Use of HPV testing for cervical screening in vaccinated women - insights from the SHEVa (Scottish HPV Prevalence in Vaccinated Women) study

The management of cervical disease is changing worldwide as a result of HPV vaccination and the increasing use of HPV testing for cervical screening. However, the impact of vaccination on the performance of HPV based screening strategies is unknown. The SHEVa (Scottish HPV Prevalence in Vaccinated women) projects are designed to gain insight into the impact of vaccination on the performance of clinically validated HPV assays. Samples collated from women attending for first cervical smear who had been vaccinated as part of a national “catch up” programme were tested with three clinically validated HPV assays (2 DNA and 1 RNA). Overall HR-HPV and type specific positivity was assessed in total population and according to underlying cytology and compared to a demographically equivalent group of unvaccinated women. HPV prevalence was significantly lower in vaccinated women and was influenced by assay-type, reducing by 23-25% for the DNA based assays and 32% for the RNA assay (p=0.0008). All assays showed over 75% reduction of HPV16 and/or 18 (p<0.0001) whereas the prevalence of non 16/18 HR-HPV was not significantly different in vaccinated vs unvaccinated women. In women with low grade abnormalities, the proportion associated with non 16/18 HR-HPV was significantly higher in vaccinated women (p<0.0001). Clinically validated HPV assays are affected differentially when applied to vaccinated women, dependent on assay chemistry. The increased proportion of non HPV16 /18 infections may have implications for clinical performance, consequently, longitudinal studies linking HPV status to disease outcomes in vaccinated women are warranted

Effective use of evolutionary computation to parameterise an epidemiological model

Predictive epidemiological models are able to be used most effectively when they have first been shown to fit historical data. Finding the right parameters settings for a model is complex: the system is likely to be noisy, the data points may be sparse, and there may be many inter-related parameters. We apply computational intelligence and data mining techniques in novel ways to investigate this significant problem. We construct an original computational model of human papilloma virus and cervical intraepithelial neoplasia with the ultimate aim of predicting the outcomes of varying control techniques (e.g. vaccination, screening, treatment, quarantine). Two computational intelligence techniques (genetic algorithms and particle swarm optimisation) are used over one- stage and two-stage optimisations for eight real-valued model parameters. Rigorous comparison over a variety of quantitative measures demonstrates the explorative nature of the genetic algorithm (useful in this parameter space to support the modeller). Correlations between parameters are drawn out that might otherwise be missed. Clustering highlights the uniformity of the best genetic algorithm results. Prediction of gender-neutral vaccination with the tuned model suggests elimination of the virus across vaccinated and cross-protected strains, supporting recent Scottish government policy. This preliminary study lays the foundation for more widespread use of computational intelligence techniques in epidemiological modelling

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

Population-Level Effects of Human Papillomavirus Vaccination Programs on Infections with Nonvaccine Genotypes

We analyzed human papillomavirus (HPV) prevalences during prevaccination and postvaccination periods to consider possible changes in nonvaccine HPV genotypes after introduction of vaccines that confer protection against 2 high-risk types, HPV16 and HPV18. Our meta-analysis included 9 studies with data for 13,886 girls and women ≤19 years of age and 23,340 women 20–24 years of age. We found evidence of cross-protection for HPV31 among the younger age group after vaccine introduction but little evidence for reductions of HPV33 and HPV45. For the group this same age group, we also found slight increases in 2 nonvaccine high-risk HPV types (HPV39 and HPV52) and in 2 possible high-risk types (HPV53 and HPV73). However, results between age groups and vaccines used were inconsistent, and the increases had possible alternative explanations; consequently, these data provided no clear evidence for type replacement. Continued monitoring of these HPV genotypes is important

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 12‑lead 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

Impact of partial bivalent HPV vaccination on vaccine-type infection; a population-based analysis

Background: Data on the effectiveness of 1 dose of HPV vaccine are lacking, particularly in population-based settings. Data from a national HPV immunisation catch-up programme of 14-18 year old girls were used to assess the effectiveness of < 3 doses of the bivalent vaccine on vaccine-type and cross reactive-type HPV infection. Methods: Cervical samples from women attending for their first cervical smear which had been genotyped for HPV as part of a longitudinal HPV surveillance programme were linked to immunisation records to establish the number of vaccine doses (0,1,2,3) administered. Vaccine effectiveness (VE) adjusted for deprivation and age at first dose, was assessed for prevalent HPV 16/18 and HPV 31/33/45 infection.Results: VE for prevalent HPV 16/18 infection associated with 1, 2 and 3 doses was 48.2% (95% CI 16.8-68.9), 54.8% (95% CI 30.7-70.8) and 72.8% (95% CI 62.8-80.3). Equivalent VE for prevalent HPV 31/33/45 infection was -1.62% (95% CI -85.1 – 45.3), 48.3 % (95% CI 7.6 -71.8) and 55.2 % (95% CI 32.6-70.2).Conclusion: Consistent with recent aggregated trial data, we demonstrate the potential effectiveness of even one dose of HPV vaccine on vaccine type infection. Given that these women were immunised as part of a catch-up campaign, the VE observed in this study is likely to be an underestimate of what will occur in girls vaccinated at younger ages. Further population-based studies which look at the clinical efficacy of one dose schedules arewarranted

Role of 20-Hydroxyeicosatetraenoic Acid in Mediating Hypertension in Response to Chronic Renal Medullary Endothelin Type B Receptor Blockade

BACKGROUND: The renal medullary endothelin (ET-1) system plays an important role in the control of sodium excretion and arterial pressure (AP) through the activation of renal medullary ET-B receptors. We have previously shown that blockade of endothelin type B receptors (ET-B) leads to salt-sensitive hypertension through mechanisms that are not fully understood. One possible mechanism is through a reduction in renal medullary production of 20-hydroxyeicosatetraenoic acid (20-HETE). 20-HETE, a metabolite of arachidonic acid, has natriuretic properties similar to ET-B activation. While these findings suggest a possible interaction between ET-B receptor activation and 20-HETE production, it is unknown whether blockade of medullary ET-B receptors in rats maintained on a high sodium intake leads to reductions in 20-HETE production. METHODOLOGY/PRINCIPAL FINDINGS: The effect of increasing sodium intake from low (NS = .8%) to high (HS = 8%) on renal medullary production of 20-HETE in the presence and absence of renal medullary ET-B receptor antagonism was examined. Renal medullary blockade of ET-B receptors resulted in salt sensitive hypertension. In control rats, blood pressure rose from 112.8±2.4 mmHg (NS) to 120.7±9.3 mmHg (HS). In contrast, when treated with an ET-B receptor blocker, blood pressure was significantly elevated from 123.7±3.2 (NS) to 164.2±7.1 (HS). Furthermore, increasing sodium intake was associated with elevated medullary 20-HETE (5.6±.8 in NS vs. 14.3±3.7 pg/mg in HS), an effect that was completely abolished by renal medullary ET-B receptor blockade (4.9±.8 for NS and 4.5±.6 pg/mg for HS). Finally, the hypertensive response to intramedullary ET-B receptor blockade was blunted in rats pretreated with a specific 20-HETE synthesis inhibitor. CONCLUSION: These data suggest that increases in renal medullary production of 20-HETE associated with elevating salt intake may be, in part, due to ET-B receptor activation within the renal medulla

X-Ray Spectroscopy of Stars

(abridged) Non-degenerate stars of essentially all spectral classes are soft X-ray sources. Low-mass stars on the cooler part of the main sequence and their pre-main sequence predecessors define the dominant stellar population in the galaxy by number. Their X-ray spectra are reminiscent, in the broadest sense, of X-ray spectra from the solar corona. X-ray emission from cool stars is indeed ascribed to magnetically trapped hot gas analogous to the solar coronal plasma. Coronal structure, its thermal stratification and geometric extent can be interpreted based on various spectral diagnostics. New features have been identified in pre-main sequence stars; some of these may be related to accretion shocks on the stellar surface, fluorescence on circumstellar disks due to X-ray irradiation, or shock heating in stellar outflows. Massive, hot stars clearly dominate the interaction with the galactic interstellar medium: they are the main sources of ionizing radiation, mechanical energy and chemical enrichment in galaxies. High-energy emission permits to probe some of the most important processes at work in these stars, and put constraints on their most peculiar feature: the stellar wind. Here, we review recent advances in our understanding of cool and hot stars through the study of X-ray spectra, in particular high-resolution spectra now available from XMM-Newton and Chandra. We address issues related to coronal structure, flares, the composition of coronal plasma, X-ray production in accretion streams and outflows, X-rays from single OB-type stars, massive binaries, magnetic hot objects and evolved WR stars.Comment: accepted for Astron. Astrophys. Rev., 98 journal pages, 30 figures (partly multiple); some corrections made after proof stag

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