Big Data

For Better Hearts

BigData@Heart

Publications

 

Research papers

 

 

  1. Amand Schmidt. Genetic drug target validation using Mendelian randomisation. Nature Communications. 26 June 2020. https://doi.org/10.1038/s41467-020-16969-0
     
  2. Alicia Uijl. A registry‐based algorithm to predict ejection fraction in patients with heart failure. ESC Heart Failure. 17 June 2020. https://doi.org/10.1002/ehf2.12779
     
  3. Cristina Lopez. Impact of Acute Hemoglobin Falls in Heart Failure Patients: A Population Study. J Clin Med. 15 June 2020. https://doi.org/10.3390/jcm9061869
     
  4. Amitava Banerjee. Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study. The Lancet. 12 May 2020. https://doi.org/10.1016/S0140-6736(20)30854-0
     
  5. Gianluigi Savarese. Comorbidities and cause-specific outcomes in heart failure across the ejection fraction spectrum: A blueprint for clinical trial design. International Journal of Cardiology. 30 April 2020. https://doi.org/10.1016/j.ijcard.2020.04.068
     
  6. William H Seligman. Development of an international standard set of outcome measures for patients with atrial. European Heart Journal. 29 January 2020. https://doi.org/10.1093/eurheartj/ehz871
     
  7. Sonia Shah. Genome-wide association and Mendelian randomisation analysis provide insights into the pathogenesis of heart failure. Nature Communications. 09 January 2020. doi.org/10.1038/s41467-019-13690-5 
     
  8. Shona Kalkman. Responsible data sharing in a big data-driven translational research platform: lessons learned. BMC Medical Informatics and Decision Making. 30 December 2019. doi.org/10.1186/s12911-019-1001-y
     
  9. Daniel M. Bean. Semantic computational analysis of anticoagulation use in atrial fibrillation from real world data. Plos One. 25 November 2019. doi.org/10.1371/journal.pone.0225625 
     
  10. Laura Pasea. Bleeding in cardiac patients prescribed antithrombotic drugs: Electronic health record phenotyping algorithms, incidence, trends and prognosis. BMC Medicine. 20 November 2019. doi.org/10.1186/s12916-019-1438-y 
     
  11. Shona Kalkman. Patients’ and public views and attitudes towards the sharing of health data for research: a narrative review of the empirical evidence. Journal of Medical Ethics. 12 November 2019. http://dx.doi.org/10.1136/medethics-2019-105651  
     
  12. Schrage B, Uijl A, Benson L, Westermann D, Ståhlberg M, Stolfo D, Dahstrom U, Linde C, Braunschweig F, and Savarese G. Association between use of primary prevention implantable cardioverter-defibrillators and mortality in patients with heart failure. A prospective propensity-score matched analysis from the Swedish heart failure registry. Circulation-Heart Failure. 3 September 2019. doi.org/10.1161/CIRCULATIONAHA.119.043012
     
  13. Stolfo D, Uijl A, Schrage B, Fudim M, Asselbergs FW, Koudstaal S, Sinagra G, Dahlstrom U, Rosano G, and Savarese G. Association between beta-blocker use and mortality/morbidity in older patients with heart failure with reduced ejection fraction. A propensity score-matched analysis from the Swedish Heart Failure Registry. European Journal of Heart Failure. 3 September 2019. https://doi.org/10.1002/ejhf.1615
     
  14. Banerjee A, Allan V, Denaxas S, Shah A, Kotecha D, Lambiase PD, Jacob J, Lund LH,Hemingway H. Subtypes of atrial fibrillation with concomitant valvular heart disease derived from electronic health records: phenotypes, population prevalence, trends and prognosis. Europace. 2019 July 14. doi.org/10.1093/europace/euz220
     
  15. Spiros Denaxas. UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER. Journal of the American Medical Informatics Association. 22 July 2019. doi.org/10.1093/jamia/ocz105 
     
  16. Shah S, Henry A, Roselli C, et al. Genome-wide association study provides new insights into the genetic architecture and pathogenesis of heart failure. BioRivix, 2019 July 10. doi.org/10.1101/682013
     
  17. Elias Allara. Genetic determinants of lipids and cardiovascular disease outcomes: a wide-angled Mendelian randomization investigation​. BioRxiv, 14 June 2019. doi.org/10.1101/668970 

  18. Kalkman S, Mostert M, Gerlinger C, Van Delden JJM, Van Thiel GJMW. Responsible data sharing in international health research: a systematic review of principles and norms. BMC Medical Ethics. 2019 March 28. doi.org/10.1186/s12910-019-0359-9
     
  19. Uijl A, Koudstaal S, Direk K, Denaxas S, Groenwold RHH, Banerjee A, Hoes AW, Hemingway H, Asselbergs FW. Risk factors for incident heart failure in age‐ and sex‐specific strata: a population‐based cohort using linked electronic health records. European Journal of Heart Failure. 2019 January 07. doi.org/10.1002/ejhf.1350
     
  20. Van der Laan SW, Harshfield EL, Hemerich D, Stacey D, Wood AM, Asselbergs FW. From lipid locus to drug target through human genomics. Cardiovascular Research. 2018 July 15. doi:10.1093/cvr/cvy120
     
  21. Johannes M I H Gho, An electronic health records cohort study on heart failure following myocardial infarction in England: incidence and predictors. BMJ Open. 2018 March 03. doi.org/10.1136/bmjopen-2017-018331 
     
  22. Hemingway H, Asselbergs FW, Danesh J, Dobson R, Maniadakis N, Maggioni A, van Thiel GJM, Cronin M, Brobert G, Vardas P, Anker SD, Grobbee DE, Denaxas S. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. European Heart Journal. 2017 August 29. doi:10.1093/eurheartj/ehx487
     
  23. Anker S, Asselbergs FW, Brobert G, Vardas P, Drobbee DE, Cronin M. Big Data in Cardiovascular Disease. European Heart Journal. 2017 June 21. doi: 10.1093/eurheartj/ehx283.