Publications

Members of our team have contributed and provided lead authors to a range of high-impact publications...

  • Ahmad T, Freeman JV, Asselbergs FW. Can advanced analytics fix modern medicine's problem of uncertainty, imprecision, and inaccuracy? Eur J Heart Fail. 2019 Jan;21(1):86-89. doi: 10.1002/ejhf.1370. Epub 2018 Dec 10.
  • Akyea, R. K., Leonardi-Bee, J., Asselbergs, F. W., Patel, R. S., Durrington, P., Wierzbicki, A. S., . . . Weng, S. F. Predicting major adverse cardiovascular events for secondary prevention: protocol for a systematic review and meta-analysis of risk prediction models. BMJ open 2020; 10(7), e034564. doi:10.1136/bmjopen-2019-034564.
  • Al-Rubaish, A. M., Al-Muhanna, F. A., Alshehri, A. M., Al-Mansori, M. A., Alali, R. A., Khalil, R. M., . . . Al-Ali, A. K. Bedside testing of CYP2C19 gene for treatment of patients with PCI with antiplatelet therapy. BMC cardiovascular disorders  2020; 20(1), 268. doi:10.1186/s12872-020-01558-2.
  • Angermann, C. E., Assmus, B., Anker, S. D., Asselbergs, F. W., Brachmann, J., Brett, M. E., . . . Böhm, M. Pulmonary artery pressure-guided therapy in ambulatory patients with symptomatic heart failure: the CardioMEMS European Monitoring Study for Heart Failure (MEMS-HF). European Journal of Heart Failure 2020; doi:10.1002/ejhf.1943.
  • Asselbergs FW, Meijboom FJ. Big data analytics in adult congenital heart disease: why coding matters. Eur Heart J. 2019 Apr 1;40(13):1078-1080. doi: 10.1093/eurheartj/ehz089.
  • Bagheri, A., Sammani, A., van der Heijden, P. G. M., Asselbergs, F. W., & Oberski, D. L. (2020). ETM: Enrichment by topic modeling for automated clinical sentence classification to detect patients' disease history. JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 21 pages. doi:10.1007/s10844-020-00605-w.
  • Bean, Daniel M., et al. Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance. PLOS ONE 12(10): e0185912. doi: 10.1371/journal.pone.0185912
  • Bosman, L. P., Cadrin-Tourigny, J., Bourfiss, M., Aliyari Ghasabeh, M., Sharma, A., Tichnell, C., . . . Te Riele, A. S. J. M. Diagnosing arrhythmogenic right ventricular cardiomyopathy by 2010 Task Force Criteria: clinical performance and simplified practical implementation. Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology 2020; 22(5), 787-796. doi:10.1093/europace/euaa039.
  • de Boer, R. A., Nijenkamp, L. L. A. M., Silljé, H. H. W., Eijgenraam, T. R., Parbhudayal, R., van Driel, B., . . . van der Velden, J. Strength of patient cohorts and biobanks for cardiomyopathy research. Netherlands Heart Journal 2020; 28, 50-56. doi:10.1007/s12471-020-01456-4.
  • Gho JMIH, Schmidt AF, Pasea L, Koudstaal S, Pujades-Rodriguez M, Denaxas S, Shah AD, Patel RS, Gale CP, Hoes AW, Cleland JG, Hemingway H, Asselbergs FW. An electronic health records cohort study on heart failure following myocardial infarction in England: incidence and predictors. BMJ Open. 2018 Mar 3;8(3):e018331. doi: 10.1136/bmjopen-2017-018331. Erratum in: BMJ Open. 2018 Mar 22;8(3):e018331corr1.
  • Gorrell, Genevieve, et al. Bio-YODIE: A named entity linking system for biomedical text. arXiv preprint arXiv 2018; 1811.04860. https://arxiv.org/pdf/1811.04860.pdf.
  • Groenhof TKJ, Asselbergs FW, Groenwold RHH, Grobbee DE, Visseren FLJ, Bots ML; UCC-SMART study group. The effect of computerized decision support systems on cardiovascular risk factors: a systematic review and meta-analysis. BMC Med Inform Decis Mak. 2019 Jun 10;19(1):108. doi: 10.1186/s12911-019-0824-x.
  • Groenhof TKJ, Koers LR, Blasse E, de Groot M, Grobbee DE, Bots ML, Asselbergs FW, Lely AT, Haitjema S; UPOD; UCC-CVRM Study Groups. Data mining information from electronic health records produced high yield and accuracy for current smoking status. J Clin Epidemiol. 2020 Feb;118:100-106. doi: 10.1016/j.jclinepi.2019.11.006. Epub 2019 Nov 12.
  • Groenhof TKJ, Kofink D, Bots ML, Nathoe HM, Hoefer IE, Van Solinge WW, Lely AT, Asselbergs FW, Haitjema S. Low-Density Lipoprotein Cholesterol Target Attainment in Patients With Established Cardiovascular Disease: Analysis of Routine Care Data. JMIR Med Inform. 2020 Apr 2;8(4):e16400. doi: 10.2196/16400.
  • Hagenbeek, F. A., Pool, R., van Dongen, J., Draisma, H. M., Hottenga, J. J., Willemsen, G., . . . Boomsma, D. I. Heritability estimates for 361 blood metabolites across 40 genome-wide association studies (vol 11, 39, 2020). NATURE COMMUNICATIONS 2020; 11(1), 1 page. doi:10.1038/s41467-020-15276-y
  • Harris S, Shi S, Brealey D, MacCallum NS, Denaxas S, Perez-Suarez D et al. Critical Care Health Informatics Collaborative (CCHIC): Data, tools and methods for reproducible research: A multi-centre UK intensive care database. Int J Med Inform 2018; 112: 82–9. https://doi.org/10.1016/j.ijmedinf.2018.01.006.
  • Heidemann, B. E., Koopal, C., Bots, M. L., Asselbergs, F. W., Westerink, J., & Visseren, F. L. J. (2020). The relation between VLDL-cholesterol and risk of cardiovascular events in patients with manifest cardiovascular disease. International Journal of Cardiology. doi:10.1016/j.ijcard.2020.08.030.
  • Helgadottir, A., Thorleifsson, G., Alexandersson, K. F., Tragante, V., Thorsteinsdottir, M., Eiriksson, F. F., . . . Stefansson, K. Genetic variability in the absorption of dietary sterols affects the risk of coronary artery disease. Eur Heart J. 2020; 41(28), 2618-2628. doi:10.1093/eurheartj/ehaa531.
  • 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; Innovative Medicines Initiative 2nd programme, Big Data for Better Outcomes, BigData@Heart Consortium of 20 academic and industry partners including ESC. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J. 2018 Apr 21;39(16):1481-1495. doi: 10.1093/eurheartj/ehx487.
  • Jackson, Richard, et al. CogStack-experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital. BMC medical informatics and decision making 2018; 18(47). doi: 10.1186/s12911-018-0623-9.
  • Kaura A, Arnold AD, Panoulas V, Glampson B, Davies J, Mulla A, Woods K, Omigie J, Shah AD, Channon KM, Weber JN, Thursz MR, Elliott P, Hemingway H, Williams B, Asselbergs FW, O'Sullivan M, Lord GM, Melikian N, Lefroy DC, Francis DP, Shah AM, Kharbanda R, Perera D, Patel RS, Mayet J. Prognostic significance of troponin level in 3121 patients presenting with atrial fibrillation (The NIHR Health Informatics Collaborative TROP-AF study). J Am Heart Assoc. 2020 Apr 7;9(7):e013684. doi: 10.1161/JAHA.119.013684. Epub 2020 Mar 26.
  • Kaura A, Panoulas V, Glampson B, Davies J, Mulla A, Woods K, Omigie J, Shah AD, Channon KM, Weber JN, Thursz MR, Elliott P, Hemingway H, Williams B, Asselbergs F, O'Sullivan M, Kharbanda R, Lord GM, Melikian N, Patel RS, Perera D, Shah AM, Francis DP, Mayet J. Association of troponin level and age with mortality in 250 000 patients: cohort study across five UK acute care centres. BMJ. 2019 Nov 20;367:l6055. doi: 10.1136/bmj.l6055.
  • Kaura A, Sterne JAC, Trickey A, Abbott S, Mulla A, Glampson B, Panoulas V, Davies J, Woods K, Omigie J, Shah AD, Channon KM, Weber JN, Thursz MR, Elliott P, Hemingway H, Williams B, Asselbergs FW, O'Sullivan M, Lord GM, Melikian N, Johnson T, Francis DP, Shah AM, Perera D, Kharbanda R, Patel RS, Mayet J. Invasive versus non-invasive management of older patients with non-ST elevation myocardial infarction (SENIOR-NSTEMI): a cohort study based on routine clinical data. Lancet. 2020 Aug 29;396(10251):623-634. doi: 10.1016/S0140-6736(20)30930-2. 
  • Klooster, C. C. V. T., Bhatt, D. L., Steg, P. G., Massaro, J. M., Dorresteijn, J. A. N., Westerink, J., . . . Visseren, F. L. J. Predicting 10-year risk of recurrent cardiovascular events andcardiovascular interventions in patients with established cardiovascular disease: results from UCC-SMART and REACH. International Journal of Cardiology 2020; doi:10.1016/j.ijcard.2020.09.053.
  • Koudstaal S, Pujades-Rodriguez M, Denaxas S, Gho JMIH, Shah AD, Yu N, Patel RS, Gale CP, Hoes AW, Cleland JG, Asselbergs FW, Hemingway H. Prognostic burden of heart failure recorded in primary care, acute hospital admissions, or both: a population-based linked electronic health record cohort study in 2.1 million people. Eur J Heart Fail. 2017 Sep;19(9):1119-1127. doi: 10.1002/ejhf.709. Epub 2016 Dec 23.
  • Kraljevica Z, Searleaf T, Shek A Roguski L,Noor K, Bean et al.Multi-domain Clinical Natural Language Processing with MedCAT: the Medical Concept Annotation Toolkit. 
    Artificial Intelligence in Medicine. Available online 1 May 2021. https://doi.org/10.1016/j.artmed.2021.102083.
  • Linschoten M, Asselbergs FW. CAPACITY-COVID: a European registry to determine the role of cardiovascular disease in the COVID-19 pandemic. Eur Heart J. 2020 Apr 8:ehaa280. doi: 10.1093/eurheartj/ehaa280. Epub ahead of print.
  • Linschoten M, Asselbergs FW, CAPACITY-COVID collaborative consortium, LEOSS Study Group. Clinical presentation, disease course and outcome of COVID-19 in hospitalized patients with and without pre-existing cardiac disease – a cohort study across sixteen countries. doi: https://doi.org/10.1101/2021.03.11.21253106 (pre-print server).
  • Linschoten, M., Kamphuis, J. A. M., & Asselbergs, F. W. (2020). Cardiovascular adverse events following treatment for non-Hodgkin lymphoma reply. Lancet Haemotology, 7(8), E557-E558. 
  • Lopez-Sainz, A., Dominguez, F., Lopes, L. R., Ochoa, J. P., Barriales-Villa, R., Climent, V., . . Dooijes, D. Clinical Features and Natural History of PRKAG2 Variant Cardiac Glycogenosis. Journal of the American College of Cardiology 2020; 76(2), 186-197. doi:10.1016/j.jacc.2020.05.029
  • Mahmoodi, B. K., Tragante, V., Kleber, M. E., Holmes, M. V., Schmidt, A. F., McCubrey, R. O., . . . Patel, R. Association of Factor V Leiden with Subsequent Atherothrombotic Events: A GENIUS-CHD Study of Individual Participant Data. Circulation 2020;  doi:10.1161/CIRCULATIONAHA.119.045526.http://10.1161/CIRCULATIONAHA.119.045526
  • Meiring C, Dixit A, Harris S, MacCallum NS, Brealey DA, Watkinson PJ, Jones A et al. Optimal intensive care outcome prediction over time using machine learning. PLoS One 2018; 13(11):e0206862. doi: 10.1371/journal.pone.0206862. eCollection 2018.
  • Noor K, Roguski L, Bai X, Handy A, Klapaukh R, Folarin A, Romao L, Matteson J, Lea N, Zhu L, Asselbergs F, Wong W, Shah A, Dobson R; Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals JMIR Med Inform 2022;10(8):e38122; URL: https://medinform.jmir.org/2022/8/e38122 DOI: 10.2196/38122
  • Oskarsson, G. R., Oddsson, A., Magnusson, M. K., Kristjansson, R. P., Halldorsson, G. H., Ferkingstad, E., . . . Stefansson, K.  Predicted loss and gain of function mutations in ACO1 are associated with erythropoiesis. COMMUNICATIONS BIOLOGY 2020; 3(1). doi:10.1038/s42003-020-0921-5.
  • Palmer E, Post B, Klapaukh R, Marra G, Harris S, MacCallum NS et al. The association between supraphysiologic arterial oxygen levels and mortality in critically ill patients. A multicenter observational cohort study. Am J Respir Crit Care Med 2019; 200(11):1373–80, https://www.atsjournals.org/doi/10.1164/rccm.201904-0849OC
  • Pei, J., Harakalova, M., Treibel, T. A., Lumbers, R. T., Boukens, B. J., Efimov, I. R., . . . Cheng, C. H3K27ac acetylome signatures reveal the epigenomic reorganization in remodeled non-failing human hearts.. Clin Epigenetics 2020; 12(1), 106. doi:10.1186/s13148-020-00895-5.
  • Pool, R., Hagenbeek, F. A., Hendriks, A. M., van Dongen, J., Willemsen, G., de Geus, E., . . . Slagboom, P. E. Genetics and Not Shared Environment Explains Familial Resemblance in Adult Metabolomics Data. TWIN RESEARCH AND HUMAN GENETICS 2020; 23(3), 145-155. doi:10.1017/thg.2020.53.
  • Poulos J, Zhu L, Shah AD. Data gaps in electronic health record (EHR) systems: An audit of problem list completeness during the COVID-19 pandemic. International Journal of Medical Informatics 2021; 150(104452). https://doi.org/10.1016/j.ijmedinf.2021.104452.
  • Sammani, A., Kayvanpour, E., Bosman, L. P., Sedaghat-Hamedani, F., Proctor, T., Gi, W. -T., . . . Asselbergs, F. W. Predicting sustained ventricular arrhythmias in dilated cardiomyopathy: a meta-analysis and systematic review. ESC HEART FAILURE 2020; doi:10.1002/ehf2.12689.
  • Savarese, G., Settergren, C., Schrage, B., Thorvaldsen, T., Löfman, I., Sartipy, U., . . . Lund, L. H. Comorbidities and cause-specific outcomes in heart failure across the ejection fraction spectrum: A blueprint for clinical trial design. International Journal of Cardiology 2020; doi:10.1016/j.ijcard.2020.04.068.
  • Schmidt, A. F., Finan, C., Gordillo-Marañón, M., Asselbergs, F. W., Freitag, D. F., Patel, R. S., . . . Hingorani, A. D. Genetic drug target validation using Mendelian randomisation. Nature Communications 2020; 11(1). doi:10.1038/s41467-020-16969-0.
  • Searle, Tom, et al. MedCATTrainer: A biomedical free text annotation Interface with active learning and research use case specific customisation. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations 2019; D19-3024. doi: 10.18653/v1/D19-3024.
  • Shi S, Pérez-Suárez D, Harris S, MacCallum N, Brealey D, Singer M et al. Critical care data processing tools. The Journal of Open Source Software 2017; 2(20):513. doi: 10.21105/joss.00513.
  • Smith DA,  Wang T, Oliver Freeman O, Charles Crichton C, Hizni Salih H, Philippa Clare Matthews PC, Jim Davies J, Kinga Anna Várnai KA, Shaw TA, Drumright LN, Romão L, David Ramlakan D, Higgins F,  Weir A, Nastouli E, Agarwa K, Gelson W, Cooke GS, Barnes E. National Institute for Health Research Health Informatics Collaborative: development of a pipeline to collate electronic clinical data for viral hepatitis research. BMJ Health & Care Informatics 2020;27:e100145. doi: 10.1136/bmjhci-2020-100145.
  • Stolfo D, Uijl A, Benson L, Schrage Budim M, Asselbergs FW, Koudstaal S, Sinagra G, Dahlström U, Rosano G, 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. Eur J Heart Fail. 2020 Jan;22(1):103-112. doi: 10.1002/ejhf.1615. Epub 2019 Oct 23.
  • Timmerman, N., de Kleijn, D. P. V., de Borst, G. J., den Ruijter, H. M., Asselbergs, F. W., Pasterkamp, G., . . . van der Laan, S. W. Family history and polygenic risk of cardiovascular disease: Independent factors associated with secondary cardiovascular events in patients undergoing carotid endarterectomy. Atherosclerosis 2020; doi:10.1016/j.atherosclerosis.2020.04.013.
  • Tissot HC, Shah AD, Brealey D, Harris S, Agbakoba R, Folarin A, Romao L, Roguski L, Dobson R, Asselbergs FW. Natural language processing for mimicking clinical trial recruitment in critical care: a semi-automated simulation based on the LeoPARDS trial. IEEE J Biomed Health Inform. 2020; 24(10):2950-9. doi: 10.1109/JBHI.2020.2977925. 
  • 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. Eur J Heart Fail. 2019 Oct;21(10):1197-1206. doi: 10.1002/ejhf.1350. Epub 2019 Jan 7.
  • Uijl, A., Lund, L. H., Vaartjes, I., Brugts, J. J., Linssen, G. C., Asselbergs, F. W., . . . Savarese, G. A registry-based algorithm to predict ejection fraction in patients with heart failure. ESC HEART FAILURE 2020, 10 pages. doi:10.1002/ehf2.12779.
  • Van Den Berg, V. J., Umans, V. A. W. M., Brankovic, M., Oemrawsingh, R. M., Asselbergs, F. W., Van Der Harst, P., . . . Akkerhuis, K. M. Stabilization patterns and variability of hs-CRP, NT-proBNP and ST2 during 1 year after acute coronary syndrome admission: Results of the BIOMArCS study. Clinical Chemistry and Laboratory Medicine 2020;. doi:10.1515/cclm-2019-1320.
  • van ’t Klooster, C. C., van der Graaf, Y., Ridker, P. M., Westerink, J., Hjortnaes, J., Sluijs, I., . . . Visseren, F. L. J. The relation between healthy lifestyle changes and decrease in systemic inflammation in patients with stable cardiovascular disease. Atherosclerosis 2020; 301, 37-43. doi:10.1016/j.atherosclerosis.2020.03.022.
  • Why Unstructured Data Holds the Key to Intelligent Healthcare Systems. HIT Consultant. 2015. URL: https://hitconsultant.net/2015/03/31/tapping-unstructured-data-healthcares-biggest-hurdle-realized/ [accessed 2022-07-08]
  • Willeit, P., Tschiderer, L., Allara, E., Reuber, K., Seekircher, L., Gao, L., . . . Lorenz, M. W. Carotid Intima-Media Thickness Progression as Surrogate Marker for Cardiovascular Risk: Meta-Analysis of 119 Clinical Trials Involving 100 667 Patients. Circulation 2020; 142(7), 621-642. doi:10.1161/CIRCULATIONAHA.120.046361
  • Wu, Honghan, et al. SemEHR: A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research. Journal of the American Medical Informatics Association 2018; 25(5):530–53. doi: 10.1093/jamia/ocx160.
  • Wu, Honghan, et al. SemEHR: Surfacing semantic data from clinical notes in electronic health records for tailored care, trial recruitment, and clinical research. The Lancet 2017; 390(S97). doi: 10.1016/S0140-6736(17)33032-5.