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.

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.

Budget impact analysis of a machine learning algorithm to predict high risk of atrial fibrillation among primary care patients.

AIMS: We investigated whether the use of an atrial fibrillation (AF) risk prediction algorithm could improve AF detection compared with opportunistic screening in primary care and assessed the associated budget impact. METHODS AND RESULTS: Eligible patients were registered with a general practice in UK, aged 65 years or older in 2018/19, and had complete data for weight, height, body mass index, and systolic and diastolic blood pressure recorded within 1 year. Three screening scenarios were assessed: (i) opportunistic screening and diagnosis (standard care); (ii) standard care replaced by the use of the algorithm; and (iii) combined use of standard care and the algorithm. The analysis considered a 3-year time horizon, and the budget impact for the National Health Service (NHS) costs alone or with personal social services (PSS) costs. Scenario 1 would identify 79 410 new AF cases (detection gap reduced by 22%). Scenario 2 would identify 70 916 (gap reduced by 19%) and Scenario 3 would identify 99 267 new cases (gap reduction 27%). These rates translate into 2639 strokes being prevented in Scenario 1, 2357 in Scenario 2, and 3299 in Scenario 3. The 3-year NHS budget impact of Scenario 1 would be £45.3 million, £3.6 million (difference ‒92.0%) with Scenario 2, and £46.3 million (difference 2.2%) in Scenario 3, but for NHS plus PSS would be ‒£48.8 million, ‒£80.4 million (64.8%), and ‒£71.3 million (46.1%), respectively. CONCLUSION: Implementation of an AF risk prediction algorithm alongside standard opportunistic screening could close the AF detection gap and prevent strokes while substantially reducing NHS and PSS combined care costs.

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.

Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study.

AIMS: To evaluate the ability of a machine learning algorithm to identify patients at high risk of atrial fibrillation in primary care. METHODS: A retrospective cohort study was undertaken using the DISCOVER registry to validate an algorithm developed using a Clinical Practice Research Datalink (CPRD) dataset. The validation dataset included primary care patients in London, England aged ≥30 years from 1 January 2006 to 31 December 2013, without a diagnosis of atrial fibrillation in the prior 5 years. Algorithm performance metrics were sensitivity, specificity, positive predictive value, negative predictive value (NPV) and number needed to screen (NNS). Subgroup analysis of patients aged ≥65 years was also performed. RESULTS: Of 2,542,732 patients in DISCOVER, the algorithm identified 604,135 patients suitable for risk assessment. Of these, 3.0% (17,880 patients) had a diagnosis of atrial fibrillation recorded before study end. The area under the curve of the receiver operating characteristic was 0.87, compared with 0.83 in algorithm development. The NNS was nine patients, matching the CPRD cohort. In patients aged ≥30 years, the algorithm correctly identified 99.1% of patients who did not have atrial fibrillation (NPV) and 75.0% of true atrial fibrillation cases (sensitivity). Among patients aged ≥65 years (n = 117,965), the NPV was 96.7% with 91.8% sensitivity. CONCLUSIONS: This atrial fibrillation risk prediction algorithm, based on machine learning methods, identified patients at highest risk of atrial fibrillation. It performed comparably in a large, real-world population-based cohort and the developmental registry cohort. If implemented in primary care, the algorithm could be an effective tool for narrowing the population who would benefit from atrial fibrillation screening in the United Kingdom.

An observational study of international normalized ratio control according to NICE criteria in patients with non-valvular atrial fibrillation: the SAIL Warfarin Out of Range Descriptors Study (SWORDS).

AIMS: In patients with non-valvular atrial fibrillation prescribed warfarin, the UK National Institute of Health and Care Excellence (NICE) defines poor anticoagulation as a time in therapeutic range (TTR) of <65%, any two international normalized ratios (INRs) within a 6-month period of ≤1.5 ('low'), two INRs ≥5 within 6 months, or any INR ≥8 ('high'). Our objectives were to (i) quantify the number of patients with poor INR control and (ii) describe the demographic and clinical characteristics associated with poor INR control. METHOD AND RESULTS: Linked anonymized health record data for Wales, UK (2006-2017) was used to evaluate patients prescribed warfarin who had at least 6 months of INR data. 32 380 patients were included. In total, 13 913 (43.0%) patients had at least one of the NICE markers of poor INR control. Importantly, in the 24 123 (74.6%) of the cohort with an acceptable TTR (≥65%), 5676 (23.5%) had either low or high INR readings at some point in their history. In a multivariable regression female gender, age (≥75 years), excess alcohol, diabetes heart failure, ischaemic heart disease, and respiratory disease were independently associated with all markers of poor INR control. CONCLUSION: Acceptable INR control according to NICE standards is poor. Of those with an acceptable TTR (>65%), one-quarter still had unacceptably low or high INR levels according to NICE criteria. Thus, only using TTR to assess effectiveness with warfarin has the potential to miss a large number of patients with non-therapeutic INRs who are likely to be at increased risk.

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.

Updated Software for Automated Assessment of Glucose Variability and Quality of Glycemic Control in Diabetes.

Background: Glycemic variability is an important factor to consider in diabetes management. It can be assessed with multiple glycemic variability metrics and quality of control indices based on continuous glucose monitoring (CGM) recordings. For this, a robust repeatable calculation is important. A widely used tool for automated assessment is the EasyGV software. The aim of this work is to implement new methods of glycemic variability assessment in EasyGV and to validate implementation of each glucose metric in EasyGV against a reference implementation of the calculations. Methods: Validation data used came from the JDRF CGM study. Validation of the implementation of metrics that are available in EasyGV software v9 was carried out and the following new methods were added and validated: personal glycemic state, index of glycemic control, times in ranges, and glycemic variability percentage. Reference values considered gold standard calculations were derived from MATLAB implementation of each metric. Results: The Pearson correlation coefficient was above 0.98 for all metrics, except for mean amplitude of glycemic excursion (r = 0.87) as EasyGV implements a fuzzy logic approach to assessment of variability. Bland-Altman plots demonstrated validation of the new software. Conclusions: The new freely available EasyGV software v10 (www.phc.ox.ac.uk/research/technology-outputs/easygv) is a validated robust tool for analyzing different glycemic variabilities and control metrics.

Assessment of Glucose Control Metrics by Discriminant Ratio.

Objective: Increasing use of continuous glucose monitoring (CGM) data has created an array of glucose metrics for glucose variability, temporal patterns, and times in ranges. However, a gold standard metric has not been defined. We assess the performance of multiple glucose metrics to determine their ability to detect intra- and interperson variability to determine a set of recommended metrics. Methods: The Juvenile Diabetes Research Foundation data set, a randomized controlled study of CGM and self-monitored blood glucose conducted in children and adults with type 1 diabetes (T1D), was used. To determine the ability of the evaluated glycemic metrics to discriminate between different subjects and attenuate the effect of within-subject variation, the discriminant ratio was calculated and compared for each metric. Then, the findings were confirmed using data from two other recent randomized clinical trials. Results: Mean absolute glucose (MAG) has the highest discriminant ratio value (2.98 [95% confidence interval {CI} 1.64-3.67]). In addition, low blood glucose index and index of glycemic control performed well (1.93 [95% CI 1.15-3.44] and 1.92 [95% CI 1.27-2.93], respectively). For percentage times in glucose target ranges, the optimal discriminator was percentage time in glucose target 70-180 mg/dL. Conclusions: MAG is the optimal index to differentiate glucose variability in people with T1D, and may be a complementary therapeutic monitoring tool in addition to glycated hemoglobin and a measure of hypoglycemia. Percentage time in glucose target 70-180 mg/dL is the optimal percentage time in range to report.

A Systematic Review of Network Meta-Analyses and Real-World Evidence Comparing Apixaban and Rivaroxaban in Nonvalvular Atrial Fibrillation.

There is no direct evidence comparing the 2 most commonly prescribed direct oral anticoagulants, apixaban and rivaroxaban, used for stroke prevention in nonvalvular atrial fibrillation (NVAF). A number of network meta-analyses (NMAs) of randomized control trials and real-world evidence (RWE) studies comparing the efficacy, effectiveness, and safety of apixaban and rivaroxaban have been published; however, a comprehensive evidence review across the available body of evidence is lacking. In this study, we aimed to systematically review and evaluate the clinical outcomes of apixaban and rivaroxaban using a combination of data gleaned from both NMAs and RWE studies. The review identified 21 NMAs and 5 RWE studies. The data demonstrated that apixaban was associated with fewer major bleeding events compared to rivaroxaban. There was no difference in the efficacy/effectiveness profiles between these treatments. Bleeding is a serious complication of anticoagulation therapy for the management of NVAF, and is associated with increased rates of hospitalization, morbidity, mortality, and health-care expenditure. The majority of studies in this comprehensive evidence review suggests that apixaban has a lower risk of major bleeding events compared to rivaroxaban in patients with NVAF.

Application of a machine learning algorithm for detection of atrial fibrillation in secondary care.

Atrial fibrillation (AF) is the most common sustained heart arrhythmia and significantly increases risk of stroke. Opportunistic AF testing in high-risk patients typically requires frequent electrocardiogram tests to capture the arrhythmia. Risk-prediction algorithms may help to more accurately identify people with undiagnosed AF and machine learning (ML) may aid in the diagnosis of AF. Here, we applied an AF-risk prediction algorithm to secondary care data linked to primary care data in the DISCOVER database in order to evaluate changes in model performance, and identify patients not previously detected in primary care. We identified an additional 5,444 patients who had an AF diagnosis only in secondary care during the data extraction period. 2,696 (49.5%) were accepted by the algorithm and the algorithm correctly assigned 2,637 (97.8%) patients to the AF cohort. Using a risk threshold of 7.4% in patients aged ≥ 30 years, algorithm sensitivity and specificity was 38% and 95%, respectively. Approximately 15% of AF patients assigned to the AF cohort by the algorithm had a secondary care diagnosis with no record of AF in primary care. These additional patients did not substantially alter algorithm performance. The additional detection of previously undiagnosed AF patients in secondary care highlights unexpected potential utility of this ML algorithm.

Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm.

Aims: As many cases of atrial fibrillation (AF) are asymptomatic, patients often remain undiagnosed until complications (e.g. stroke) manifest. Risk-prediction algorithms may help to efficiently identify people with undiagnosed AF. However, the cost-effectiveness of targeted screening remains uncertain. This study aimed to assess the cost-effectiveness of targeted screening, informed by a machine learning (ML) risk prediction algorithm, to identify patients with AF.Methods: Cost-effectiveness analyses were undertaken utilizing a hybrid screening decision tree and Markov disease progression model. Costs and outcomes associated with the detection of AF compared traditional systematic and opportunistic AF screening strategies to targeted screening informed by a ML risk prediction algorithm. Model analyses were based on adults ≥50 years and adopted the UK NHS perspective.Results: Targeted screening using the ML risk prediction algorithm required fewer patients to be screened (61 per 1,000 patients, compared to 534 and 687 patients in the systematic and opportunistic strategies) and detected more AF cases (11 per 1,000 patients, compared to 6 and 8 AF cases in the systematic and opportunistic screening strategies). The targeted approach demonstrated cost-effectiveness under base case settings (cost per QALY gained of £4,847 and £5,544 against systematic and opportunistic screening respectively). The targeted screening strategy was predicted to provide an additional 3.40 and 2.05 QALYs per 1,000 patients screened versus systematic and opportunistic strategies. The targeted screening strategy remained cost-effective in all scenarios evaluated.Limitations: The analysis relied on assumptions that include the extended period of patient life span and the lack of consideration for treatment discontinuations/switching, as well as the assumption that the ML risk-prediction algorithm will identify asymptomatic AF.Conclusions: Targeted screening using a ML risk prediction algorithm has the potential to enhance the clinical and cost-effectiveness of AF screening, improving health outcomes through efficient use of limited healthcare resources.

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.

Evolution of the burden of active hepatitis C virus infection in England from September 2015 to September 2016: a repeated cross-sectional analysis.

OBJECTIVE: To evaluate the impact of treatment with new direct-acting antivirals (DAAs) on the prevalent hepatitis C virus (HCV) population in England. DESIGN: A repeated cross-sectional analysis. SETTING: Four secondary care hospitals in England. PARTICIPANTS: Patients who, in 2015 and/or 2016, had chronic HCV infection and were alive were eligible, regardless of the type of HCV intervention received. OUTCOME MEASURES: Data including intravenous drug use (IVDU) status, HCV genotype, cirrhosis status, HCV treatment history, vital status and treatment outcomes were collected at two time points in 2015 and 2016 using electronic case report forms. RESULTS: There were 1605 and 1355 patients with active chronic HCV in 2015 and 2016, respectively. Between 2015 and 2016, the proportion of patients with current IVDU increased (10.3% vs 14.5%, respectively), while that of patients with cirrhosis (28.2% vs 22.4%) and treatment-experienced patients (31.2% vs 27.1%) decreased. Among patients whose treatment outcome was known by 2016, high cure rates were observed, with an overall sustained virological response rate of 93.2%. From 2015 to 2016, there was a progressive increase in the proportion of treated patients who were non-cirrhotic, with current IVDU and non-liver transplant recipients. CONCLUSIONS: The characteristics of patients with HCV remaining in contact with specialised care evolved with a changing landscape of treatment and related health policy. With increasing access to DAAs in UK, high cure rates were achieved in the study cohort.

Anticoagulant selection for patients with VTE-Evidence from a systematic literature review of network meta-analyses.

The aim of this study was to systematically review published network meta-analyses (NMAs) that compare venous thromboembolism (VTE) treatments. A systematic literature search (in MEDLINE, Embase, and Cochrane Database of Systematic Reviews through September 2017) was conducted to identify NMAs that compared the safety and efficacy of direct oral anticoagulants (DOACs) for the treatment of VTE in the acute and extended treatment settings. The NMAs included randomized controlled trials comparing multiple DOACs, low-molecular weight heparin, unfractionated heparin, and vitamin K antagonists (VKAs). The quality of the NMA results were evaluated using the Grading of Recommendations and Evaluation (GRADE) assessment. The SLR identified 294 records and nine NMAs (68 trials). Among the NMAs, three evaluated the acute treatment setting, five the extended, and one in both treatment settings. The NMAs showed a significant reduction in major bleeding and clinically relevant bleeding (CRB) with apixaban compared to other DOACs. Major bleeding with apixaban was reduced compared to dabigatran, edoxaban, and fondaparinux-VKA combination in all comparisons in the acute setting (range of effect estimates: 0.30-0.43). CRB was reduced with apixaban compared to dabigatran, edoxaban, and rivaroxaban in the acute and extended settings (range of effect estimates: 0.23-0.72). No significant differences were seen in efficacy outcomes between the DOACs. This SLR of NMAs systematically collected all indirect evidence of the impact of apixaban compared to other anticoagulants in patients with VTE. In the absence of head-to-head trials, well-conducted NMAs provide the best evidence.

Effectiveness of current and future regimens for treating genotype 3 hepatitis C virus infection: a large-scale systematic review.

BACKGROUND: Six distinct genetic variants (genotypes 1 - 6) of hepatitis C virus (HCV) exist globally. Certain genotypes are more prevalent in particular countries or regions than in others but, globally, genotype 3 (GT3) is the second most common. Patients infected with HCV GT1, 2, 4, 5 or 6 recover to a greater extent, as measured by sustained virological response (SVR), following treatment with regimens based on direct-acting antivirals (DAAs) than after treatment with older regimens based on pegylated interferon (Peg-IFN). GT3, however, is regarded as being more difficult to treat as it is a relatively aggressive genotype, associated with greater liver damage and cancer risk; some subgroups of patients with GT3 infection are less responsive to current licensed DAA treatments. Newer DAAs have become available or are in development. METHODS: According to PRISMA guidance, we conducted a systematic review (and descriptive statistical analysis) of data in the public domain from relevant clinical trial or observational (real-world) study publications within a 5-year period (February 2011 to May 2016) identified by PubMed, Medline In-Process, and Embase searches. This was supplemented with a search of five non-indexed literature sources, comprising annual conferences of the AASLD, APASL, CROI, EASL, and WHO, restricted to a 1-year period (April 2015 to May 2016). RESULTS: Of the all-oral regimens, the efficacy (SVR12 ≥ 90%) of sofosbuvir plus daclatasvir- and velpatasvir-based regimens in clinical trials supports and reinforces their recommendation by guidelines. Other promising regimens comprise grazoprevir + elbasvir + sofosbuvir, and ombitasvir + paritaprevir/ribavirin + sofosbuvir. Newer regimens incorporating pibrentasvir + glecaprevir or grazoprevir + ruzasvir + MK-3682 (uprifosbuvir), offer all-oral, ribavirin-free SVR12 rates consistently greater than 95%. Observational studies report slightly lower overall SVR rates but reflect corresponding clinical trial data in terms of treatments most likely to achieve good responses. CONCLUSIONS: On the basis of SVR12, we established that for treating GT3 infections (i) regimens incorporating newer DAAs are more effective than those comprising older DAAs, and (ii) ribavirin may be of less benefit in newer DAA regimens than in older DAA regimens. The analysis provides evidence that DAA regimens can replace Peg-IFN-based regimens for GT3 infection.

Global Prevalence of Chronic Kidney Disease - A Systematic Review and Meta-Analysis.

Chronic kidney disease (CKD) is a global health burden with a high economic cost to health systems and is an independent risk factor for cardiovascular disease (CVD). All stages of CKD are associated with increased risks of cardiovascular morbidity, premature mortality, and/or decreased quality of life. CKD is usually asymptomatic until later stages and accurate prevalence data are lacking. Thus we sought to determine the prevalence of CKD globally, by stage, geographical location, gender and age. A systematic review and meta-analysis of observational studies estimating CKD prevalence in general populations was conducted through literature searches in 8 databases. We assessed pooled data using a random effects model. Of 5,842 potential articles, 100 studies of diverse quality were included, comprising 6,908,440 patients. Global mean(95%CI) CKD prevalence of 5 stages 13·4%(11·7-15·1%), and stages 3-5 was 10·6%(9·2-12·2%). Weighting by study quality did not affect prevalence estimates. CKD prevalence by stage was Stage-1 (eGFR>90+ACR>30): 3·5% (2·8-4·2%); Stage-2 (eGFR 60-89+ACR>30): 3·9% (2·7-5·3%); Stage-3 (eGFR 30-59): 7·6% (6·4-8·9%); Stage-4 = (eGFR 29-15): 0·4% (0·3-0·5%); and Stage-5 (eGFR<15): 0·1% (0·1-0·1%). CKD has a high global prevalence with a consistent estimated global CKD prevalence of between 11 to 13% with the majority stage 3. Future research should evaluate intervention strategies deliverable at scale to delay the progression of CKD and improve CVD outcomes.

A Metabolic Study of Huntington's Disease.

BACKGROUND: Huntington's disease patients have a number of peripheral manifestations suggestive of metabolic and endocrine abnormalities. We, therefore, investigated a number of metabolic factors in a 24-hour study of Huntington's disease gene carriers (premanifest and moderate stage II/III) and controls. METHODS: Control (n = 15), premanifest (n = 14) and stage II/III (n = 13) participants were studied with blood sampling over a 24-hour period. A battery of clinical tests including neurological rating and function scales were performed. Visceral and subcutaneous adipose distribution was measured using magnetic resonance imaging. We quantified fasting baseline concentrations of glucose, insulin, cholesterol, triglycerides, lipoprotein (a), fatty acids, amino acids, lactate and osteokines. Leptin and ghrelin were quantified in fasting samples and after a standardised meal. We assessed glucose, insulin, growth hormone and cortisol concentrations during a prolonged oral glucose tolerance test. RESULTS: We found no highly significant differences in carbohydrate, protein or lipid metabolism markers between healthy controls, premanifest and stage II/III Huntington's disease subjects. For some markers (osteoprotegerin, tyrosine, lysine, phenylalanine and arginine) there is a suggestion (p values between 0.02 and 0.05) that levels are higher in patients with premanifest HD, but not moderate HD. However, given the large number of statistical tests performed interpretation of these findings must be cautious. CONCLUSIONS: Contrary to previous studies that showed altered levels of metabolic markers in patients with Huntington's disease, our study did not demonstrate convincing evidence of abnormalities in any of the markers examined. Our analyses were restricted to Huntington's disease patients not taking neuroleptics, anti-depressants or other medication affecting metabolic pathways. Even with the modest sample sizes studied, the lack of highly significant results, despite many being tested, suggests that the majority of these markers do not differ markedly by disease status.