Real-world evidence of epidemiology, patient characteristics, and mortality in people with drug-resistant epilepsy in the United Kingdom, 2011-2021.

BACKGROUND: A third of people with epilepsy are drug resistant. People with drug-resistant epilepsy (DRE) have a higher risk of mortality and physical injuries than those who respond to anti-seizure medication (ASM). This study describes patient characteristics, comorbidities, and mortality in people with DRE in the UK. METHODS: The Clinical Practice Research Datalink was utilised to select people with DRE prescribed a third ASM between 1 January 2011 and 31 March 2021. Annual incidence and prevalence of DRE, patient characteristics, comorbidities, and mortality rates were analysed. Subgroup analysis was performed by age, sex, presence of intellectual disabilities and time from epilepsy diagnosis to DRE. RESULTS: A total of 34,647 people with DRE were included (mean ± SD age 42.68 ± 23.59 years, 52.6% females). During the study period, annual DRE incidence ranged from 1.99% to 3.12%. As of 31 March 2021, DRE prevalence was 26.6% (95% confidence interval [CI] 26.3%-26.8%). A greater proportion of people with DRE resided in the most deprived regions, with 21.1% and 16.7% in the top two quintiles of the Index of Multiple Deprivation respectively, compared to < 15% in the three less deprived regions. All-cause mortality ranged from 3,687 to 4,802 per 100,000 persons with DRE, four times higher than that in the general population in the UK. Variations existed across subgroups. CONCLUSIONS: Considerable disease burden was observed in people with DRE in the UK. The findings emphasise the importance of early DRE diagnosis and appropriate disease management in people who develop DRE.

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.

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.

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.

Fluorescent functionalised naphthalimides and their Au(i)-NHC complexes for potential use in cellular bioimaging.

A series of cationic, dihydroimidazolinium-functionalized 1,8-naphthalimide fluorophores have been isolated as their hexafluorophosphate salts, [HL]PF6. These pro-ligands react with [AuCl(tht)] in the presence of base to form N-heterocyclic carbene (NHC) complexes, [AuCl(L)]. Two X-ray structures represent a pro-ligand and complex pairing: the latter reveals the two-coordinate linear geometry of the NHC-Au(i) species, as well as intermolecular interactions supported by both ligand π-π stacking and a weak aurophilic interaction of 3.3205(6) Å. The luminescence properties of the pro-ligands and complexes are dominated by the ICT character of the substituted fluorophore at ca. 500 nm, which is further modulated via functionalization at the 4-position of the naphthalimide. Cytotoxicity assessments were performed for all [HL]PF6 and [AuCl(L)] species against LOVO, MCF-7, A549 and PC3 cell lines; added lipophilicity seems to correlate with increased cytotoxicity. Confocal fluorescence microscopy was undertaken on a selected [HL]PF6 and [AuCl(L)] species and showed that the intracellular distribution is dependent upon the specific ligand structure. More detailed co-localisation studies show that selected examples present a predominant lysosomal staining pattern. FLIM studies exemplified the applicability of these probes, and secondly suggest that fluorescence lifetime could be used to provide information on the integrity of the complex and thus liberation of gold in a biological environment.

Synthesis and characterisation of fluorescent aminophosphines and their coordination to gold(i).

Three novel fluorescent aminophosphine ligands have been synthesised that incorporate napthyl (L1), pyrenyl (L2) and anthraquinone (L3) chromophores into their structures. The ligands react with [AuCl(tht)] (tht = tetrahydrothiophene) to give neutral complexes of the form [AuCl(L1-3)]. Solid state, X-ray crystallographic data was obtained for the anthraquinone derivative, [AuCl(L3)], and showed a distorted linear coordination geometry at Au(i). The packing structure also revealed a number of intermolecular π-π interactions that involve the anthraquinone and phenyl units of the aminophosphine ligand. 31P NMR spectroscopic data revealed δP values of +42.2 (L1), +42.1 (L2) and +26.1 (L3) ppm, which shifted downfield upon coordination to Au(i) to +64.6, +64.7, and +55.8 ppm, respectively. Supporting TD-DFT studies were able to reproduce the structure and 31P NMR chemical shifts of [AuCl(L3)] as well as rationalise the HOMO-LUMO compositions. Photophysical studies showed that the appended fluorophore dominates the absorption and emission properties for the ligands and complexes, with the anthraquinone derivatives showing visible emission at ca. 570 nm which was attributed to the intramolecular charge transfer character of the phosphinoaminoanthraquinone fragment.

From Ligand to Phosphor: Rapid, Machine-Assisted Synthesis of Substituted Iridium(III) Pyrazolate Complexes with Tuneable Luminescence.

A first-generation machine-assisted approach towards the preparation of hybrid ligand/metal materials has been developed. A comparison of synthetic approaches demonstrates that incorporation of both flow chemistry and microwave heating can be successfully applied to the rapid synthesis of a range of new phenyl-1H-pyrazoles (ppz) substituted with electron-withdrawing groups (-F, -CF3 , -OCF3 , and -SF5 ). These, in turn, can be translated into heteroleptic complexes, [Ir(ppz)2 (bipy)]BF4 (bipy=2,2'-bipyridine). Microwave-assisted synthesis for the IrIII complexes allows isolation of spectroscopically pure species in less than 1 h of reaction time starting from IrCl3 . All of the new complexes have been characterised photophysically (including nanosecond time-resolved transient absorption spectroscopy), electrochemically, and by TD-DFT studies. The complexes exhibit ligand-dependent, tuneable, green-yellow luminescence (500-560 nm), with quantum yields in the range 5-15 %.

Cationic, luminescent cyclometalated iridium(III) complexes based on substituted 2-phenylthiazole ligands.

Ten cationic heteroleptic iridium(III) complexes, [Ir(emptz)2(N^N)](PF6) were prepared from a cyclometalated iridium bridged-chloride dimer involving two ethyl-4-methylphenylthiazole-5-carboxylate (emptz) ligands. One X-ray crystallographic study was undertaken where the ancillary N^N ligand was 4,7-diphenyl-1,10-phenanthroline and revealed the anticipated structure, showing a distorted octahedral coordination geometry at Ir(III). The complexes were visibly luminescent with modestly structured emission at 540-590 nm and lifetimes (60-340 ns) consistent with phosphorescence. TD-DFT calculations suggest that strong MLCT character contributes to the visible absorption characteristics, whilst the moderately structured emission profiles indicate a (3)MLCT/(3)IL admixture of states to the phosphorescence.