Search

Pharmacometric analyses of time series viral load data may detect drug effects with greater power than approaches using single time points. Because SARS-CoV-2 viral load rapidly rises and then falls, viral dynamic models have been used. We compared different modelling approaches when analysing Phase II-type viral dynamic data. Using two SARS-CoV-2 datasets of viral load starting within 7 days of symptoms, we fitted the slope-intercept exponential decay (SI), reduced target cell limited (rTCL), target cell limited (TCL) and TCL with eclipse phase (TCLE) models using nlmixr. Model performance was assessed via Bayesian information criterion (BIC), visual predictive checks (VPCs), goodness-of-fit plots, and parameter precision. The most complex (TCLE) model had the highest BIC for both datasets. The estimated viral decline rate was similar for all models except the TCL model for dataset A with a higher rate [median (range) day-1 : dataset A; 0.63 (0.56 - 1.84); dataset B: 0.81 (0.74-0.85)]. Our findings suggest simple models should be considered during pharmacodynamic model development.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) viral loads change rapidly following symptom onset, so to assess antivirals it is important to understand the natural history and patient factors influencing this. We undertook an individual patient-level meta-analysis of SARS-CoV-2 viral dynamics in humans to describe viral dynamics and estimate the effects of antivirals used to date. This systematic review identified case reports, case series, and clinical trial data from publications between January 1, 2020, and May 31, 2020, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A multivariable Cox proportional hazards (Cox-PH) regression model of time to viral clearance was fitted to respiratory and stool samples. A simplified four parameter nonlinear mixed-effects (NLME) model was fitted to viral load trajectories in all sampling sites and covariate modeling of respiratory viral dynamics was performed to quantify time-dependent drug effects. Patient-level data from 645 individuals (age 1 month to 100 years) with 6,316 viral loads were extracted. Model-based simulations of viral load trajectories in samples from the upper and lower respiratory tract, stool, blood, urine, ocular secretions, and breast milk were generated. Cox-PH modeling showed longer time to viral clearance in older patients, men, and those with more severe disease. Remdesivir was associated with faster viral clearance (adjusted hazard ratio (AHR) = 9.19, P

Introduction: Sepsis from bacterial infection remains a significant cause of morbidity and mortality. Antibiotic use continues to increase in the community and secondary care. This is driven by the potential benefits to the individual patient of a course of antibiotics. Far less attention is given to the potential adverse effects of antibiotic use in our view. These costs may be significant to both the individual and society.\ud \ud Areas covered: We review the evidence underpinning the costs and benefits of antibiotic use. We also discuss strategies to personalize medicine in this area that maximize the benefit to cost ratio for patients and society.\ud \ud Expert opinion: The body’s innate immune response to infection is similar to that of other inflammatory insults. Our view is as clinicians we need to differentiate these responses and hence require an accurate method to determine a diagnosis of a bacterial infection and monitor illness severity. Without this, clinicians will continue to prescribe significant volumes of unnecessary antibiotics in cases of non-bacterial inflammatory states.

BackgroundThe pharmacokinetics of β-lactam antibiotics in critical illness remain poorly characterized, particularly in neonates, children and the elderly. We undertook a pharmacokinetic study of commonly used β-lactam antibiotics in critically ill patients of all ages. The aims were to produce a whole-life β-lactam pharmacokinetic model and describe the extent to which standard doses achieve pharmacokinetic/pharmacodynamic targets associated with clinical cure.Patients and methodsA total of 212 critically ill participants with an age range from 1 day (gestational age 24 weeks) to 90 years were recruited from a UK hospital, providing 1339 pharmacokinetic samples. Population pharmacokinetic analysis was undertaken using non-linear mixed-effects modelling (NONMEM) for each drug. Pooled data were used to estimate maturation and decline of β-lactam pharmacokinetics throughout life.ResultsPharmacokinetic models for eight drugs were described, including what is thought to be the first benzylpenicillin model in critically ill adults. We estimate that 50% of adult β-lactam clearance is achieved by 43 weeks post-menstrual age (chronological plus gestational age). Fifty percent of decline from peak adult clearance occurs by 71 years. Paediatric participants were significantly less likely than adults to achieve pharmacokinetic/pharmacodynamic targets with standard antibiotic doses (P ConclusionsWe believe this to be the first prospective whole-life antibiotic pharmacokinetic study in the critically ill. The study provides further evidence that standard antibiotic doses fail to achieve pharmacokinetic/pharmacodynamic targets associated with clinical success in adults, children and neonates. Maturation and decline parameters estimated from this study could be adopted as a standard for future prospective studies.

Introduction The age of patients admitted into critical care in the UK is increasing. Clinical decisions for very-old patients, usually defined as over 80, can be challenging. Clinicians are frequently asked to predict outcomes as part of discussions around the pros and cons of an intensive care unit (ICU) admission. Measures of overall health in old age, such as the clinical frailty scale (CFS), are increasingly used to help guide these discussions. We aimed to understand the characteristics of the very-old critically unwell population in the UK and the associations between frailty and outcome of an ICU admission in our healthcare system (National Health Service, NHS). Methods Baseline characteristics, ICU interventions and outcomes (ICU- and 30-day mortality) were recorded for sequential admissions of very old patients to UK ICUs as part of the European VIP 1 and 2 studies. Patient characteristics, interventions and outcome measures were compared by frailty group using standard statistical tests. Multivariable logistic regression modelling was undertaken to test association between baseline characteristics, admission type and outcome. Results 1858 participants were enrolled from 95 ICUs in the UK. The median age was 83. The median CFS was 4 (IQR 3–5). 30-day survival was significantly lower in the frail group (CFS > 4, 58%) compared to vulnerable (CFS = 4, 65%) and fit (CFS < 4 68%, p = .004). Sequential organ failure assessment (SOFA) score, reason for admission and CFS were all independently associated with increased 30-day mortality ( p < .01). Conclusion In the UK, frailty is associated with an increase in mortality at 30-days following an ICU admission. At moderate frailty (CFS 5–6), three out of every five patients admitted survived to 30-days. This is a similar mortality to septic shock.

Supplemental Material, sj-pdf-1-inc-10.1177_17511437211050789 for The clinical frailty scale ��� does it predict outcome of the very-old in UK ICUs? by Dagan O Lonsdale, Liting Tong, Helen Farrah, Sarah Farnell-Ward, Chris Ryan, Ximena Watson, Maurizio Cecconi, Hans Flaatten, Jesper Fj��lner, Christian Jung, Bertrand Guidet, Dylan de Lange, Wojciech Szczeklik, Johanna M Muessig and Susannah K Leaver in Journal of the Intensive Care Society

Purpose of Review Sepsis is a leading cause of death worldwide. Groundbreaking international collaborative efforts have culminated in the widely accepted surviving sepsis guidelines, with iterative improvements in management strategies and definitions providing important advances in care for patients. Key to the diagnosis of sepsis is identification of infection, and whilst the diagnostic criteria for sepsis is now clear, the diagnosis of infection remains a challenge and there is often discordance between clinician assessments for infection. Recent Findings We review the utility of common biochemical, microbiological and radiological tools employed by clinicians to diagnose infection and explore the difficulty of making a diagnosis of infection in severe inflammatory states through illustrative case reports. Finally, we discuss some of the novel and emerging approaches in diagnosis of infection and sepsis. Summary While prompt diagnosis and treatment of sepsis is essential to improve outcomes in sepsis, there remains no single tool to reliably identify or exclude infection. This contributes to unnecessary antimicrobial use that is harmful to individuals and populations. There is therefore a pressing need for novel solutions. Machine learning approaches using multiple diagnostic and clinical inputs may offer a potential solution but as yet these approaches remain experimental.

Dosing of amoxicillin-clavulanic acid in critical illness is difficult as β-lactam pharmacokinetics are altered by physiological changes and therapies initiated in the intensive care unit such as renal replacement therapy (RRT). Successful treatment relies on sustaining a free antibiotic concentration above the minimum inhibitory concentration of the target pathogen (fT>MIC). We present a case of a patient treated with amoxicillin-clavulanic acid (1.2 g for 8 h) for an aspiration pneumonia. Dosing in this case was complicated by the necessity for RRT to treat a drug overdose with carbamazepine, despite normal native renal function. Antibiotic concentrations taken at steady state revealed a clearance of 14.6 L/h and a low fT>MIC (

PURPOSE: Traditional critical care dogma regarding the benefits of early tracheostomy during invasive ventilation has had to be revisited due to the risk of COVID-19 to patients and healthcare staff. Standard practises that have evolved to minimise the risks associated with tracheostomy must be comprehensively reviewed in light of the numerous potential episodes for aerosol generating procedures. We meet the urgent need for safe practise standards by presenting the experience of two major London teaching hospitals, and synthesise our findings into an evidence-based guideline for multidisciplinary care of the tracheostomy patient. METHODS: This is a narrative review presenting the extensive experience of over 120 patients with tracheostomy, with a pragmatic analysis of currently available evidence for safe tracheostomy care in COVID-19 patients. RESULTS: Tracheostomy care involves many potentially aerosol generating procedures which may pose a risk of viral transmission to staff and patients. We make a series of recommendations to ameliorate this risk through infection control strategies, equipment modification, and individualised decannulation protocols. In addition, we discuss the multidisciplinary collaboration that is absolutely fundamental to safe and effective practise. CONCLUSION: COVID-19 requires a radical rethink of many tenets of tracheostomy care, and controversy continues to exist regarding the optimal techniques to minimise risk to patients and healthcare workers. Safe practise requires a coordinated multidisciplinary team approach to infection control, weaning and decannulation, with integrated processes for continuous prospective data collection and audit.

Sepsis secondary to bacterial infection remains a significant cause of morbidity and mortality globally. Recent decades have seen the evolution of international collaborations to improve care for these patients and identify areas for research. In this article we discuss the pathophysiology underlying the condition, review the current recommended management strategies, discuss areas of controversy, and highlight the need for ongoing research, particularly in diagnostics.

A core premise of attachment theory is that the quality of early care shapes the development and consolidation of later constructed mental representations, which in turn influence mental health (Bowlby, 1973, 1980; Sroufe et al., 2005). Following this premise, this set of pre-registered analyses aims to build on a programmatic line of research (Dagan et al., 2018; Dagan et al., 2020) to investigate the predictive significance of attachment representations in general, and scripted attachment representations (i.e., secure base script knowledge) more specifically, on depressive symptoms in adulthood.

Introduction: COVID-19 arrived as a new disease entity to the UK in early 2020. As one of the tertiary hospitals to admit COVID-19 patients, we collected data in order to characterise patients Method: Data was collected on 771 patients admitted with confirmed COVID-19 during the first 3 months of the pandemic, who had already been discharged or died. Presenting symptoms, co-morbidities, frailty, observations, ethnicity and mortality were noted. Results: The median age was 72 with a median clinical frailty score (CFS) and Charlson co-morbidity index (CCI) of 4. The most frequently reported symptoms were fever(62%), cough(60%) and breathlessness (57%) but older patients had more presentations with falls (34%) and confusion (37%). Older patients presented earlier compared with younger patients (2 vs 7 days). Patients with a CCI of 0 had a median age of 38 and frailty score of 2, whilst patients with CCI ≥5 had a median age of 82 and frailty score of 6. The most common co-morbidities were hypertension(42%), diabetes mellitus (31%)and chronic respiratory disease(16%). Data on ethnicity and mortality is undergoing further analysis Conclusion: This is a large single site cohort of COVID-19 patients. Older patients presented sooner after symptom onset. They were more likely to present with falls and confusion and have a higher frailty score and co-morbidity index. Further multivariate analysis is ongoing to examine additional variables.

SUMMARY Background SARS-CoV-2 viral loads change rapidly following symptom onset so to assess antivirals it is important to understand the natural history and patient factors influencing this. We undertook an individual patient-level meta-analysis of SARS-CoV-2 viral dynamics in humans to describe viral dynamics and estimate the effects of antivirals used to-date. Methods This systematic review identified case reports, case series and clinical trial data from publications between 1/1/2020 and 31/5/2020 following PRISMA guidelines. A multivariable Cox proportional hazards regression model (Cox-PH) of time to viral clearance was fitted to respiratory and stool samples. A simplified four parameter nonlinear mixed-effects (NLME) model was fitted to viral load trajectories in all sampling sites and covariate modelling of respiratory viral dynamics was performed to quantify time dependent drug effects. Findings Patient-level data from 645 individuals (age 1 month-100 years) with 6316 viral loads were extracted. Model-based simulations of viral load trajectories in samples from the upper and lower respiratory tract, stool, blood, urine, ocular secretions and breast milk were generated. Cox-PH modelling showed longer time to viral clearance in older patients, males and those with more severe disease. Remdesivir was associated with faster viral clearance (adjusted hazard ratio (AHR) = 9·19, p Interpretation Combination therapy including interferons should be further investigated. A NLME model for designing and analysing viral pharmacodynamics in trials has been established. RESEARCH IN CONTEXT Evidence before this study We performed a systematic literature search for studies reporting serial viral load measurements in humans for SARS-CoV-2 in MEDLINE and Embase, MedRxiv and BioRxiv along with the PROSPERO systematic review register. As of 27/5/20, no previous or ongoing review has systematically searched for and pooled individual patient-level serial viral load data in this disease or depicted SARS-CoV-2 viral load trajectories in a meta-analysis. In addition, modelling of SARS-CoV-2 viral dynamics and model-based quantification and simulation of antiviral drug activity is to date only available from single studies. A comprehensive dataset for distinguishing between natural history of the disease and drug effect related viral load decline was therefore lacking. Added value of this study This individual patient meta-analysis includes a large dataset of individual patient serial viral load measurements for SARS-CoV-2 with and without drug therapy. The full dataset contains 645 individuals with 6316 viral load samples. The majority were sampled from the upper respiratory tract. There were also samples available from the lower respiratory tract, blood, urine and stool, breast milk and ocular secretions. This enabled us to estimate parameters of a nonlinear mixed effects (NLME) viral dynamics model to characterise the course of viral shedding across each sample site. A multivariable Cox proportional hazards regression analysis revealed increasing age, disease severity and male sex to be associated with longer time to viral clearance, indicating that patients with worse clinical outcomes do have higher viral loads. A NLME model incorporating drug effects showed early initiation of antivirals shortened time to viral clearance. By including data on multiple drug combinations Cox proportional hazards suggests type I interferons plus ribavirin to be potentially synergistic in vivo. Our NLME model can be used to determine the sample size of future Phase II trials. For example, to detect a significant difference in proportion of patients clearing the virus after 7 days of treatment in patients starting therapy at day 3 post symptom onset with 90% power, one would require 606, 284, 90 or 31 patients per group for lopinavir/ritonavir, ribavirin, interferon or interferon plus ribavirin drug effects respectively. The later that antivirals are initiated, the smaller the drug effect and hence the larger sample size required to determine antiviral activity. Implications of all the available evidence This individual patient level meta-analysis provides insight into the natural history of the SARS-CoV-2 viral dynamics through the biggest serial viral load data set available to date. A NLME viral dynamics model was established enabling estimation of the impact of drug effects on viral clearance. The full data set and model code is available to as a tool to design and analyse Phase II antiviral trials.

Variation in response to biologic therapy for inflammatory diseases, such as psoriasis, is partly driven by variation in drug exposure. Real‐world psoriasis data were used to develop a pharmacokinetic/pharmacodynamic (PK/PD) model for the first‐line therapeutic antibody ustekinumab. The impact of differing dosing strategies on response was explored. Data were collected from a UK prospective multicenter observational cohort (491 patients on ustekinumab monotherapy, drug levels, and anti‐drug antibody measurements on 797 serum samples, 1,590 measurements of Psoriasis Area Severity Index (PASI)). Ustekinumab PKs were described with a linear one‐compartment model. A maximum effect (Emax) model inhibited progression of psoriatic skin lesions in the turnover PD mechanism describing PASI evolution while on treatment. A mixture model on half‐maximal effective concentration identified a potential nonresponder group, with simulations suggesting that, in future, the model could be incorporated into a Bayesian therapeutic drug monitoring “dashboard” to individualize dosing and improve treatment outcomes.

Treating infection is a key part of the work of most clinicians. Whilst new drug technologies like biologics have begun a revolution in the treatment of cancer and autoimmune disease, there has been a conspicuous absence of new classes of antibiotic over the last 30 years. [...]

AIMS: Prescribing is a complex skill required of doctors and, increasingly, other healthcare professionals. Use of a personal formulary can help to develop this skill. In 2006–9, we developed a core list of the 100 most commonly prescribed drugs. Our aim in the present study was to update this ‘starter formulary’ to ensure its continued relevance for prescriber training. METHODS: We analysed large contemporary primary and secondary care datasets to identify the most frequently prescribed medicinal products. Items were classified into natural groups, broadly following their British National Formulary classification. The resulting drug groups were included in the core list if they comprised ≥0.1% prescriptions in both settings or ≥0.2–0.3% prescriptions in one setting. Drugs from emergency guidelines that did not qualify by prescribing frequency completed the list. RESULTS: Over 1 billion primary care items and approximately 1.8 million secondary care prescriptions were analysed. The updated list comprises 81 drug groups commonly prescribed in both settings; six from primary care; seven from secondary care; and six from emergency guidelines. Eighty‐eight per cent of the formulary was unchanged. Notable changes include entry of newer anti‐epileptics and dipeptidyl peptidase‐4 inhibitors and exit of phenytoin and thiazolidinediones. CONCLUSIONS: The relative stability of the core drug list over 9 years and the current update ensure that learning based on this list remains relevant to practice. Trainee prescribers may be encouraged to use this ‘starter formulary’ to develop a sound basis of prescribing knowledge and skills that they can subsequently apply more widely.