Semantic analysis of social media messages of patients with neovascular age-related macular degeneration and diabetic macular edema by open Internet sources — a study of patients' opinions in real clinical practice
https://doi.org/10.21516/2072-0076-2023-16-1-51-58
Abstract
Purpose: to analyze social media messages of patients with neovascular age-related macular degeneration (nAMD) and diabetic retinopathy (DR), or their careers in order to investigates the patients’ opinion in the condition of real clinical routine. Material and methods. Real-life anonymized stories of patients from Russian-language open Internet sources (forums, social networks in Russia) were processed by artificial intelligence techniques: the technologies of automated analysis of unstructured natural language texts, including semantic technologies. In these messages, patients and their careers (mainly, family members) openly and in an ‘uncensored’ way share their experience in diagnostics and treatment while looking for a second opinion or supporting each other. They use general social networks as well as specific disease-related forums or Q&A portals. We identified 73 098 DR/nAMD-related posts, including 13 138 posts by 844 DR patients and 358 posts by 212 nAMD patients. The posts were analyzed in several steps with the technologies of automated analysis of unstructured natural language texts including semantic technologies aimed at processing large volumes of data. The semantic analysis of texts dealt with the whole meaning rather than individual keywords. Results. We obtained information on the patients’ characteristics and treatment plans of retinal diseases in real practice but also on the patients’ attitude to their condition, diagnostic and curative procedures, their needs and difficulties experienced during treatment. The nAMD and DR patients have a low level of Internet activity and poor awareness of these diseases as compared with the patients suffering from non-ophthalmological diseases with lower prevalence (breast cancer, multiple sclerosis, etc.) or other ophthalmological disorders. Most of the content for DR was produced by the patients’ relatives (82.6 % of messages), and for nAMD — by the patients themselves (65 %). The key item for DR patients was diabetic microvascular manifestations (over 42 000 posts discussed ‘diabetic foot’ and only 681 ‘diabetic retinopathy’). Quality of life (QoL) was shown to be significantly affected with inability to work as a major burden for 30 % of nAMD patients, and diabetes-associated comorbidities as a key factor compromising QoL in 20 % of DR patients. In nAMD patients, the average time-to-diagnosis after disease manifestation was 1 year (35 % patients reported 1–2 months), in DR, over a half of the messages mentioned 1–2 years. The key reasons for visiting the clinics included in-depth eye exams (OCT mentioned by 59 % of nAMD patients) and treatment (24.1 %). Only 33.2 % of nAMD patients and 7 % of DR patients noted that they received anti-VEGFs. Treatment unaffordability is one of the key barriers. The patients lack clear understanding of the prognosis and effective treatment options. Conclusion. The study revealed low activity and awareness of nAMD and DR patients with regard to their diseases. This justifies the need of increasing computer literacy and awareness of effective treatment options and efficacy criteria not only in patients, but also in their younger relatives. The results confirm that, among the studied group of retinal patients, vision-related quality of life is compromised. We need to change at least several aspects of nAMD and DR patients’ management: reducing the time to diagnosis, prescribing effective treatment options and increasing the availability of these options.
About the Authors
V. V. NeroevRussian Federation
Vladimir V. Neroev — Academician of the RAS, Dr. of Med. Sci., professor, director, head of retina and optic nerve pathology department
14/19, Sadovaya-Chernogryazskaya St., Moscow, 105062
O. V. Zaytseva
Russian Federation
Olga V. Zaytseva — Cand. оf Med. Sci., deputy director, leading researcher of the department of retina and optic nerve pathology
14/19, Sadovaya-Chernogryazskaya St., Moscow, 105062
A. Yu. Berdieva
Russian Federation
Anna Y. Berdieva — federal medical advisor
70, Leningradskiy Avenue, Moscow, 125315
Z. M. Gabdullina
Russian Federation
Zemfira M. Gabdullina — federal medical advisor
70, Leningradskiy Avenue, Moscow, 125315
M. N. Pudikov
Russian Federation
Mikhail N. Pudikov — therapeutic area head Retina
70, Leningradskiy Avenue, Moscow, 125315
A. A. Leonova
Russian Federation
Anna A. Leonova — computational linguist
4, Ilyinka str., Moscow, 109012
V. F. Khoroshevsky
Russian Federation
Vladimir F. Khoroshevsky — Dr. of Tech. Sci., leading researcher
4, Ilyinka str., Moscow, 109012; 40, Vavilov str., Moscow, 119333
References
1. Neroev V.V. Russia’s nationwide epidemiological noninvasive study of patients with wet age-related macular degeneration. Russian ophthalmological journal. 2011; 4 (2): 4–9 (in Russian).
2. Neroev V.V., Zaytseva O.V., Mikhailova L.A. Incidence of diabetic retinopathy in the Russian Federation according to Federal statistics. Russian ophthalmological journal. 2018; 11 (2): 5–9 (in Russian). doi: 10.21516/2072-0076-2018-11-2-5-9
3. Wong W.L., Su X., Li X., et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. The Lancet Global Health. 2014; 2 (2): e106–16. doi: 10.1016/S2214-109X(13)70145-1
4. Teo Z.L., Tham Y.-C., Yu M., et al. Global prevalence of diabetic retinopathy and projection of burden through 2045. Systematic review and meta-analysis. Ophthalmology. 2021; 128 (11): 1580–91. doi: 10.1016/j.ophtha.2021.04.027
5. Burton M.J., Ramke J., Marques A.P., et al. The lancet global health commission on global eye health: vision beyond 2020. Lancet Glob Health. 2021; 9 (4): e489–551. doi: 10.1016/S2214-109X(20)30488-5
6. Ivakhnenko O.I., Neroev V.V., Zaytseva O.V. Age-related macular degeneration and diabetic eye lesion. Socio-economic aspects. Vestnik oftal’mologii. 2021; 137 (1): 123–9 (in Russian). doi: 10.17116/oftalma2021137011123
7. International Diabetes Federation. Diabetes Atlas 10th Edition, 2021. Available at: https://diabetesatlas.org/idfawp/resource-files/2021/07/IDF_Atlas_10th_ Edition_2021.pdf (Accessed 22.12.2022).
8. Dedov I.I., Shestakova M.V., Mayorov A.Yu., eds. Algorithms for specialized medical care for patients with diabetes mellitus. Clinical guidelines. 9th ed. Moscow; 2019 (in Russian).
9. Dedov I.I., Shestakova M.V., Vikulova O.K., Zheleznyakova A.V., Isakov M.А. Epidemiological characteristics of diabetes mellitus in the Russian Federation: clinical and statistical analysis according to the Federal diabetes register data of 01.01.2021. Diabetes mellitus. 2021; 24 (3): 204–21 (in Russian). doi: 10.14341/ DM12759
10. Dedov I.I., Shestakova M.V., Suntsov Yu.I., et al. Results of “The Diabetes mellitus' subprogramme” Federal targeted programme “Prevention and Management of Socially Significant Diseases, 2007–2012”. Diabetes mellitus. 2013; 2S: 2–48 (in Russian).
11. Im J.H.B., Jin Y.P., Chow R., Yan P. Prevalence of diabetic macular edema based on optical coherence tomography in people with diabetes: A systematic review and meta-analysis. Surv. Ophthalmol. 2022; 67 (4): 1244–51. doi: 10.1016/j. survophthal.2022.01.009
12. Li J.Q., Walchowski T., Schmid M., et al. Prevalence, incidence and future projection of diabetic eye disease in Europe: a systematic review and meta-analysis. Eur. J. Epidemiol. 2020; 35 (1): 11–23. doi: 10.1007/s10654-019-00560-z
13. Neroev V.V. Visual impairment in the Russian Federation. Russian National Ophthalmological Forum. Moscow; 2022 (in Russian)]. Available at: http:// avo-portal.ru/events/reports/item/450-doklad-neroeva-vv-invalidnost-pozreniyu-v-rossiyskoy-federatsii (Accessed 22.12.2022).
14. AlRyalat S.A., Abukahel A., Elubous K.A. Randomized controlled trials in ophthalmology: a bibliometric study. F1000Res. 2019; 8: 1718. doi: 10.12688/ f1000research.20673.1
15. Holz F.G., Figueroa M. S., Bandello E., et al. Ranibizumab treatment in treatment-naïve neovascular age-related macular degeneration: results from LUMINOUS, a global real-world study. Retina. 2020; 40 (9): 1673–85. doi: 10.1097/IAE.0000000000002670
16. Mitchell P., Shedow T. G., Farah M., et al. Effectiveness and safety of ranibizumab 0.5 mg in treatment-naïve patients with diabetic macular edema: Results from the real-world global LUMINOUS study. PLoS One. 2020; 15 (6): e0233595. doi: 10.1371/journal.pone.0233595
17. Kovaleva S.A., Fedyaev D.V., Seryapina Yu.V. Actual issues of providing and paying for medical care within compulsory medical insurance in patients with retinal diseases. Medical Technologies. Assessment and Choice. 2021; 43 (1): 63–72 (in Russian). doi: 10.17116/medtech20214301163
18. Arificial Intelligence in Healthcare Market by Offering (Hardware, Software, Services), Technology (Machine Learning, NLP, Context-aware Computing, Computer Vision), Application, End User and Geography — Global Forecast to 2027. Available at: https://www.researchandmarkets.com/reports/5116503/ artificial-intelligence-in-healthcare-market-by (Accessed 03.12.2022).
19. Study on Big Data in Public Health, Telemedicine and Healthcare. Final Report. Publication Office of the European Union. (2016). Available at: https:// health.ec.europa.eu/system/files/2016-12/bigdata_report_en_0.pdf (Accessed 12.11.2022).
20. AI in Pharmaceuticals. Pharmaceutical Executive. January 25, 2019. Available at: https://www.pharmexec.com/view/ai-pharmaceuticals (Accessed 6.12.2022).
21. Goldina T.A., Burmistrov V.A., Efimenko I.V., Khoroshevskiy V.F. Аrtificial intelligence in healthcare: Real World Data and Patient Voice — Are we ready for new realities? Medical Technologies. Assessment and Choice. 2021; 2: 22–31 (in Russian). doi: 10.17116/ medtech20214302122
22. Rapport F., Braithwaite J. Are we on the cusp of a fourth research paradigm? Predicting the future for a new approach to methods-use in medical and health services research. BMC Med. Res. Methodol. 2018; 18 (1): 131. doi: 10.1186/ s12874-018-0597-4
23. Khoroshevsky V.F., Efimenko V.F., Efimenko I.V. Artificial intelligence, biotechnology and medicine: Reality, myths and trends. Verlag: Springer; 2020: 416–36.
24. Van Stekelenborg J., Ellenius J., Maskell S., et al. Recommendations for the use of social media in pharmacovigilance: Lessons from IMI WEB-RADR. Drug Saf. 2019; 42 (12): 1393–407. doi: 10.1007/s40264-019-00858-7
25. Califf R., von Eschenbach A., McClellan M. Expanding the use of real-world evidence in regulatory and value-based payment decision-making for drugs and biologics. Bipartisan policy center, August 2019. Available at: https:// bipartisanpolicy.org/ (Accessed 23.10.2022).
26. Zou K.H., Li J. Z., Imoerato J., et al. Harnessing real-world data for regulatory use and applying innovative applications. J. Multidiscip. Healthc. 2020: 13: 671–9. doi: 10.2147/JMDH.S262776
27. Samsonov M.Yu., Pogrebnoy N.O., Volskaya E.A. New technologies in real-world data analysis (RWD): challenges and potential solutions. Remedium. 2020; (1–3). С. 3–9 (in Russian). doi: 10.21518/1561- 5936-2020-1-2-3-3-9
28. Altman D.G. How large a sample? In: Gore S.M., Altman D.G., eds. Statistics in Practice. London, UK: British Medical Association; 1982.
29. Whitley E., Ball J. Statistics review 4: Sample size calculations. Critical Care. 2002; 6 (4): 335–41. doi: 10.1186/cc1521
30. Efimenko I., Samsonov M., Paleeva A., et al. AI-based processing of patient voice in rare neuromuscular disorders: Understanding patient experience and early disease detection. Neuromuscular disorders. 2021; 31: S 47-S162. http:// dx.doi.org/10.1016/j.nmd.2021.07.346
Review
For citations:
Neroev V.V., Zaytseva O.V., Berdieva A.Yu., Gabdullina Z.M., Pudikov M.N., Leonova A.A., Khoroshevsky V.F. Semantic analysis of social media messages of patients with neovascular age-related macular degeneration and diabetic macular edema by open Internet sources — a study of patients' opinions in real clinical practice. Russian Ophthalmological Journal. 2023;16(1):51-58. (In Russ.) https://doi.org/10.21516/2072-0076-2023-16-1-51-58