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Background As the availability of genomic testing grows, variant interpretation will increasingly be performed by genomic generalists, rather than domain-specific experts. Demand is rising for laboratories to accurately classify variants in inherited cardiac condition (ICC) genes, including secondary findings. Methods We analyse evidence for inheritance patterns, allelic requirement, disease mechanism and disease-relevant variant classes for 65 ClinGen-curated ICC gene-disease pairs. We present this information for the first time in a structured dataset, CardiacG2P, and assess application in genomic variant filtering. Results For 36/65 gene-disease pairs, loss of function is not an established disease mechanism, and protein truncating variants are not known to be pathogenic. Using the CardiacG2P dataset as an initial variant filter allows for efficient variant prioritisation whilst maintaining a high sensitivity for retaining pathogenic variants compared with two other variant filtering approaches. Conclusions Access to evidence-based structured data representing disease mechanism and allelic requirement aids variant filtering and analysis and is a pre-requisite for scalable genomic testing.

IntroductionThere is uncertainty about the effectiveness of oral fluid restriction in patients with acute heart failure treated with intravenous loop diuretics, due to a lack of high-quality evidence from randomised controlled trials (RCTs). RCTs of non-pharmacological therapies are challenging and costly to undertake in the acute setting using conventional approaches. We sought to investigate the feasibility of conducting a pragmatic RCT that was integrated into the electronic health record (EHR), in the setting of acute unplanned care. Our primary aim was to determine the feasibility of using an automated, interruptive alert to invite clinicians to enrol patients into an RCT of fluid restriction in patients presenting with fluid overload.MethodsTHIRST Alert was a single-centre parallel group, open-label, randomised controlled trial conducted to pilot a novel and efficient approach to trial conduct in a hospital setting. Patient screening, recruitment, randomisation, and outcome ascertainment were all conducted through the EHR and routine care processes. A proportionate model of verbal opt-out consent was used. Over a 6-month period, clinicians who prescribed more than one dose of intravenous furosemide within 48 hours of an unplanned patient admission were exposed to an alert which invited them to assess whether their patient was suitable for inclusion into the trial. Patients <18 and those admitted to the care of surgical or maternity teams were excluded. Enrolled patients underwent simple 1:1 randomisation by the EHR, and were allocated to either oral fluid restriction of 1L/ day or no oral fluid restriction. The co-primary outcome measures were the number of patients enrolled and the documented difference in oral fluid intake between the intervention and control group in the 48 hours after randomised allocation. The trial did not involve any additional investigator input or patient follow-up.ResultsBetween 3 May 2023 and 1 November 2023, a total of 1,191 alerts were triggered. 23 of 141 eligible patients (16%) were enrolled on the trial by routine care clinicians (table 1). In 21 of 23 patients (91%), there was evidence of adherence to the randomised treatment allocation: a clinical order concordant with the randomised allocation was recorded in the health record in 12/12 patients allocated to fluid restriction arm and 9/11 patients allocated to no restriction (figure 1). For 19/23 (83%) of the patients enrolled in the study, heart failure was included in the hospital episode statistics for the admission. In the intention to treat (ITT) analysis, the documented oral intake of the restricted group was 1170 ml (930–1620 ml IQR) and 650 ml (75–1100 ml IQR) in the unrestricted group. The mean number of documented entries for the primary outcome measure of fluid intake was 6.6 for the restricted group and 4.9 for the unrestricted group.ConclusionsTo our knowledge, the THIRST Alert trial is one of the first pragmatic RCTs delivered entirely through the electronic health record system and without the direct intervention of a research team. Our study demonstrates the feasibility of conducting low-cost and efficient trials during the routine care process to generate evidence that can inform practice and improve patient outcomes. Paradoxically, we observed a higher documented fluid intake in the fluid restriction group which may indicate differences in measurement and documentation in patients with active treatments. Further studies are required to determine whether oral fluid restriction is an effective adjunct to diuretic treatment in this setting or if it is a low-value intervention that contributes to care complexity and patient thirst without clinical benefit. Trial registration: NCT05869656. Funded by NIHR UCLH Biomedical Research Centre.Abstract 157 Table 1Alert and user characteristics. The alert design allowed users to select either ‘Yes’, ‘No’ or ‘Dismiss’ when invited to enrol the patient into the trial. Yes/No replies silenced the alert. ‘Dismiss’ allowed the alert to be presented again, if <48 hours had elapsed. *Many clinicians exposed to the alert clicked dismiss during the study period. IQR = interquartile range Admissions with patient enrolment (n=23) Admissions without patient enrolment (n=122) Total number of alerts 112 1079 Alerts per admission (IQR) 2 (1–6) 7.5 (4–12) Clinicians exposed to alert* 46 207 Foundation year doctor (%) 16 (35) 44 (21) Specialty registrar (%) 24 (52) 133 (64) Consultant (%) 6(13) 30 (15) Abstract 157 Figure 1CONSORT flow diagram. The alert was interruptive and presented to medical doctors based on trigger conditions in the EHR. Adherence to treatment allocation was determined by nursing and physician acknowledgement orders. The trial finished at the point of hospital discharge. EHR = electronic health recordConflict of InterestNone

Objective: Electronic Health Records (EHR) are a rich source of information on human diseases, but the information is variably structured, fragmented, curated using different coding systems and collected for purposes other than medical research. We describe an approach for developing, validating and sharing reproducible phenotypes from national structured EHR in the United Kingdom (UK) with applications for translational research. Materials and Methods: We implemented a rule-based phenotyping framework, with up to six approaches of validation. We applied our framework to a sample of 15 million individuals in a national EHR data source (population-based primary care, all ages) linked to hospitalization and death records in England. Data comprised continuous measurements e.g. blood pressure, medication information and coded diagnoses, symptoms, procedures and referrals, recorded using five controlled clinical terminologies: a) Read (primary care, subset of SNOMED-CT), b) International Classification of Diseases 9th/10th Revision (ICD-9, ICD-10, secondary care diagnoses and cause of mortality), c) OPCS Classification of Interventions and Procedures (OPCS-4, hospital surgical procedures) and d) Gemscript Drug Codes. Results: Using the CALIBER phenotyping framework, we created algorithms for 51 diseases, syndromes, biomarkers and lifestyle risk factors and provide up to six validation approaches. The EHR phenotypes are curated in the open-access CALIBER Portal (https://www.caliberresearch.org/portal) and have been used by 40 national/international research groups in 60 peer-reviewed publications. Conclusion: We describe a UK EHR phenomics approach within the CALIBER EHR data platform with initial evidence of validity and use, as an important step towards international use of UK EHR data for health research. Footnotes * - Included NPV, sensitivity and specificity figures for expert case note review - Added overview of algorithm validation appoach - Provided definition of reproducibility and described context - Updated and corrected author affiliations - Corrected minor typographical mistakes - Updated and corrected funding information

Target identification (identifying the correct drug targets for each disease) and target validation (demonstrating the effect of target perturbation on disease biomarkers and disease end-points) are essential steps in drug development. We showed previously that biomarker and disease endpoint associations of single nucleotide polymorphisms (SNPs) in a gene encoding a drug target accurately depict the effect of modifying the same target with a pharmacological agent; others have shown that genomic support for a target is associated with a higher rate of drug development success. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome wide association studies (GWAS) to an updated set of genes encoding druggable human proteins, to compounds with bioactivity against these targets and, where these were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, to enable druggable genome-wide association studies for drug target selection and validation in human disease.

Background: We characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9. Methods: Published and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Fourteen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentration. Results: The PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95%CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95%CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95%CI 0.57; 1.22) for the GS, compared to 0.85 (95%CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95%CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer's disease - outcomes for which large-scale trial data were unavailable. Conclusions: Genetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. Apparent discordance between genetic associations and trial outcome for T2DM might be explained lack by a of statistical precision, or differences in the nature and duration of genetic versus pharmacological perturbation of PCSK9.

Accurate and reproducible phenotyping is essential for large-scale biomedical research. However, developing robust phenotype definitions in biobanks is challenging due to diverse data sources and varying medical ontologies. As a result, the current phenotyping landscape is fragmented. We developed a computational framework to harmonize electronic health record (EHR) data, participant questionnaires, and clinical registry information, defining 313 disease phenotypes among 502,356 UK Biobank (UKB) participants. Our method integrated four medical ontologies (Read v2, CTV3, ICD-10, OPCS-4) across seven data sources, including primary care, hospital admissions, cancer and death registries, and self-reported data on diseases, procedures, and medication. Phenotypes underwent multi-layered validation, assessing data source concordance, age-sex incidence and prevalence patterns, external comparison to a representative UK EHR dataset, modifiable risk factor associations, and genetic correlations with external genome-wide association studies (GWAS). Results indicated consistent disease distributions by age and sex, high correlation with non-selected general population data prevalence estimates, confirmed risk factor associations, and significant genetic correlations with external GWAS for nine of ten evaluated diseases. Our approach establishes comprehensive disease validation profiles, improving phenotype generalizability despite inherent UKB demographic biases. The modular, reproducible framework can be extended to additional diseases and populations, supporting federated analyses across diverse biobanks, and facilitating research in underrepresented populations.

Summary of significant mergers and acquisitions (M&A) developments in 2005 in the United States and other countries - countries are listed in alphabetical order.