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. METHODS: This retrospective, observational cohort study utilised UK primary and secondary care data from Clinical Practice Research Datalink (CPRD) and linked Hospital Episode Statistics (HES), respectively. Patients were included if they were aged ≥18 years and had a record of ≥1 condition of interest (chronic kidney disease [CKD] stage 3+, heart failure, resistant hypertension [RHTN], dialysis, diabetes) and/or renin-angiotensin-aldosterone system inhibitors (RAASi) use between 01 January 2003 and 30 June 2018. Based on their diagnosis/ RAASi prescription record, patients were assigned to overlapping cohorts. The outcomes assessed were HK and recurrent HK, the latter defined as second or subsequent HK episode during follow-up. HK was defined as a serum K+ measurement ≥5.0 mmol/L; thresholds of ≥5.5 and ≥ 6.0 mmol/L were also explored. RESULTS: Of 931 460 meeting the eligibility criteria, 310 535 (33.3%) patients experienced ≥1 HK event and 187 719 (20.2%) experienced HK recurrence. The probability of subsequent HK events increased with event number from 60.5% for the second event to 76.5% for the sixth and the corresponding time to the next HK event decreased from 15.8 months to 6.1 months. Amongst the key risk factors, serum creatinine, serum phosphorus, systolic blood pressure, and white cell count demonstrated direct relationships with incidence and recurrence of HK, while inverse relationships were observed for estimated glomerular filtration rate (eGFR), haemoglobin and diastolic blood pressure. The relationship for Charlson's Comorbidity Index was mixed. The use of RAASi and anti-hyperglycaemic agents was associated with an increased risk of HK, while the use of diuretics (non-K+ -sparing) was protective against HK. CONCLUSION: Several risk factors for HK that are easily measured in routine clinical practice were identified, facilitating the identification of patients who are at the highest risk of experiencing HK, including recurrent HK.

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., long vs. short inter-dialytic intervals [LIDI and SIDI, respectively] in a thrice weekly schedule) on mortality. METHODS: Comprehensive searches were conducted across six databases and selected conference proceedings by two independent reviewers up to September 2020. A hundred and two studies reporting frequency of HK, mortality, or cardiovascular (CV) outcomes in adult patients with acute, chronic or end-stage renal disease in receipt of HD were included. Narrative synthesis of results was undertaken with key findings presented in tables and figures. RESULTS: Median prevalence of HK in patients with renal disease receiving HD was 21.6% and increased in patients receiving concomitant medications - mainly renin-angiotensin-aldosterone system inhibitors and potassium-sparing diuretics. Associations between elevated potassium levels and increased risk of both all-cause and CV mortality in the HD population were consistent across the included studies. In addition, there was a rise in all-cause and CV mortality on the day following LIDI compared with the day after the two SIDIs in patients on HD. CONCLUSIONS: Evidence identified in this systematic review indicates a relationship between HK and LIDI with mortality in patients with renal disease receiving HD, emphasizing the need for effective monitoring and management to control potassium levels both in emergency and chronic HD settings.

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. US2 recruited the cellular E3 ligase TRC8 to direct the proteasomal degradation of all its targets, reminiscent of its degradation of MHC-I. Whereas integrin α-chains were selectively degraded, their integrin ß1 binding partner accumulated in the ER. Consequently integrin signaling, cell adhesion and migration were strongly suppressed. US2 was necessary and sufficient for degradation of the majority of its substrates, but remarkably, the HCMV NK cell evasion function UL141 requisitioned US2 to enhance downregulation of the NK cell ligand CD112. UL141 retained CD112 in the ER from where US2 promoted its TRC8-dependent retrotranslocation and degradation. These findings redefine US2 as a multifunctional degradation hub which, through recruitment of the cellular E3 ligase TRC8, modulates diverse immune pathways involved in antigen presentation, NK cell activation, migration and coagulation; and highlight US2's impact on HCMV pathogenesis.

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, αß 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). HCMV immediate early (IE) gene up regulates NKGDLs, and we now describe the differential activation of ULBP2 and MICA/B by IE1 and IE2 respectively. Despite activation by IE functions, HCMV effectively suppressed cell surface expression of NKGDLs through both the early and late phases of infection. The immune evasion functions UL16, UL142, and microRNA(miR)-UL112 are known to target NKG2DLs. While infection with a UL16 deletion mutant caused the expected increase in MICB and ULBP2 cell surface expression, deletion of UL142 did not have a similar impact on its target, MICA. We therefore performed a systematic screen of the viral genome to search of addition functions that targeted MICA. US18 and US20 were identified as novel NK cell evasion functions capable of acting independently to promote MICA degradation by lysosomal degradation. The most dramatic effect on MICA expression was achieved when US18 and US20 acted in concert. US18 and US20 are the first members of the US12 gene family to have been assigned a function. The US12 family has 10 members encoded sequentially through US12-US21; a genetic arrangement, which is suggestive of an 'accordion' expansion of an ancestral gene in response to a selective pressure. This expansion must have be an ancient event as the whole family is conserved across simian cytomegaloviruses from old world monkeys. The evolutionary benefit bestowed by the combinatorial effect of US18 and US20 on MICA may have contributed to sustaining the US12 gene family.

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. Significant levels of the fully glycosylated (endoglycosidase-H-resistant) Fas were retained within HCMV-infected cells throughout the infection within intracellular membranous structures. HCMV infection provided cells with a high level of protection against Fas-mediated apoptosis. Downregulation of Fas was observed with HCMV strains AD169, FIX, Merlin and TB40.

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. Members of the Trial Steering Committee (TSC) were invited to attend a semi-structured focus group session, Principal Investigators and their research teams at practices involved in the trial were invited to participate in a semi-structured interview. Following transcription, reflexive thematic analysis was undertaken based on pre-specified themes of recruitment, challenges, lessons learned, and recommendations that formed the structure of the focus group/interview sessions, whilst also allowing the exploration of new themes that emerged from the data. RESULTS: Eight of 14 members of the TSC, and one of six practices involved in the trial participated in the reflection exercise. Recruitment was highlighted as a major challenge encountered by trial researchers, even prior to disruption due to the COVID-19 pandemic. Researchers also commented on themes such as the need to consider incentivisation, and challenges associated with using technology in trials, especially in older age groups. CONCLUSIONS: Undertaking a formal reflection exercise following the completion of the PULsE-AI trial enabled us to review experiences encountered whilst undertaking a prospective randomised trial in primary care. In sharing our learnings, we hope to support other clinicians undertaking research in primary care to ensure that future trials are of optimal value for furthering knowledge, streamlining pathways, and benefitting patients.

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.

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. According to their diagnoses, patients were grouped into overlapping cohorts that were updated as multimorbidity progressed. Outcomes of interest were incident HK, all-cause mortality (ACM), and major adverse cardiovascular events (MACE). A total of 673 686 patients met the eligibility criteria, 36.3% of whom developed multimorbidity during the study period. A consistent U-shaped association was observed between serum K+ level and adjusted incidence of ACM and MACE. Hyperkalaemia was progressively more common with increasing Charlson Comorbidity Index (CCI). Relative to a CCI <3, scores of ≥3 to <6, and ≥6 were associated with 2.9- and 6.2-fold increases, respectively, in crude HK (serum K+ ≥5.0 mmol/L) incidence rate. In all condition-based cohorts except for HF, there was a clear correlation between increasing CCI and the risk of ACM and MACE associated with hypokalaemia and HK. CONCLUSION: Patients with a higher CCI are at an increased risk of developing HK and appear more prone to adverse clinical outcomes associated with abnormal serum K+ levels, warranting additional routine clinical monitoring.

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. Model outcomes were reported at both individual- and population-level (estimated UK population ≥30 years of age at high-risk of undiagnosed AF) and included number of patients screened, number of AF cases identified, mean total and incremental costs (screening, events, treatment), quality-adjusted-life-years (QALYs), and incremental cost-effectiveness ratio (ICER). RESULTS: The screening strategy was estimated to result in 45,493 new diagnoses of AF across the high-risk population in the UK (3.3 million), and an estimated additional 14,004 lifetime diagnoses compared with routine care only. Per-patient costs for high-risk individuals who underwent the screening strategy were estimated at £1,985 (vs £1,888 for individuals receiving routine care only). At a population-level, the screening strategy was associated with a cost increase of approximately £322 million and an increase of 81,000 QALYs. The screening strategy demonstrated cost-effectiveness versus routine care only at an accepted ICER threshold of £20,000 per QALY-gained, with an ICER of £3,994/QALY. CONCLUSIONS: Compared with routine care only, it is cost-effective to target individuals at high risk of undiagnosed AF, through an AF risk prediction algorithm, who should then undergo diagnostic testing. This AF risk prediction algorithm can reduce the number of patients needed to be screened to identify undiagnosed AF, thus alleviating primary care burden.

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; n = 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 (n = 944), were invited for diagnostic testing (n = 256 consented); those who did not accept the invitation, and all control arm participants, were managed routinely. The primary endpoint was the proportion of AF, atrial flutter, and fast atrial tachycardia diagnoses during the trial (June 2019-February 2021) in high-risk participants. Atrial fibrillation and related arrhythmias were diagnosed in 5.63% and 4.93% of high-risk participants in intervention and control arms, respectively {odds ratio (OR) [95% confidence interval (CI)]: 1.15 (0.77-1.73), P = 0.486}. Among intervention arm participants who underwent diagnostic testing (28.1%), 9.41% received AF and related arrhythmia diagnoses [vs. 4.93% (control); OR (95% CI): 2.24 (1.31-3.73), P = 0.003]. Conclusion: The AF risk-prediction algorithm accurately identified high-risk participants in both arms. While the proportions of AF and related arrhythmia diagnoses were not significantly different between high-risk arms, intervention arm participants who underwent diagnostic testing were twice as likely to receive arrhythmia diagnoses compared with routine care. The algorithm could be a valuable tool to select primary care groups at high risk of undiagnosed AF who may benefit from diagnostic testing.

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. METHODS: A systematic literature review was conducted of studies reporting on the risk of CV morbidity, CV mortality or all-cause mortality characterised by CKD severity (published between January 2000 and September 2018). Additional costs and bed days associated with CKD severity in the USA and UK were estimated on the basis of median hazard ratios for CV morbidity risk at each CKD and albuminuria stage. RESULTS: Twenty-nine studies reported risk of adverse clinical outcomes based on KDIGO categorisations. Compared to stage 1 (or without) CKD, patients with stage 5 CKD and macroalbuminuria experienced a relative risk increase of 11.77-12.46 across all outcomes. Additional costs and bed days associated with stage 5 CKD and macroalbuminuria (versus stage 1 (or without) CKD) per 1000 patient years were US$3.93 million and 803 bed days and £435,000 and 1017 bed days, in the USA and UK, respectively. CONCLUSION: Risks of adverse clinical outcomes increase with CKD and albuminuria severity and are associated with substantial additional costs and resource utilisation. Thus, early diagnosis and proactive management of CKD and its complications should be a priority for healthcare providers to alleviate the burden of CV morbidity and its management on healthcare resources.

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. METHODS: A cost-effectiveness analysis was undertaken using a hybrid model comprising a screening decision tree linked to a lifetime Markov model to estimate outcomes in HCV-infected people. Cost and utility estimates were sourced from the published literature and expert guidance provided by clinicians and policy makers in Iraq. Cost estimates were reported in 2019 USD or 2019 Iraqi Dinar and both costs and benefits were discounted at 3.5% annually. RESULTS: Strategies using a simplified approach were found to be cost saving in addition to improving patient outcomes when compared with a standard testing and diagnostic approach. When considering risk-based screening, a simplified approach was associated with a total cost saving of Iraqi Dinar 4375 billion (USD 3.7 billion) and per patient life-year and quality-adjusted life-year gains of 0.30 and 0.55, compared with a standard approach. Benefits and cost savings were driven by a 32.2% and 23.6% reduction in the incidence of cirrhosis and hepatocellular carcinoma, respectively. Estimated benefits and cost savings increased under total population screening. All screening and testing and diagnostic approaches were cost effective compared with a no screening scenario. CONCLUSIONS: Improvements in the detection of HCV combined with a simplified one-stop testing and diagnostic approach represents an opportunity to reduce the burden of HCV in Iraq and may play a significant role in meeting World Health Organisation HCV elimination targets.

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. Data were collected from medical records of each patient from the index date (date of first bDMARD initiation) until the most recent visit, death or end of study (31 December 2017). RESULTS: In total, 213 patients were included in the study. Patients received up to eight lines of therapy (LOTs). Treatment with abatacept, or any other bDMARD, was associated with reductions in DAS28-ESR and DAS28-CRP scores at 6 and 12 months. The distribution of EULAR responses (good/moderate/no response) tended to be more favourable for patients when receiving abatacept than when receiving other bDMARDs (22.8%/41.3%/35.9% versus 16.6%/41.4%/42.1% at 6 months, and 27.9%/36.1%/36.1% versus 21.2%/34.5%/44.2% at 12 months). Patients receiving abatacept at LOT1 (n = 68) spent significantly longer on treatment compared with patients receiving other bDMARDs (53.4 vs. 17.4 months; p< 0.01); a similar trend was observed for LOT2. Among patients who discontinued after 6 months, a greater proportion experienced infection requiring antibiotics when receiving other bDMARDs compared to those receiving abatacept. CONCLUSIONS: RA patients who received bDMARDs, including abatacept, experienced reduced disease activity. When receiving abatacept as first or second line of therapy, patients persisted with treatment significantly longer than those receiving other bDMARDs.

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. Significantly, cell surface expression of both CD112 and CD155 was restored when UL141 was deleted from the HCMV genome. While gpUL141 alone is sufficient to mediate retention of CD155 in the endoplasmic reticulum, UL141 requires assistance from additional HCMV-encoded functions to suppress expression of CD112.

Estimating HIV Management and Comorbidity Costs Among Aging HIV Patients in the United States: A Systematic Review.

BACKGROUND: As life expectancy of patients infected with human immunodeficiency virus (HIV) approaches that of the general population, the composition of HIV management costs is likely to change. OBJECTIVES: To (a) review treatment and disease management costs in HIV, including costs of adverse events (AEs) related to antiretroviral therapy (ART) and long-term toxicities, and (b) explore the evolving cost drivers. METHODS: A targeted literature review between January 2012 and November 2017 was conducted using PubMed and major conferences. Articles reporting U.S. costs of HIV management, acquired immunodeficiency syndrome (AIDS)-defining events, end of life care, and ART-associated comorbidities such as cardiovascular disease (CVD), chronic kidney disease (CKD), and osteoporosis were included. All costs were inflated to 2017 U.S. dollars. A Markov model-based analysis was conducted to estimate the effect of increased life expectancy on costs associated with HIV treatment and management. RESULTS: 22 studies describing HIV costs in the United States were identified, comprising 16 cost-effectiveness analysis studies, 5 retrospective analyses of health care utilization, and 1 cost analysis in a resource-limited setting. Management costs per patient per month, including routine care costs (on/off ART), non-HIV medication, opportunistic infection prophylaxis, inpatient utilization, outpatient utilization, and emergency department utilization were reported as CD4+ cell-based health state costs ranging from $1,192 for patients with CD4 > 500 cells/mm3 to $2,873 for patients with CD4 < 50 cells/mm3. Event costs for AEs ranged from $0 for headache, pain, vomiting, and lipodystrophy to $31,545 for myocardial infarction. The mean monthly per-patient costs for CVD management, CKD management, and osteoporosis were $5,898, $6,108, and $4,365, respectively. Improvements in life expectancy, approaching that of the general population in 2018, are projected to increase ART-related and AE costs by 35.4% and comorbidity costs by 175.8% compared with estimated costs with HIV life expectancy observed in 1996. CONCLUSIONS: This study identified and summarized holistic cost estimates appropriate for use within U.S. HIV cost-effectiveness analyses and demonstrates an increasing contribution of comorbidity outcomes, primarily associated with aging in addition to long-term treatment with ART, not typically evaluated in contemporary HIV cost-effectiveness analyses. DISCLOSURES: This analysis was sponsored by ViiV Healthcare, which had no role in the analyses and interpretation of study results. Ward, Sugrue, Hayward, and McEwan are employees of HEOR Ltd, which received funding from ViiV Healthcare to conduct this study. Anderson is an employee of GlaxoSmithKline and holds shares in the company. Punekar and Oglesby are employees of ViiV Healthcare and hold shares in GlaxoSmithKline. Lopes was employed by ViiV Healthcare at the time of the study and holds shares in GlaxoSmithKline.

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. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, was found to be more effective than existing models at identifying patients at risk of AF. Therefore, the aim of the trial is to assess the effectiveness of this risk prediction algorithm combined with diagnostic testing for the identification of AF in a real-world primary care setting. Eligible participants (aged ≥30 years and without an existing AF diagnosis) registered at participating UK general practices will be randomised into intervention and control arms. Intervention arm participants identified at highest risk of developing AF (algorithm risk score ≥ 7.4%) will be invited for a 12­lead electrocardiogram (ECG) followed by two-weeks of home-based ECG monitoring with a KardiaMobile device. Control arm participants will be used for comparison and will be managed routinely. The primary outcome is the number of AF diagnoses in the intervention arm compared with the control arm during the research window. If the trial is successful, there is potential for the risk prediction algorithm to be implemented throughout primary care for narrowing the population considered at highest risk for AF who could benefit from more intensive screening for AF. Trial Registration: NCT04045639.

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. METHODS: The review was conducted across four databases (MEDLINE, Embase, the Cochrane library and EconLit) and health technology assessment agency websites. Relevant information on each model was extracted. Transition and mortality rates were also extracted to assess the choice of model parameterisation on disease progression by simulating patient's time with end-stage renal disease (ESRD) and time to ESRD/death. The incorporation of cardiovascular disease in a population with CKD was qualitatively assessed across identified models. RESULTS: The search identified 101 models that met the criteria for inclusion. Models were classified into CKD models (n = 13), diabetes models with nephropathy (n = 48), ESRD-only models (n = 33) and cardiovascular models with CKD components (n = 7). Typically, published models utilised frameworks based on either (estimated or measured) glomerular filtration rate (GFR) or albuminuria, in line with clinical guideline recommendations for the diagnosis and monitoring of CKD. Generally, two core structures were identified, either a microsimulation model involving albuminuria or a Markov model utilising CKD stages and a linear GFR decline (although further variations on these model structures were also identified). Analysis of parameter variability in CKD disease progression suggested that mean time to ESRD/death was relatively consistent across model types (CKD models 28.2 years; diabetes models with nephropathy 24.6 years). When evaluating time with ESRD, CKD models predicted extended ESRD survival over diabetes models with nephropathy (mean time with ESRD 8.0 vs. 3.8 years). DISCUSSION: This review provides an overview of how CKD is typically modelled. While common frameworks were identified, model structure varied, and no single model type was used for the modelling of patients with CKD. In addition, many of the current methods did not explicitly consider patient heterogeneity or underlying disease aetiology, except for diabetes. However, the variability of individual patients' GFR and albuminuria trajectories perhaps provides rationale for a model structure designed around the prediction of individual patients' GFR trajectories. Frameworks of future CKD models should be informed and justified based on clinical rationale and availability of data to ensure validity of model results. In addition, further clinical and observational research is warranted to provide a better understanding of prognostic factors and data sources to improve economic modelling accuracy in CKD.

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. However, there exists a need for an implementable risk model that is contemporaneous and informed by routinely collected patient data, reflecting the real-world pathology of AF. METHODS: This study sought to develop and evaluate novel and conventional statistical and machine learning models for risk-predication of AF. This was a retrospective, cohort study of adults (aged ≥30 years) without a history of AF, listed on the Clinical Practice Research Datalink, from January 2006 to December 2016. Models evaluated included published risk models (Framingham, ARIC, CHARGE-AF), machine learning models, which evaluated baseline and time-updated information (neural network, LASSO, random forests, support vector machines), and Cox regression. RESULTS: Analysis of 2,994,837 individuals (3.2% AF) identified time-varying neural networks as the optimal model achieving an AUROC of 0.827 vs. 0.725, with number needed to screen of 9 vs. 13 patients at 75% sensitivity, when compared with the best existing model CHARGE-AF. The optimal model confirmed known baseline risk factors (age, previous cardiovascular disease, antihypertensive medication usage) and identified additional time-varying predictors (proximity of cardiovascular events, body mass index (both levels and changes), pulse pressure, and the frequency of blood pressure measurements). CONCLUSION: The optimal time-varying machine learning model exhibited greater predictive performance than existing AF risk models and reflected known and new patient risk factors for AF.

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. A final screen employed a comprehensive panel of viral mutants to predict viral genes that target >250 human proteins. This approach revealed that helicase-like transcription factor (HLTF), a DNA helicase important in DNA repair, potently inhibits early viral gene expression but is rapidly degraded during infection. The functionally unknown HCMV protein UL145 facilitates HLTF degradation by recruiting the Cullin4 E3 ligase complex. Our approach and data will enable further identifications of innate pathways targeted by HCMV and other viruses.

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. The model was populated with prevalence and therapy data from a UK setting. Scenarios of current standard of care (SoC) treatment efficacy and uptake were compared to anticipated sustained virologic response (SVR) rates of 90-100% and increased uptake over varied horizons. RESULTS: SoC led to modest reductions in prevalence; >5% after 200 years. New treatments achieving 90% SVR could reduce prevalence below 5% within 60 years at current uptake rates or within 5 years if all patients are treated. Amongst 4,240 PWID, chronic HCV infections avoided as a result of increasing treatment uptake over the period 2015-2027 ranged from 20-580 and 34-912 with SoC and 90% SVR rates respectively. The reduction in downstream HCV infections due to increasing treatment uptake resulted in an approximate discounted gain of 300 life-years (from avoiding reduced life expectancy from HCV infection) and a gain of 1,700 QALYs (from avoiding the disutility of HCV infection and related complications), with a projected £5.4 million cost saving. CONCLUSION: While improved SVR profiles led to reductions in modelled prevalence, increased treatment uptake was the key driver of future infections avoided. Increased treatment among PWID with new more efficacious therapies could significantly change the future dynamics, cost and health burden of HCV-related disease.