Undertaking multi-centre randomised controlled trials in primary care: learnings and recommendations from the PULsE-AI trial researchers.

Background: Conducting effective and translational research can be challenging and few trials undertake formal reflection exercises and disseminate learnings from them. Following completion of our multicentre randomised controlled trial, which was impacted by the COVID-19 pandemic, we sought to reflect on our experiences and share our thoughts on challenges, lessons learned, and recommendations for researchers undertaking or considering research in primary care.

Methods: Researchers involved in the Prediction of Undiagnosed atriaL fibrillation using a machinE learning AlgorIthm (PULsE-AI) trial, conducted in England from June 2019 to February 2021 were invited to participate in a qualitative reflection exercise.

Identification of undiagnosed atrial fibrillation using a machine learning risk-prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England.

Aims: The aim of the PULsE-AI trial was to assess the effectiveness of a machine learning risk-prediction algorithm in conjunction with diagnostic testing for identifying undiagnosed atrial fibrillation (AF) in primary care in England.

Methods And Results: Eligible participants (aged ≥30 years without AF diagnosis;  = 23 745) from six general practices in England were randomized into intervention and control arms. Intervention arm participants, identified by the algorithm as high risk of undiagnosed AF ( = 944), were invited for diagnostic testing ( = 256 consented); those who did not accept the invitation, and all control arm participants, were managed routinely.

Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care: cost-effectiveness of a screening strategy evaluated in a randomized controlled trial in England.

Objective: The PULsE-AI trial sought to determine the effectiveness of a screening strategy that included a machine learning risk prediction algorithm in conjunction with diagnostic testing for identification of undiagnosed atrial fibrillation (AF) in primary care. This study aimed to evaluate the cost-effectiveness of implementing the screening strategy in a real-world setting.

Methods: Data from the PULsE-AI trial - a prospective, randomized, controlled trial conducted across six general practices in England from June 2019 to February 2021 - were used to inform a cost-effectiveness analysis that included a hybrid screening decision tree and Markov AF disease progression model.

Cost Effectiveness of Screening for Hepatitis C Virus in Iraq in the Era of Simplified Testing and Treatment.

Background And Objective: Recent advances in hepatitis C virus (HCV) diagnostic testing methods allow for a one-stop simplified 'test and cure' approach. The cost effectiveness of incorporating this simplified approach into HCV screening in Iraq remains uncertain. This study aimed to compare the cost effectiveness of different HCV testing and diagnostic approaches, and screening strategies in Iraq from a health service perspective.

Outcomes in rheumatoid arthritis patients treated with abatacept: a UK multi-centre observational study.

Background: Rheumatoid arthritis (RA) is an inflammatory autoimmune disease that causes chronic synovitis, resulting in progressive joint destruction and functional disability and affects approximately 400,000 people in the UK. This real-world study aimed to describe the characteristics, treatment patterns and clinical outcomes of patients who received abatacept in UK clinical practice.

Methods: This was a multi-centre, retrospective, observational study of patients with RA treated with abatacept at four UK centres between 01 January 2013 and 31 December 2017.

The effect of hyperkalemia and long inter-dialytic interval on morbidity and mortality in patients receiving hemodialysis: a systematic review.

Background: Patients with chronic kidney disease, especially those receiving hemodialysis (HD), are at risk of hyperkalemia (HK). This systematic review aimed to evaluate the prevalence of HK in patients with renal disease receiving HD and collate evidence on the effect of HK and differing HD patterns (i.e.

Costs and Healthcare Resource Use Associated with Risk of Cardiovascular Morbidity in Patients with Chronic Kidney Disease: Evidence from a Systematic Literature Review.

Introduction: The management of chronic kidney disease (CKD) costs in excess of $114 billion in the USA and £1.45 billion in the UK annually and is projected to increase alongside the increasing disease prevalence. The aim of this review was to evaluate the risks of cardiovascular (CV) morbidity, CV mortality or all-cause mortality based on KDIGO (Kidney Disease: Improving Global Outcomes) 2012 categorisations and estimate the additional costs and healthcare resource utilisation associated with CV morbidity linked to CKD severity in US and UK settings.

Risk factors associated with the incidence and recurrence of hyperkalaemia in patients with cardiorenal conditions.

Introduction: Hyperkalaemia (HK) is associated with increased mortality risk. Prior studies suggest that the causes of HK are multifactorial. This study aimed to examine risk factors for incident and recurrent HK in six large real-world cohorts of UK patients that could be considered at elevated HK risk because of underlying disease pathology and/or medication use.

Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial.

Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening.

Serum potassium as a predictor of adverse clinical outcomes in patients with increasing comorbidity burden.

Aims: The aim of this study was to establish whether patients with multiple comorbidities may be at elevated risk of hyperkalaemia (HK), a potentially life-threatening electrolyte imbalance, and the associated adverse clinical outcomes.

Methods And Results: This was a retrospective, observational cohort study using UK primary and secondary care data. Adult patients with at least one of: resistant hypertension, chronic kidney disease stage 3+, dialysis, heart failure (HF), and diabetes, were eligible for inclusion.

Predicting atrial fibrillation in primary care using machine learning.

Background: Atrial fibrillation (AF) is the most common sustained heart arrhythmia. However, as many cases are asymptomatic, a large proportion of patients remain undiagnosed until serious complications arise. Efficient, cost-effective detection of the undiagnosed may be supported by risk-prediction models relating patient factors to AF risk.

Economic Modelling of Chronic Kidney Disease: A Systematic Literature Review to Inform Conceptual Model Design.

Background: Chronic kidney disease (CKD) is a progressive condition that leads to irreversible damage to the kidneys and is associated with an increased incidence of cardiovascular events and mortality. As novel interventions become available, estimates of economic and clinical outcomes are needed to guide payer reimbursement decisions.

Objective: The aim of the present study was to systematically review published economic models that simulated long-term outcomes of kidney disease to inform cost-effectiveness evaluations of CKD treatments.

High-Definition Analysis of Host Protein Stability during Human Cytomegalovirus Infection Reveals Antiviral Factors and Viral Evasion Mechanisms.

Human cytomegalovirus (HCMV) is an important pathogen with multiple immune evasion strategies, including virally facilitated degradation of host antiviral restriction factors. Here, we describe a multiplexed approach to discover proteins with innate immune function on the basis of active degradation by the proteasome or lysosome during early-phase HCMV infection. Using three orthogonal proteomic/transcriptomic screens to quantify protein degradation, with high confidence we identified 35 proteins enriched in antiviral restriction factors.

Assessing the Long-Term Impact of Treating Hepatitis C Virus (HCV)-Infected People Who Inject Drugs in the UK and the Relationship between Treatment Uptake and Efficacy on Future Infections.

Objective: The prevalence of the hepatitis C virus (HCV) remains high amongst people who inject drugs (PWID) and accounts for the majority of newly acquired infections. This study aims to quantify the value of treatment amongst PWID with more efficacious treatments and at increased uptake rates, with respect to the avoidance of future infections and subsequent long-term complications of HCV.

Methods: A dynamic HCV transmission and disease progression model was developed, incorporating acute and chronic infection and their long-term complications (decompensated cirrhosis, cancer, liver transplant and mortality), with the potential for HCV transmission to other PWID prior to successful treatment.

Characterizing unmet medical need and the potential role of new biologic treatment options in patients with ulcerative colitis and Crohn's disease: a systematic review and clinician surveys.

Objectives: Comparative outcomes of patients with ulcerative colitis (UC) and Crohn's disease (CD) prescribed a biologic therapy are inconclusive. The aim of this research was to characterize the degree of unmet medical need in patients with UC or CD and to identify the potential role for new therapies.

Methods: A systematic literature review was undertaken of studies reporting outcomes associated with the use of existing biologic therapies in patients with UC or CD, focusing on the nature and rate of treatment failure.

Plasma membrane profiling defines an expanded class of cell surface proteins selectively targeted for degradation by HCMV US2 in cooperation with UL141.

Human cytomegalovirus (HCMV) US2, US3, US6 and US11 act in concert to prevent immune recognition of virally infected cells by CD8+ T-lymphocytes through downregulation of MHC class I molecules (MHC-I). Here we show that US2 function goes far beyond MHC-I degradation. A systematic proteomic study using Plasma Membrane Profiling revealed US2 was unique in downregulating additional cellular targets, including: five distinct integrin α-chains, CD112, the interleukin-12 receptor, PTPRJ and thrombomodulin.

Two novel human cytomegalovirus NK cell evasion functions target MICA for lysosomal degradation.

NKG2D plays a major role in controlling immune responses through the regulation of natural killer (NK) cells, αβ and γδ T-cell function. This activating receptor recognizes eight distinct ligands (the MHC Class I polypeptide-related sequences (MIC) A andB, and UL16-binding proteins (ULBP)1-6) induced by cellular stress to promote recognition cells perturbed by malignant transformation or microbial infection. Studies into human cytomegalovirus (HCMV) have aided both the identification and characterization of NKG2D ligands (NKG2DLs).

Human cytomegalovirus suppresses Fas expression and function.

Human cytomegalovirus (HCMV) is known to evade extrinsic pro-apoptotic pathways not only by downregulating cell surface expression of the death receptors TNFR1, TRAIL receptor 1 (TNFRSF10A) and TRAIL receptor 2 (TNFRSF10B), but also by impeding downstream signalling events. Fas (CD95/APO-1/TNFRSF6) also plays a prominent role in apoptotic clearance of virus-infected cells, so its fate in HCMV-infected cells needs to be addressed. Here, we show that cell surface expression of Fas was suppressed in HCMV-infected fibroblasts from 24 h onwards through the late phase of productive infection, and was dependent on de novo virus-encoded gene expression but not virus DNA replication.

Neural signatures of autism.

Functional magnetic resonance imaging of brain responses to biological motion in children with autism spectrum disorder (ASD), unaffected siblings (US) of children with ASD, and typically developing (TD) children has revealed three types of neural signatures: (i) state activity, related to the state of having ASD that characterizes the nature of disruption in brain circuitry; (ii) trait activity, reflecting shared areas of dysfunction in US and children with ASD, thereby providing a promising neuroendophenotype to facilitate efforts to bridge genomic complexity and disorder heterogeneity; and (iii) compensatory activity, unique to US, suggesting a neural system-level mechanism by which US might compensate for an increased genetic risk for developing ASD. The distinct brain responses to biological motion exhibited by TD children and US are striking given the identical behavioral profile of these two groups. These findings offer far-reaching implications for our understanding of the neural systems underlying autism.

Human cytomegalovirus UL141 promotes efficient downregulation of the natural killer cell activating ligand CD112.

Human cytomegalovirus (HCMV) UL141 induces protection against natural killer cell-mediated cytolysis by downregulating cell surface expression of CD155 (nectin-like molecule 5; poliovirus receptor), a ligand for the activating receptor DNAM-1 (CD226). However, DNAM-1 is also recognized to bind a second ligand, CD112 (nectin-2). We now show that HCMV targets CD112 for proteasome-mediated degradation by 48 h post-infection, thus removing both activating ligands for DNAM-1 from the cell surface during productive infection.