Tuberculosis and Respiratory Diseases

The Korean Academy of Tuberculosis and Respiratory Diseases (대한결핵및호흡기학회)
권호 표지
DOI QR코드

DOI QR Code

Big Data Research on Severe Asthma

  • Sang Hyuk Kim (Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Dongguk University Gyeongju Hospital, Dongguk University College of Medicine) ;
  • Youlim Kim (Division of Pulmonary and Allergy, Department of Internal Medicine, Konkuk University Hospital, Konkuk University School of Medicine)
  • Received : 2023.11.18
  • Accepted : 2024.02.29
  • Published : 2024.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

The continuously increasing prevalence of severe asthma has imposed an increasing burden worldwide. Despite the emergence of novel therapeutic agents, management of severe asthma remains challenging. Insights garnered from big data may be helpful in the effort to determine the complex nature of severe asthma. In the field of asthma research, a vast amount of big data from various sources, including electronic health records, national claims data, and international cohorts, is now available. However, understanding of the strengths and limitations is required for proper utilization of specific datasets. Use of big data, along with advancements in artificial intelligence techniques, could potentially facilitate the practice of precision medicine in management of severe asthma.

Keywords

References

  1. Roski J, Bo-Linn GW, Andrews TA. Creating value in health care through big data: opportunities and policy implications. Health Aff (Millwood) 2014;33:1115-22. https://doi.org/10.1377/hlthaff.2014.0147
  2. De Mauro A, Greco M, Grimaldi M. A formal definition of big data based on its essential features. Libr Rev 2016;65:122-35. https://doi.org/10.1108/LR-06-2015-0061
  3. Mallappallil M, Sabu J, Gruessner A, Salifu M. A review of big data and medical research. SAGE Open Med 2020;8:2050312120934839.
  4. Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2014;2:3.
  5. Okada M. Big data and real-world data-based medicine in the management of hypertension. Hypertens Res 2021;44:147-53. https://doi.org/10.1038/s41440-020-00580-3
  6. Shin SY. Current status and future direction of digital health in Korea. Korean J Physiol Pharmacol 2019;23:311-5. https://doi.org/10.4196/kjpp.2019.23.5.311
  7. Agache I, Akdis CA, Akdis M, Canonica GW, Casale T, Chivato T, et al. EAACI biologicals guidelines: recommendations for severe asthma. Allergy 2021;76:14-44. https://doi.org/10.1111/all.14425
  8. Lim GN, Allen JC, Tiew PY, Chen W, Koh MS. Healthcare utilization and health-related quality of life of severe asthma patients in Singapore. J Asthma 2023;60:969-80. https://doi.org/10.1080/02770903.2022.2114086
  9. Brusselle GG, Koppelman GH. Biologic therapies for severe asthma. N Engl J Med 2022;386:157-71. https://doi.org/10.1056/NEJMra2032506
  10. Kim SH, Kim Y. Tailored biologics selection in severe asthma. Tuberc Respir Dis (Seoul) 2024;87:12-21. https://doi.org/10.4046/trd.2023.0103
  11. Cozzoli N, Salvatore FP, Faccilongo N, Milone M. How can big data analytics be used for healthcare organization management?: literary framework and future research from a systematic review. BMC Health Serv Res 2022;22:809.
  12. Dash S, Shakyawar SK, Sharma M, Kaushik S. Big data in healthcare: management, analysis and future prospects. J Big Data 2019;6:1-25. https://doi.org/10.1186/s40537-018-0162-3
  13. Wallace PJ, Shah ND, Dennen T, Bleicher PA, Crown WH. Optum labs: building a novel node in the learning health care system. Health Aff (Millwood) 2014;33:1187-94. https://doi.org/10.1377/hlthaff.2014.0038
  14. Lynam A, Curtis C, Stanley B, Heatley H, Worthington C, Roberts EJ, et al. Data-resource profile: United Kingdom Optimum Patient Care Research Database. Pragmat Obs Res 2023;14:39-49. https://doi.org/10.2147/POR.S395632
  15. FitzGerald JM, Tran TN, Alacqua M, Altraja A, Backer V, Bjermer L, et al. International severe asthma registry (ISAR): protocol for a global registry. BMC Med Res Methodol 2020;20:212.
  16. Kim JA, Yoon S, Kim LY, Kim DS. Towards actualizing the value potential of Korea Health Insurance Review and Assessment (HIRA) data as a resource for health research: strengths, limitations, applications, and strategies for optimal use of HIRA data. J Korean Med Sci 2017;32:718-28. https://doi.org/10.3346/jkms.2017.32.5.718
  17. Hennessy S, Leonard CE, Palumbo CM, Newcomb C, Bilker WB. Quality of medicaid and medicare data obtained through Centers for Medicare and Medicaid Services (CMS). Med Care 2007;45:1216-20. https://doi.org/10.1097/MLR.0b013e318148435a
  18. Bolognesi MP, Habermann EB. Commercial claims data sources: PearlDiver and individual payer databases. J Bone Joint Surg Am 2022;104(Suppl 3):15-7. https://doi.org/10.2106/JBJS.22.00607
  19. Park JS, Lee CH. Clinical study using Healthcare Claims Database. J Rheum Dis 2021;28:119-25. https://doi.org/10.4078/jrd.2021.28.3.119
  20. Choi H, Kim SH, Han K, Park TS, Park DW, Moon JY, et al. Association between exercise and risk of cardiovascular diseases in patients with non-cystic fibrosis bronchiectasis. Respir Res 2022;23:288.
  21. Kim HK, Song SO, Noh J, Jeong IK, Lee BW. Data configuration and publication trends for the Korean National Health Insurance and Health Insurance Review & Assessment Database. Diabetes Metab J 2020;44:671-8. https://doi.org/10.4093/dmj.2020.0207
  22. Kang HS, Kim JY, Park HJ, Jung JW, Choi HS, Park JS, et al. E-cigarette-associated severe pneumonia in Korea using data linkage between the Korea National Health and Nutrition Examination Survey (KNHANES, 2013-2019) and the National Health Insurance Service (NHIS) Claims Database. J Korean Med Sci 2021;36:e331.
  23. Jeffery MM, Inselman JW, Maddux JT, Lam RW, Shah ND, Rank MA. Asthma patients who stop asthma biologics have a similar risk of asthma exacerbations as those who continue asthma biologics. J Allergy Clin Immunol Pract 2021;9:2742-50. https://doi.org/10.1016/j.jaip.2021.02.031
  24. Moore WC, Kornmann O, Humbert M, Poirier C, Bel EH, Kaneko N, et al. Stopping versus continuing long-term mepolizumab treatment in severe eosinophilic asthma (COMET study). Eur Respir J 2022;59:2100396.
  25. Ramagopalan SV, Simpson A, Sammon C. Can real-world data really replace randomised clinical trials? BMC Med 2020;18:13.
  26. Ryan D, Heatley H, Heaney LG, Jackson DJ, Pfeffer PE, Busby J, et al. Potential severe asthma hidden in UK primary care. J Allergy Clin Immunol Pract 2021;9:1612-23. https://doi.org/10.1016/j.jaip.2020.11.053
  27. Hong SH, Cho JY, Kim TB, Lee EK, Kwon SH, Shin JY. Cost-effectiveness of tiotropium in elderly patients with severe asthma using real-world data. J Allergy Clin Immunol Pract 2021;9:1939-47. https://doi.org/10.1016/j.jaip.2020.11.052
  28. Lee H, Ryu J, Nam E, Chung SJ, Yeo Y, Park DW, et al. Increased mortality in patients with corticosteroid-dependent asthma: a nationwide population-based study. Eur Respir J 2019;54:1900804.
  29. Wang E, Wechsler ME, Tran TN, Heaney LG, Jones RC, Menzies-Gow AN, et al. Characterization of severe asthma worldwide: data from the International Severe Asthma Registry. Chest 2020;157:790-804. https://doi.org/10.1016/j.chest.2019.10.053
  30. Chen W, Sadatsafavi M, Tran TN, Murray RB, Wong CB, Ali N, et al. Characterization of patients in the International Severe Asthma Registry with high steroid exposure who did or did not initiate biologic therapy. J Asthma Allergy 2022;15:1491-510. https://doi.org/10.2147/JAA.S377174
  31. Scelo G, Torres-Duque CA, Maspero J, Tran TN, Murray R, Martin N, et al. Analysis of comorbidities and multimorbidity in adult patients in the International Severe Asthma Registry. Ann Allergy Asthma Immunol 2024;132:42-53. https://doi.org/10.1016/j.anai.2023.08.021
  32. Lee JH, Kim HJ, Park CS, Park SY, Park SY, Lee H, et al. Clinical characteristics and disease burden of severe asthma according to oral corticosteroid dependence: real-world assessment from the Korean Severe Asthma Registry (KoSAR). Allergy Asthma Immunol Res 2022;14:412-23. https://doi.org/10.4168/aair.2022.14.4.412
  33. Lee Y, Lee JH, Park SY, Lee JH, Kim JH, Kim HJ, et al. Roles of real-world evidence in severe asthma treatment: challenges and opportunities. ERJ Open Res 2023;9:00248-2022.
  34. Grimes DA, Schulz KF. Cohort studies: marching towards outcomes. Lancet 2002;359:341-5. https://doi.org/10.1016/S0140-6736(02)07500-1
  35. Wang X, Cheng Z. Cross-sectional studies: strengths, weaknesses, and recommendations. Chest 2020;158(1S):S65-71. https://doi.org/10.1016/j.chest.2020.03.012
  36. Goodrum H, Roberts K, Bernstam EV. Automatic classification of scanned electronic health record documents. Int J Med Inform 2020;144:104302.
  37. Yang X, Chen A, PourNejatian N, Shin HC, Smith KE, Parisien C, et al. A large language model for electronic health records. NPJ Digit Med 2022;5:194.
  38. Exarchos KP, Beltsiou M, Votti CA, Kostikas K. Artificial intelligence techniques in asthma: a systematic review and critical appraisal of the existing literature. Eur Respir J 2020;56:2000521.
  39. Inselman JW, Jeffery MM, Maddux JT, Lam RW, Shah ND, Rank MA, et al. A prediction model for asthma exacerbations after stopping asthma biologics. Ann Allergy Asthma Immunol 2023;130:305-11.  https://doi.org/10.1016/j.anai.2022.11.025