therapy in patients with autoimmune disease

Mycophenolate mofetil (MMF) is increasingly used for the treatment of autoimmune diseases (AID). In renal transplant recipients, it has been demonstrated that adjustment of the MMF dose according to the area under the plasma concentration versus time curve (AUC) of mycophenolic acid (MPA), the active moiety of MMF, improves clinical outcome. The aim of this study was to develop a maximum a posteriori Bayesian estimator (MAP-BE) to estimate MPA AUC(0-12) in patients with AID using a limited number of samples. The predictive performance of the MAP-BE was compared with a multiple linear regression method. Full MPA concentration versus time curves were available from 38 patients with AID treated with MMF. Nonlinear mixed-effect modeling was used to develop a population pharmacokinetic model. Patients were divided in an index and a validation data set. The pharmacokinetic model derived from the index data set was used to develop several MAP-BEs. The Bayesian estimators were used to predict AUC(0-12) in the validation data set on the basis of a limited number of blood samples. The bias and precision of these predictions were compared with those of limited sampling strategies developed with multiple linear regression. The absorption of MPA was described with 2 first-order processes with a short and a long lag time and a subsequent first-order elimination. The 2-compartment model accounted for the enterohepatic recirculation of MPA as well. Using 1-4 samples, MPA AUC(0-12) was adequately estimated by the MAP-BE. Bias (-5.5%) was not significantly different from zero, and precision was below 27%. The predictive performance of the multiple linear regression method was comparable. In conclusion, MAP-BEs were developed for the estimation of MPA AUC(0-12) in patients with AID. The predictive performance was good and comparable to those of the multiple linear regression method. Due to its flexibility with respect to sample times, the MAP-BE may be preferred over the multiple linear regression metho