Grouping sounds into evolving units for the purpose of historical language comparison.

Computer-assisted approaches to historical language comparison have made great progress during the past two decades. Scholars can now routinely use computational tools to annotate cognate sets, align words, and search for regularly recurring sound correspondences. However, computational approaches still suffer from a very rigid sequence model of the form part of the linguistic sign, in which words and morphemes are segmented into fixed sound units which cannot be modified. In order to bring the representation of sound sequences in computational historical linguistics closer to the research practice of scholars who apply the traditional comparative method, we introduce improved sound sequence representations in which individual sound segments can be grouped into evolving sound units in order to capture language-specific sound laws more efficiently. We illustrate the usefulness of this enhanced representation of sound sequences in concrete examples and complement it by providing a small software library that allows scholars to convert their data from forms segmented into sound units to forms segmented into evolving sound units and vice versa.

In linguistics, it is difficult to clearly draw the boundaries between the sounds in individual words. What one linguist may analyze as two sounds, another linguist might analyze at just one sound. Since the segmentation of words into sounds is crucial for many analyses in linguistics and since no perfect solution can be found, we offer a new representation that allows scholars to analyze the sounds in a word in a more flexible way that conforms to general standards while at the same time giving linguists enough flexibility to advance individual analyses.

The balancing act between high electronic and low ionic transport influenced by perovskite grain boundaries.

A better understanding of the materials' fundamental physical processes is necessary to push hybrid perovskite photovoltaic devices towards their theoretical limits. The role of the perovskite grain boundaries is essential to optimise the system thoroughly. The influence of the perovskite grain size and crystal orientation on physical properties and their resulting photovoltaic performance is examined. We develop a novel, straightforward synthesis approach that yields crystals of a similar size but allows the tuning of their orientation to either the (200) or (002) facet alignment parallel to the substrate by manipulating dimethyl sulfoxide (DMSO) and tetrahydrothiophene-1-oxide (THTO) ratios. This decouples crystal orientation from grain size, allowing the study of charge carrier mobility, found to be improved with larger grain sizes, highlighting the importance of minimising crystal disorder to achieve efficient devices. However, devices incorporating crystals with the (200) facet exhibit an s-shape in the current density-voltage curve when standard scan rates are used, which typically signals an energetic interfacial barrier. Using the drift-diffusion simulations, we attribute this to slower-moving ions (mobility of 0.37 × 10-10 cm2 V-1 s-1) in combination with a lower density of mobile ions. This counterintuitive result highlights that reducing ion migration does not necessarily minimise hysteresis.

N,N'-Diaryl-Sulfurdiimides are Strongly Redox Tuned.

The synthesis and extensive characterization of nine aryl sulfur diimides (SDIs, Ar-NSN-Ar) are presented with a robust computational and experimental investigation of the fundamental properties of these important members of the thiazyl family of compounds, with particular attention paid to their highly tunable electrochemical behaviour. This is the first work to undertake a systematic comparison of the electrochemical profiles of a coherent series of SDIs to demonstrate and quantify the response of their reduction potentials to substituent electron-donating and -withdrawing properties. This effect is found to be not only exceptionally strong, but also correlates very closely with computed orbital energies. Electron paramagnetic resonance spectroscopy is used to determine the nature, localization, and qualitative lifetimes of the radical anions of SDIs. This work also addresses significant misconceptions about physical properties of SDIs. Experimental data and modern computational methods are employed to provide a resolute answer to the long-standing contention of the solution-state conformations of SDIs, and to correct historical mistakes in the assignment of infrared spectroscopic data. High-quality crystal structures of all SDIs in this work showcase the utility of the recently introduced structural refinement software NoSpherA2, enabling full anisotropic refinement of H-atoms with accurate C-H bond lengths.

Post transition metal substituted Keggin-type POMs as thin film chemiresistive sensors for H2O and CO2 detection.

Chemiresitive sensing allows the affordable and facile detection of small molecules such as H2O and CO2. Herein, we report a novel class of Earth-abundant post transition metal substituted Keggin polyoxometalates (POMs) for chemiresistive sensing applications, with conductivities up to 0.01 S cm-1 under 100% CO2 and 65% Relative Humidity (RH).

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.

Whole genome sequencing of human Borrelia burgdorferi isolates reveals linked blocks of accessory genome elements located on plasmids and associated with human dissemination.

Lyme disease is the most common vector-borne disease in North America and Europe. The clinical manifestations of Lyme disease vary based on the genospecies of the infecting Borrelia burgdorferi spirochete, but the microbial genetic elements underlying these associations are not known. Here, we report the whole genome sequence (WGS) and analysis of 299 B. burgdorferi (Bb) isolates derived from patients in the Eastern and Midwestern US and Central Europe. We develop a WGS-based classification of Bb isolates, confirm and extend the findings of previous single- and multi-locus typing systems, define the plasmid profiles of human-infectious Bb isolates, annotate the core and strain-variable surface lipoproteome, and identify loci associated with disseminated infection. A core genome consisting of ~900 open reading frames and a core set of plasmids consisting of lp17, lp25, lp36, lp28-3, lp28-4, lp54, and cp26 are found in nearly all isolates. Strain-variable (accessory) plasmids and genes correlate strongly with phylogeny. Using genetic association study methods, we identify an accessory genome signature associated with dissemination in humans and define the individual plasmids and genes that make up this signature. Strains within the RST1/WGS A subgroup, particularly a subset marked by the OspC type A genotype, have increased rates of dissemination in humans. OspC type A strains possess a unique set of strongly linked genetic elements including the presence of lp56 and lp28-1 plasmids and a cluster of genes that may contribute to their enhanced virulence compared to other genotypes. These features of OspC type A strains reflect a broader paradigm across Bb isolates, in which near-clonal genotypes are defined by strain-specific clusters of linked genetic elements, particularly those encoding surface-exposed lipoproteins. These clusters of genes are maintained by strain-specific patterns of plasmid occupancy and are associated with the probability of invasive infection.

Ionic Accumulation as a Diagnostic Tool in Perovskite Solar Cells: Characterizing Band Alignment with Rapid Voltage Pulses.

Despite record-breaking devices, interfaces in perovskite solar cells are still poorly understood, inhibiting further progress. Their mixed ionic-electronic nature results in compositional variations at the interfaces, depending on the history of externally applied biases. This makes it difficult to measure the band energy alignment of charge extraction layers accurately. As a result, the field often resorts to a trial-and-error process to optimize these interfaces. Current approaches are typically carried out in a vacuum and on incomplete cells, hence values may not reflect those found in working devices. To address this, a pulsed measurement technique characterizing the electrostatic potential energy drop across the perovskite layer in a functioning device is developed. This method reconstructs the current-voltage (JV) curve for a range of stabilization biases, holding the ion distribution "static" during subsequent rapid voltage pulses. Two different regimes are observed: at low biases, the reconstructed JV curve is "s-shaped", whereas, at high biases, typical diode-shaped curves are returned. Using drift-diffusion simulations, it is demonstrated that the intersection of the two regimes reflects the band offsets at the interfaces. This approach effectively allows measurements of interfacial energy level alignment in a complete device under illumination and without the need for expensive vacuum equipment.

The association between brain volume loss and disability in multiple sclerosis: A systematic review.

BACKGROUND: Multiple sclerosis (MS) is a chronic, inflammatory, demyelinating, degenerative disease of the central nervous system that affects approximately 2.8 million people worldwide. Compelling evidence from observational studies and clinical trials indicates a strong association between brain volume loss (BVL) and the accumulation of disability in MS. However, the considerable heterogeneity in study designs and methods of assessment of BVL invites questions concerning the generalizability of the reported findings. Therefore, we conducted this systematic review to characterize the relationship between BVL and physical disability in patients with MS. METHODS: A systematic literature search of MEDLINE and EMBASE databases was performed supplemented by gray literature searches. The following study designs were included: prospective/retrospective cohort, cross-sectional and case-control. Only English language articles published from 2010 onwards were eligible for final inclusion. There were no restrictions on MS subtype, age, or ethnicity. Of the 1620 citations retrieved by the structured searches, 50 publications met our screening criteria and were included in the final data set. RESULTS: Across all BVL measures, there was considerable heterogeneity in studies regarding the underlying study population, the definitions of BVL and image analysis methodologies, the physical disability measure used, the measures of association reported and whether the analysis conducted was univariable or multivariable. A total of 36 primary studies providing data on the association between whole BVL and physical disability in MS collectively suggest that whole brain atrophy is associated with greater physical disability progression in MS patients. Similarly, a total of 15 primary studies providing data on the association between ventricular atrophy and physical disability in MS suggest that ventricular atrophy is associated with greater physical disability progression in MS patients. Along similar lines, the existing evidence based on a total of 13 primary studies suggests that gray matter atrophy is associated with greater physical disability progression in MS patients. Four primary studies suggest that corpus callosum atrophy is associated with greater physical disability progression in MS patients. The majority of the existing evidence (6 primary studies) suggests no association between white matter atrophy and physical disability in MS. It is difficult to assign a relationship between basal ganglia volume loss and physical disability as well as medulla oblongata width and physical disability in MS due to very limited data. CONCLUSION: The evidence gathered from this systematic review, although very heterogeneous, suggests that whole brain atrophy is associated with greater physical disability progression in MS patients. Our review can help define future imaging biomarkers for physical disability progression and treatment monitoring in MS.

Whole genome sequencing of Borrelia burgdorferi isolates reveals linked clusters of plasmid-borne accessory genome elements associated with virulence.

Lyme disease is the most common vector-borne disease in North America and Europe. The clinical manifestations of Lyme disease vary based on the genospecies of the infecting Borrelia burgdorferi spirochete, but the microbial genetic elements underlying these associations are not known. Here, we report the whole genome sequence (WGS) and analysis of 299 patient-derived B. burgdorferi sensu stricto ( Bbss ) isolates from patients in the Eastern and Midwestern US and Central Europe. We develop a WGS-based classification of Bbss isolates, confirm and extend the findings of previous single- and multi-locus typing systems, define the plasmid profiles of human-infectious Bbss isolates, annotate the core and strain-variable surface lipoproteome, and identify loci associated with disseminated infection. A core genome consisting of ∻800 open reading frames and a core set of plasmids consisting of lp17, lp25, lp36, lp28-3, lp28-4, lp54, and cp26 are found in nearly all isolates. Strain-variable (accessory) plasmids and genes correlate strongly with phylogeny. Using genetic association study methods, we identify an accessory genome signature associated with dissemination and define the individual plasmids and genes that make up this signature. Strains within the RST1/WGS A subgroup, particularly a subset marked by the OspC type A genotype, are associated with increased rates of dissemination. OspC type A strains possess a unique constellation of strongly linked genetic changes including the presence of lp56 and lp28-1 plasmids and a cluster of genes that may contribute to their enhanced virulence compared to other genotypes. The patterns of OspC type A strains typify a broader paradigm across Bbss isolates, in which genetic structure is defined by correlated groups of strain-variable genes located predominantly on plasmids, particularly for expression of surface-exposed lipoproteins. These clusters of genes are inherited in blocks through strain-specific patterns of plasmid occupancy and are associated with the probability of invasive infection.

Accurate Crystal Structures of C12H9CN, C12H8(CN)2, and C16H11CN Valence Isomers Using Nonspherical Atomic Scattering Factors.

Fourteen crystal structures, mostly from good-quality datasets but including some marginal and twinned exemplars, from a series of novel polycyclic benzo- and naphtho-fused organic nitriles are presented and accurately described, including some related structures of a bromo-substituted and partially hydrogenated analogues. These structures represent a considerable increase in the number of published structures within their archetypes. This work highlights the significant advancement in structural refinement software proffered by NoSpherA2, which enables Hirshfeld Atom Refinement (HAR) of the structures within Olex2 v1.5. This results in the determination of C-H bond lengths with near to neutron diffraction accuracies at far lower experimental cost, and with an average improvement in C-C bond precision of 42% compared to Independent Atom Model refinements. H-atoms (apart from disordered components) refined well with anisotropic displacement parameters. Nonclassical H-bonding (C-H···N≡C) in this series is analyzed, and dipolar nitrile-nitrile interactions (C≡N···C≡N) in three major motifs described by (Wood, P. A. Acta Cryst. B, 2008, 64, 393-396) are found in 9 out of 13 nitrile-containing compounds of this series, a much higher proportion than the global average of 21% of nitrile-containing compounds. The HAR/NoSpherA2 approach shows increasing benefits with better data quality without apparent discontinuities.

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.

Correlation of Vital Signs and Depth of Sedation by Modified Observer's Assessment of Alertness and Sedation (MOAA/S) Scale in Bronchoscopy.

BACKGROUND: With complex, lengthy bronchoscopies, there is a need for safe, effective sedation. Most bronchoscopists strive for moderate sedation, though often difficult without compromising vital signs. The Modified Observer's Assessment of Alertness and Sedation (MOAA/S) scale is a validated 6-point scale assessing responsiveness of patients coinciding with the American Society of Anesthesiologists (ASA) continuum of sedation. It is commonly used in studying bronchoscopic sedation, but depth of sedation by MOAA/S and correlation with vital signs and adverse events has not been determined. METHODS: This study was a post hoc analysis of a prospective, double-blind, randomized trial evaluating the safety and efficacy of remimazolam. MOAA/S and corresponding vital signs were used to assess the effect of the level of sedation on vital signs and adverse events. RESULTS: A total of 23,341 MOAA/S scores from 431 patients were recorded. Older and higher ASA class patients spent more time in deeper sedation (MOAA/S 0 to 1) (6% vs. 2%, P=0.01). Oxygen saturation was equal in deep sedation (MOAA/S 0 to 1) (97±3%) compared with moderate sedation (96±3%) (P=0.11). Mean systolic and diastolic blood pressures were significantly lower when comparing MOAA/S 0 to 1 to MOAA/S 5 (systolic blood pressure: 126±19 vs. 147±24 mm Hg, P<0.01; diastolic blood pressure: 68±14 vs. 84±15 mm Hg, P<0.01). There was a nonsignificant trend towards lower heart rate at deep versus moderate sedation (84±15 vs. 94±18 beats/min, P=0.07). Respiratory rate was also comparable with moderate and deep sedation (17±5 vs. 18±6 beats/min, P=0.94). CONCLUSION: There was no clinically meaningful correlation between vital signs and depth of sedation assessed by MOAA/S. Older and higher ASA class patients spend more time in deeper sedation. However, when in deep sedation, there was no difference in vital signs other than a slightly increased incidence of clinically insignificant hypotension.

Exploring the Effects of a Golf Program on Psychological and Social Wellbeing for People With Dementia, Carers, and Staff.

There is a paucity of evidence regarding the effects of sport and physical activity on wellbeing in dementia. The present study is the first known study to involve golf with this population. People with dementia (n = 10) and carers (n = 5) participated in a 6-week golf program, facilitated by golf center staff (n = 3). Multiple avenues of data collection were utilized and were subject to thematic analysis. Five central themes were identified: emotion, respite, losing the "dementia" label, friendship/camaraderie, and improving relationships. Underlying subthemes were also identified, while potential links between themes were highlighted. Themes were also considered in terms of which participants (person with dementia, carer, and staff) provided evidence for each theme. This study presents preliminary support for the role of golf to enhance the psychological and social wellbeing of people with early stages of dementia, carers, and staff. Potential mechanisms and future research are discussed.

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.