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Коморбидная патология у инфицированных SARSCoV-2 в онкологической практике – предиктор тяжелых исходов при COVID-19. Прогностический онлайн-инструмент оценки риска COVID-19 в онкологии
Коморбидная патология у инфицированных SARSCoV-2 в онкологической практике – предиктор тяжелых исходов при COVID-19. Прогностический онлайн-инструмент оценки риска COVID-19 в онкологии
Секачева М. И., Русанов А. С., Кохановская Я. Ю. Коморбидная патология у инфицированных SARS-CoV‑2 в онкологической практике – предиктор тяжелых исходов при COVID‑19. Прогностический онлайн-инструмент оценки риска COVID‑19 в онкологии. Современная Онкология. 2023;25(4):490–496.
DOI: 10.26442/18151434.2023.4.202371
© ООО «КОНСИЛИУМ МЕДИКУМ», 2023 г.
DOI: 10.26442/18151434.2023.4.202371
© ООО «КОНСИЛИУМ МЕДИКУМ», 2023 г.
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Аннотация
Цель. Оценить коморбидную патологию у инфицированных вирусом SARS-CoV‑2 (Severe Acute Respiratory Syndrome CoronaVirus‑2), являющимся возбудителем коронавирусной болезни (COVID‑19), в онкологической практике как предиктор тяжелых исходов, апробировать в онкологии инструмент оценки риска COVID‑19 CORONET в целях прогноза вероятной тяжести течения заболевания у онкологических пациентов (ОП), находящихся на стационарном лечении.
Материалы и методы. Проведен ретроспективный анализ 173 историй болезни ОП. Исследование осуществлено в рамках программы Научного центра мирового уровня «Цифровой биодизайн и персонализированное здравоохранение» ФГАОУ ВО «Первый МГМУ им. И. М. Сеченова» (Сеченовский Университет) с участием в проекте ESMO-CoCARE (European Society for Medical Oncology COVID‑19 and CAncerREgistry). На основе реестра ESMO-CoCARE, с учетом внесенной выборки пациентов (n=173), разработан CORONET – инструмент оценки риска COVID‑19 в онкологии.
Результаты. Показано, что ОП с коморбидной патологией имеют повышенный риск тяжелого течения COVID‑19. Прогноз вероятной тяжести исходов у ОП, инфицированных SARS-CoV‑2, зависит от сопутствующих заболеваний. Наличие в анамнезе ≥3 коморбидных патологий выявлено у 106 (61,27%) больных COVID‑19. В группу риска тяжелого течения заболевания из данной выборки по CORONET включены 15 больных (средний балл заключения CORONET – 2,44). При ретроспективном анализе получено фактическое подтверждение прогноза тяжелого течения заболевания пациентов исследуемой группы с последующим их смертельным исходом (71,42% всех умерших). Выявлено, что такие показатели, как низкий уровень альбумина, высокий уровень С-реактивного белка и нейтрофилов, обладают высокой прогностической значимостью в оценке вероятной тяжести течения COVID‑19 у ОП, находящихся на стационарном лечении по поводу указанного заболевания. Уровень смертности пациентов, поступивших с COVID‑19 и онкозаболеванием, составил 12,14%.
Заключение. Наиболее информативными предикторами тяжелых исходов при COVID‑19 у ОП, находящихся на стационарном лечении, позволяющими прогнозировать тяжесть течения заболевания, являются число сопутствующих болезней, возраст, оценка по шкале NEWS2, оценка статуса активности пациента по шкале ECOG, уровень альбумина и C-реактивного белка, абсолютный уровень нейтрофилов. Выявленные преимущества использования онлайн-инструмента поддержки принятия решений CORONET признаны убедительными не только на этапе принятия решения о госпитализации, но и уже во время нахождения ОП на стационарном лечении.
Ключевые слова: COVID‑19, онкологические заболевания, коморбидная патология, факторы риска тяжелого течения, CORONET
Materials and methods. A retrospective analysis of 173 cancer patient disease stories was conducted. The study was conducted as part of the program of the World-Class Research Center “Digital Biodesine and Personalized Health Care” of the University of Sechenov, with participation in the ESMO-CoCARE Register project. Based on the ESMO COVID‑19 and CAncerREgistry (ESMO-CoCARE) registry, taking into account the entered our patient sample (n=173), a COVID‑19 risk assessment tool in oncology (CORONET) was developed.
Results. Cancer patients with comorbid pathology have been shown to have an increased risk of COVID‑19 course of the disease from this sample by CORONET (mean conclusion score CORONET 2.44 pointsseverity). Prognosis of probable severity of outcomes in cancer patients infected with SARS-CoV‑2 depends on associated diseases. The presence of a history of ≥3 comorbid pathologies in COVID‑19 patients was detected in 106 people (61.27%). 15 patients were included in the risk of severe course. Retrospective analysis obtained actual confirmation of the prognosis of severe course of the disease in this group followed by death in all these patients (71.42% of all deaths). Indicators such as low albumin levels, high levels of C-reactive protein and neutrophils showed high prognostic significance of the likely severity of coronavirus infection in cancer patients on inpatient treatment for COVID‑19. The mortality rate of patients admitted with COVID‑19 and cancer was 12.14%.
Conclusion. The most informative predictors of severe outcomes in COVID‑19 in inpatient cancer patients to assess the prognosis of disease severity are: number of comorbidities, age, NEWS2 scale, ECOG patient activity status, albumin level, C-reactive protein level and absolute neutrophil level. The benefits gained in the use of the online decision support tool CORONET are considered convincing not only during the decision-making phase of hospitalization, but also in cancer patients on inpatient treatment.
Keywords: COVID‑19, cancer, comorbid pathology, severe flow risk factors, CORONET
Материалы и методы. Проведен ретроспективный анализ 173 историй болезни ОП. Исследование осуществлено в рамках программы Научного центра мирового уровня «Цифровой биодизайн и персонализированное здравоохранение» ФГАОУ ВО «Первый МГМУ им. И. М. Сеченова» (Сеченовский Университет) с участием в проекте ESMO-CoCARE (European Society for Medical Oncology COVID‑19 and CAncerREgistry). На основе реестра ESMO-CoCARE, с учетом внесенной выборки пациентов (n=173), разработан CORONET – инструмент оценки риска COVID‑19 в онкологии.
Результаты. Показано, что ОП с коморбидной патологией имеют повышенный риск тяжелого течения COVID‑19. Прогноз вероятной тяжести исходов у ОП, инфицированных SARS-CoV‑2, зависит от сопутствующих заболеваний. Наличие в анамнезе ≥3 коморбидных патологий выявлено у 106 (61,27%) больных COVID‑19. В группу риска тяжелого течения заболевания из данной выборки по CORONET включены 15 больных (средний балл заключения CORONET – 2,44). При ретроспективном анализе получено фактическое подтверждение прогноза тяжелого течения заболевания пациентов исследуемой группы с последующим их смертельным исходом (71,42% всех умерших). Выявлено, что такие показатели, как низкий уровень альбумина, высокий уровень С-реактивного белка и нейтрофилов, обладают высокой прогностической значимостью в оценке вероятной тяжести течения COVID‑19 у ОП, находящихся на стационарном лечении по поводу указанного заболевания. Уровень смертности пациентов, поступивших с COVID‑19 и онкозаболеванием, составил 12,14%.
Заключение. Наиболее информативными предикторами тяжелых исходов при COVID‑19 у ОП, находящихся на стационарном лечении, позволяющими прогнозировать тяжесть течения заболевания, являются число сопутствующих болезней, возраст, оценка по шкале NEWS2, оценка статуса активности пациента по шкале ECOG, уровень альбумина и C-реактивного белка, абсолютный уровень нейтрофилов. Выявленные преимущества использования онлайн-инструмента поддержки принятия решений CORONET признаны убедительными не только на этапе принятия решения о госпитализации, но и уже во время нахождения ОП на стационарном лечении.
Ключевые слова: COVID‑19, онкологические заболевания, коморбидная патология, факторы риска тяжелого течения, CORONET
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Materials and methods. A retrospective analysis of 173 cancer patient disease stories was conducted. The study was conducted as part of the program of the World-Class Research Center “Digital Biodesine and Personalized Health Care” of the University of Sechenov, with participation in the ESMO-CoCARE Register project. Based on the ESMO COVID‑19 and CAncerREgistry (ESMO-CoCARE) registry, taking into account the entered our patient sample (n=173), a COVID‑19 risk assessment tool in oncology (CORONET) was developed.
Results. Cancer patients with comorbid pathology have been shown to have an increased risk of COVID‑19 course of the disease from this sample by CORONET (mean conclusion score CORONET 2.44 pointsseverity). Prognosis of probable severity of outcomes in cancer patients infected with SARS-CoV‑2 depends on associated diseases. The presence of a history of ≥3 comorbid pathologies in COVID‑19 patients was detected in 106 people (61.27%). 15 patients were included in the risk of severe course. Retrospective analysis obtained actual confirmation of the prognosis of severe course of the disease in this group followed by death in all these patients (71.42% of all deaths). Indicators such as low albumin levels, high levels of C-reactive protein and neutrophils showed high prognostic significance of the likely severity of coronavirus infection in cancer patients on inpatient treatment for COVID‑19. The mortality rate of patients admitted with COVID‑19 and cancer was 12.14%.
Conclusion. The most informative predictors of severe outcomes in COVID‑19 in inpatient cancer patients to assess the prognosis of disease severity are: number of comorbidities, age, NEWS2 scale, ECOG patient activity status, albumin level, C-reactive protein level and absolute neutrophil level. The benefits gained in the use of the online decision support tool CORONET are considered convincing not only during the decision-making phase of hospitalization, but also in cancer patients on inpatient treatment.
Keywords: COVID‑19, cancer, comorbid pathology, severe flow risk factors, CORONET
Полный текст
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50. Временные методические рекомендации по профилактике, диагностике и лечению новой коронавирусной инфекции (COVID‑19) Версия 17 (14.12.2022) Минздрава России. Режим доступа: https://nmrc.ru/wp-content/uploads/2022/12/ВМР_COVID‑19_V17.pdf. Ссылка активна на 25.01.2023 [Vremennye metodicheskie rekomendatsii po profilaktike, diagnostike i lecheniiu novoi koronavirusnoi infektsii (COVID‑19) Versiia 17 (14.12.2022) Minzdrava Rossii. Available at: https://nmrc.ru/wp-content/uploads/2022/12/VMR_COVID‑19_V17.pdf. Accessed: 25.01.2023 (in Russian)].
51. Dettorre GM, Dolly S, Loizidou A, et al. Systemic pro-inflammatory response identifies patients with cancer with adverse outcomes from SARS-CoV‑2 infection: the OnCovid Inflammatory Score. J Immunother Cancer. 2021;9(3):e002277. DOI:10.1136/jitc‑2020‑002277
52. Dettorre GM, Patel M, Gennari A, et al. The systemic pro-inflammatory response: targeting the dangerous liaison between COVID‑19 and cancer. ESMO Open. 2021; 6(3):100‑23. DOI:10.1016/j.esmoop.2021.100123
53. Вечорко В.И., Аверков О. В., Гришин Д. В., Зимин А. А. Шкалы NEWS2, 4C Mortality Score, COVID-GRAM, Sequential Organ Failure Assessment Quick как инструменты оценки исходов тяжелой формы COVID‑19 (пилотное ретроспективное когортное исследование). Кардиоваскулярная терапия и профилактика. 2022;21(3):3103 [Vechorko VI, Averkov OV, Grishin DV, Zimin AA. NEWS2, 4C Mortality Score, COVID-GRAM, Sequential Organ Failure Assessment Quick scales as outcomes assessment tools for severe COVID‑19 (pilot retrospective cohort study). Cardiovascular Therapy and Prevention. 2022;21(3):3103 (in Russian)]. DOI:10.15829/1728‑8800‑2022‑3103
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4. Abdurakhmanov I, Umurzakov Sh, Zhamilova G, et al. COVID‑19 and comorbid pathology (literature review). The Scientific Heritage. 2021;68:56‑64 (in Russian).
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7. Sanyaolu A, Okorie C, Marinkovic A. Comorbidity and its impact on patients with COVID‑19. SN Compr Clin Med. 2020;2:1069‑76. DOI:10.1007/s42399‑020‑00363‑4
8. Richardson S, Hirsch JS, Narasimhan M. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized with COVID‑19 in the New York City Area. JAMA. 2020;323(20):2052‑9. DOI:10.1001/jama.2020.6775
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13. Lee RJ, Wysocki O, Zhou C, et al. Establishment of CORONET, COVID‑19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID‑19 Disease On Presentation to Hospital. JCO Clin Cancer Inform. 2022;6:e2100177. DOI:10.1200/CCI.21.00177
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19. Royal College of Physicians. National Early Warning Score (NEWS) 2: Standardizing the assessment of cute-illness severity in the NHS. Updated report of a working party. London: RCP, 2017.
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22. Tian J, Yuan X, Xiao J, et al. Clinical characteristics and risk factors associated with COVID‑19 disease severity in patients with cancer in Wuhan, China: a multicentre, retrospective, cohort study. Lancet Oncol. 2020;21(7):893‑903. DOI:10.1016/S1470‑2045(20)30309‑0
23. Gao YD, Ding M, Dong X, et al. Risk factors for severe and critically ill COVID‑19 patients: A review. Allergy. 2021;76(2):428‑55. DOI:10.1111/all.14657
24. Wu C, Chen X, Cai Y, et al. Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Intern Med. 2020;180(7):934‑43. DOI:10.1001/jamainternmed.2020.0994
25. Guzik TJ, Mohiddin SA, Dimarco A, et al. COVID‑19 and the cardiovascular system: implications for risk assessment, diagnosis, and treatment options. Cardiovasc Res. 2020;116(10):1666‑87. DOI:10.1093/cvr/cvaa106
26. Liu C, Zhao Y, Okwan-Duodu D, et al. COVID‑19 in cancer patients: risk, clinical features, and management. Cancer Biol Med. 2020;17(3):519‑27. DOI:10.20892/j.issn.2095‑3941.2020.0289
27. Chavez-MacGregor M, Lei X, Zhao H, et al. Evaluation of COVID‑19 Mortality and Adverse Outcomes in US Patients With or Without Cancer. JAMA Oncol. 2022;8(1):69‑78. DOI:10.1001/jamaoncol.2021.5148
28. O’Driscoll M, Dos Santos GR, Wang L, et al. Age-specific mortality and immunity patterns of SARS-CoV‑2. Nature. 2021;590(7844):140‑5. DOI:10.1038/s41586‑020‑2918‑0
29. Jin J-M, Bai P, He W, et al. Gender Differences in Patients With COVID‑19: Focus on Severity and Mortality. Front Public Health. 2020;29(8):152. DOI:10.3389/fpubh.2020.00152
30. Li Q, Guan X, Wu P, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020;382(13):1199‑207. DOI:10.1056/NEJMoa2001316
31. Buikema AR, Buzinec P, Paudel ML, et al. Racial and ethnic disparity in clinical outcomes among patients with confirmed COVID‑19 infection in a large US electronic health record database. EClinicalMedicine. 2021;39:101075. DOI:10.1016/j.eclinm.2021.101075
32. Bowen A, Miller AD, Zambrano LD, et al. Demographic and Clinical Factors Associated With Death Among Persons <21 Years Old With Multisystem Inflammatory Syndrome in Children – United States, February 2020 – March 2021. Open Forum Infect Dis. 2021;8(8):ofab388. DOI:10.1093/ofid/ofab388
33. Popkin BM, Du S, Green WD, et al. Individuals with obesity and COVID‑19: A global perspective on the epidemiology and biological relationships. Obes Rev. 2020;21(11):e13128. DOI:10.1111/obr.13128
34. Elkrief A, Desilets A, Papneja N, et al. High mortality among hospital-acquired COVID‑19 infection in patients with cancer: A multicentre observational cohort study. Eur J Cancer. 2020;139:181‑7. DOI:10.1016/j.ejca.2020.08.017
35. Garg S, Kim L, Whitaker M, et al. Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019 – COVID-NET, 14 States, March 1‑30, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(15):458‑64. DOI:10.15585/mmwr.mm6915e3
36. Aitbaev K, Murkamilov I, Fomin V, et al. Fibrosis as a consequence of COVID‑19 pandemic. Bulletin of Science and Practice. 2021;7(5):204‑15 (in Russian). DOI:10.33619/2414‑2948/66
37. Chazova IE, Mironova OI. COVID‑19 and cardiovascular diseases. Terapevticheskii Arkhiv (Ter. Arkh.). 2020;92(9):4‑7 (in Russian). DOI:10.26442/00403660.2020.09.000742
38. Shepelkevich AP, Dydyshko Yu V. Obesity and diabetes mellitus predictors of poor COVID‑19 outcomes. Med. zhurn. 2021;1(75):36‑48 (in Russian).
39. Shestakova MV, Vikulova OK, Isakov MА, Dedov II. Diabetes and COVID‑19: analysis of the clinical outcomes according to the data of the Russian diabetes registry. Problems of Endocrinology. 2020;66(1):35‑46 (in Russian). DOI:10.14341/probl12458
40. Arefin S, Hernandez L, Ward LJ, et al. MO069 SARS-COV‑2 receptor ACE‑2, TMPRSS2 and soluble ACE‑2 in patients with end stage kidney disease. Nephrology Dialysis Transplantation. 2021;36(Suppl. 1). DOI:10.1093/ndt/gfab078.005
41. Bode B, Garrett V, Messler J, et al. Glycemic Characteristics and Clinical Outcomes of COVID‑19 Patients Hospitalized in the United States. J Diabetes Sci Technol. 2020;14(4):813‑21. DOI:10.1177/1932296820924469
42. Chen Y, Yang D, Cheng B, et al. Clinical Characteristics and Outcomes of Patients With Diabetes and COVID‑19 in Association With Glucoselowering Medication. Diabetes Care. 2020;43(7):1399‑407. DOI:10.2337/dc20‑0660
43. Zhou Y, Yang Q, Chi J, et al. Comorbidities and the risk of severe or fatal outcomes associated with coronavirus disease 2019: A systematic review and meta-analysis. Int J Infect Dis. 2020;99:47‑56. DOI:10.1016/j.ijid.2020.07.029
44. Zarifkar P, Kamath A, Robinson C, et al. Clinical Characteristics and Outcomes in Patients with COVID‑19 and Cancer: a Systematic Review and Meta-analysis. Clin Oncol (R Coll Radiol). 2021;33(3):e180‑91. DOI:10.1016/j.clon.2020.11.006
45. Wang Q, Berger NA, Xu R. Analyses of Risk, Racial Disparity, and Outcomes Among US Patients With Cancer and COVID‑19 Infection. JAMA Oncol. 2021;7(2):220‑7. DOI:10.1001/jamaoncol.2020.6178
46. Grivas P, Khaki AR, Wise-Draper TM, et al. Association of clinical factors and recent anticancer therapy with COVID‑19 severity among patients with cancer: a report from the COVID‑19 and Cancer Consortium. Ann Oncol. 2021;32(6):787‑800. DOI:10.1016/j.annonc.2021.02.024
47. Ständiger Arbeitskreis der Kompetenz- und Behandlungszentren für Krankheiten durch hochpathogene Erreger am Robert Koch-Institut (STAKOB). Hinweise zu Erkennung, Diagnostik und Therapie von Patientenmit COVID‑19. Available at: https://www.rki.de/covid‑19‑therapie. Accessed: 24.01.2023.
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49. The Italian Society of Emergency Medicine (SIMEU). COVID‑19 First Line Report PS/DEA management organizational structure as part of an epidemic or pre-epidemic outbreak. Available at: https://www.simeu.it/w/articoli/leggiArticolo/334/dir. Accessed: 24.01.2023.
50. Vremennye metodicheskie rekomendatsii po profilaktike, diagnostike i lecheniiu novoi koronavirusnoi infektsii (COVID‑19) Versiia 17 (14.12.2022) Minzdrava Rossii. Available at: https://nmrc.ru/wp-content/uploads/2022/12/VMR_COVID‑19_V17.pdf. Accessed: 25.01.2023 (in Russian).
51. Dettorre GM, Dolly S, Loizidou A, et al. Systemic pro-inflammatory response identifies patients with cancer with adverse outcomes from SARS-CoV‑2 infection: the OnCovid Inflammatory Score. J Immunother Cancer. 2021;9(3):e002277. DOI:10.1136/jitc‑2020‑002277
52. Dettorre GM, Patel M, Gennari A, et al. The systemic pro-inflammatory response: targeting the dangerous liaison between COVID‑19 and cancer. ESMO Open. 2021; 6(3):100‑23. DOI:10.1016/j.esmoop.2021.100123
53. Vechorko VI, Averkov OV, Grishin DV, Zimin AA. NEWS2, 4C Mortality Score, COVID-GRAM, Sequential Organ Failure Assessment Quick scales as outcomes assessment tools for severe COVID‑19 (pilot retrospective cohort study). Cardiovascular Therapy and Prevention. 2022;21(3):3103 (in Russian). DOI:10.15829/1728‑8800‑2022‑3103
2. Coronavirus disease 2019 (COVID‑19) Situation Report‑46. Available at: http://www.who.int/emergencies/diseases/novel-coronavirus‑2019. Accessed: 26.10.2021.
3. Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020;395(10223):497‑506. DOI:10.1016/S0140‑6736(20)30183‑5
4. Абдурахманов И.У., Умурзаков Ш. Э., Жамилова Г. К., и др. COVID‑19 и коморбидная патология. The Scientific Heritage. 2021;68:56‑64 [Abdurakhmanov I, Umurzakov Sh, Zhamilova G, et al. COVID‑19 and comorbid pathology (literature review). The Scientific Heritage. 2021;68:56‑64 (in Russian)].
5. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID‑19 in Wuhan, China: a retrospective cohort study. Lancet. 2020; 395(10229):1054‑62. DOI:10.1016/S0140‑6736(20)30566‑3
6. Chen T, Wu D, Chen H, et al. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study. BMJ. 2020;368: m1091. DOI:10.1136/bmj.m1091
7. Sanyaolu A, Okorie C, Marinkovic A. Comorbidity and its impact on patients with COVID‑19. SN Compr Clin Med. 2020;2:1069‑76. DOI:10.1007/s42399‑020‑00363‑4
8. Richardson S, Hirsch JS, Narasimhan M. Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized with COVID‑19 in the New York City Area. JAMA. 2020;323(20):2052‑9. DOI:10.1001/jama.2020.6775
9. Wang W, Tang J, Wei F. Updated understanding of the outbreak of 2019 novel coronavirus (2019‑nCoV) in Wuhan, China. J Med Virol. 2020;92(4):441‑7. DOI:10.1002/jmv.25689
10. Centers for Disease Control and Prevention (CDC). Evidence used to update the list of underlying medical conditions that increase a person’s risk of severe illness from COVID‑19. Available at: https://www.cdc.gov/coronavirus/2019‑ncov/need-extra-precautions/evidence-table.html. Accessed: 06.10.2020.
11. Myers LC, Parodi SM, Escobar GJ, Liu VX. Characteristics of Hospitalized Adults With COVID‑19 in an Integrated Health Care System in California. JAMA. 2020; 323(21):2195‑8. DOI:10.1001/jama.2020.7202
12. Castelo-Branco L, Tsourti Z, Gennatas S, et al. COVID‑19 in patients with cancer: first report of the ESMO international, registry-based, cohort study (ESMO-CoCARE). ESMO Open. 2022;7(3):100499. DOI:10.1016/j.esmoop.2022.100499
13. Lee RJ, Wysocki O, Zhou C, et al. Establishment of CORONET, COVID‑19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID‑19 Disease On Presentation to Hospital. JCO Clin Cancer Inform. 2022;6:e2100177. DOI:10.1200/CCI.21.00177
14. COVID‑19 Risk in Oncology Evaluation Tool. Available at: https://coronet.manchester.ac.uk. Accessed: 21.01.2023.
15. Даниленко А.А., Шахтарина С. В., Фалалеева Н. А. COVID‑19 у пациентов с онкогематологическими заболеваниями. Клиническая онкогематология. 2021;14(2):220‑4 [Danilenko AA, Shakhtarina SV, Falaleeva NA. COVID‑19 in Patients with Oncohematological Diseases. Clinical Oncohematology. 2021;14(2):220‑4 (in Russian)]. DOI:10.21320/2500‑2139‑2021‑14‑2–220‑224
16. Liang W, Guan W, Chen R, et al. Cancer patients in SARS-CoV‑2 infection: a nationwide analysis in China. Lancet Oncol. 2020;21(3):335‑7. DOI:10.1016/S1470‑2045(20)30096‑6
17. Dai M, Liu D, Liu M, et al. Patients with Cancer Appear More Vulnerable to SARS-CoV‑2: A Multicenter Study during the COVID‑19 Outbreak. Cancer Discov. 2020;10(6): 783‑91. DOI:10.1158/2159‑8290.CD‑20‑0422
18. Kuderer NM, Choueiri TK, Shah DP, et al. Clinical impact of COVID‑19 on patients with cancer (CCC19): a cohort study. Lancet. 2020;395(10241):1907‑18. DOI:10.1016/S0140‑6736(20)31187‑9
19. Royal College of Physicians. National Early Warning Score (NEWS) 2: Standardizing the assessment of cute-illness severity in the NHS. Updated report of a working party. London: RCP, 2017.
20. Liao X, Wang B, Kang Y. Novel coronavirus infection during the 2019‑2020 epidemic: preparing intensive care units – the experience in Sichuan Province, China. Intensive Care Med. 2020;46(2):357‑60. DOI:10.1007/s00134‑020‑05954‑2
21. Бузиашвили Ю.И., Стилиди И. С., Асымбекова Э. У., и др. Комплексная оценка качества жизни у пациентов во время проведения неоадъювантной химиотерапии. Медицинский алфавит. 2022;(13):26‑31 [Buziashvili JI, Stilidi IS, Asymbekova EU, et al. Comprehensive assessment of quality of life in patients during neoadjuvant chemotherapy. Medical Alphabet. 2022;(13):26‑31 (in Russian)]. DOI:10.33667/2078‑5631‑2022‑13‑26‑31
22. Tian J, Yuan X, Xiao J, et al. Clinical characteristics and risk factors associated with COVID‑19 disease severity in patients with cancer in Wuhan, China: a multicentre, retrospective, cohort study. Lancet Oncol. 2020;21(7):893‑903. DOI:10.1016/S1470‑2045(20)30309‑0
23. Gao YD, Ding M, Dong X, et al. Risk factors for severe and critically ill COVID‑19 patients: A review. Allergy. 2021;76(2):428‑55. DOI:10.1111/all.14657
24. Wu C, Chen X, Cai Y, et al. Risk Factors Associated With Acute Respiratory Distress Syndrome and Death in Patients With Coronavirus Disease 2019 Pneumonia in Wuhan, China. JAMA Intern Med. 2020;180(7):934‑43. DOI:10.1001/jamainternmed.2020.0994
25. Guzik TJ, Mohiddin SA, Dimarco A, et al. COVID‑19 and the cardiovascular system: implications for risk assessment, diagnosis, and treatment options. Cardiovasc Res. 2020;116(10):1666‑87. DOI:10.1093/cvr/cvaa106
26. Liu C, Zhao Y, Okwan-Duodu D, et al. COVID‑19 in cancer patients: risk, clinical features, and management. Cancer Biol Med. 2020;17(3):519‑27. DOI:10.20892/j.issn.2095‑3941.2020.0289
27. Chavez-MacGregor M, Lei X, Zhao H, et al. Evaluation of COVID‑19 Mortality and Adverse Outcomes in US Patients With or Without Cancer. JAMA Oncol. 2022;8(1):69‑78. DOI:10.1001/jamaoncol.2021.5148
28. O’Driscoll M, Dos Santos GR, Wang L, et al. Age-specific mortality and immunity patterns of SARS-CoV‑2. Nature. 2021;590(7844):140‑5. DOI:10.1038/s41586‑020‑2918‑0
29. Jin J-M, Bai P, He W, et al. Gender Differences in Patients With COVID‑19: Focus on Severity and Mortality. Front Public Health. 2020;29(8):152. DOI:10.3389/fpubh.2020.00152
30. Li Q, Guan X, Wu P, et al. Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020;382(13):1199‑207. DOI:10.1056/NEJMoa2001316
31. Buikema AR, Buzinec P, Paudel ML, et al. Racial and ethnic disparity in clinical outcomes among patients with confirmed COVID‑19 infection in a large US electronic health record database. EClinicalMedicine. 2021;39:101075. DOI:10.1016/j.eclinm.2021.101075
32. Bowen A, Miller AD, Zambrano LD, et al. Demographic and Clinical Factors Associated With Death Among Persons <21 Years Old With Multisystem Inflammatory Syndrome in Children – United States, February 2020 – March 2021. Open Forum Infect Dis. 2021;8(8):ofab388. DOI:10.1093/ofid/ofab388
33. Popkin BM, Du S, Green WD, et al. Individuals with obesity and COVID‑19: A global perspective on the epidemiology and biological relationships. Obes Rev. 2020;21(11):e13128. DOI:10.1111/obr.13128
34. Elkrief A, Desilets A, Papneja N, et al. High mortality among hospital-acquired COVID‑19 infection in patients with cancer: A multicentre observational cohort study. Eur J Cancer. 2020;139:181‑7. DOI:10.1016/j.ejca.2020.08.017
35. Garg S, Kim L, Whitaker M, et al. Hospitalization Rates and Characteristics of Patients Hospitalized with Laboratory-Confirmed Coronavirus Disease 2019 – COVID-NET, 14 States, March 1‑30, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(15):458‑64. DOI:10.15585/mmwr.mm6915e3
36. Айтбаев К.А., Муркамилов И. Т., Фомин В. В., и др. Легочный фиброз как последствие пандемии COVID‑19. Бюллетень науки и практики. 2021;7(5):204‑15 [Aitbaev K, Murkamilov I, Fomin V, et al. Fibrosis as a consequence of COVID‑19 pandemic. Bulletin of Science and Practice. 2021;7(5):204‑15 (in Russian)]. DOI:10.33619/2414‑2948/66
37. Чазова И.Е., Миронова О. Ю. COVID‑19 и сердечно-сосудистые заболевания. Терапевтический архив. 2020;92(9):4‑7 [Chazova IE, Mironova OI. COVID‑19 and cardiovascular diseases. Terapevticheskii Arkhiv (Ter. Arkh.). 2020;92(9):4‑7 (in Russian)]. DOI:10.26442/00403660.2020.09.000742
38. Шепелькевич А.П., Дыдышко Ю. В. Ожирение и сахарный диабет как предикторы неблагоприятных исходов COVID‑19. Мед. журн. 2021;1:36‑48 [Shepelkevich AP, Dydyshko Yu V. Obesity and diabetes mellitus predictors of poor COVID‑19 outcomes. Med. zhurn. 2021;1(75):36‑48 (in Russian)].
39. Шестакова М.В., Викулова О. К., Исаков М. А., Дедов И. И. Сахарный диабет и COVID‑19: анализ клинических исходов по данным регистра сахарного диабета Российской Федерации. Проблемы Эндокринологии. 2020;66(1):35‑46 [Shestakova MV, Vikulova OK, Isakov MА, Dedov II. Diabetes and COVID‑19: analysis of the clinical outcomes according to the data of the Russian diabetes registry. Problems of Endocrinology. 2020;66(1):35‑46 (in Russian)]. DOI:10.14341/probl12458
40. Arefin S, Hernandez L, Ward LJ, et al. MO069 SARS-COV‑2 receptor ACE‑2, TMPRSS2 and soluble ACE‑2 in patients with end stage kidney disease. Nephrology Dialysis Transplantation. 2021;36(Suppl. 1). DOI:10.1093/ndt/gfab078.005
41. Bode B, Garrett V, Messler J, et al. Glycemic Characteristics and Clinical Outcomes of COVID‑19 Patients Hospitalized in the United States. J Diabetes Sci Technol. 2020;14(4):813‑21. DOI:10.1177/1932296820924469
42. Chen Y, Yang D, Cheng B, et al. Clinical Characteristics and Outcomes of Patients With Diabetes and COVID‑19 in Association With Glucoselowering Medication. Diabetes Care. 2020;43(7):1399‑407. DOI:10.2337/dc20‑0660
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51. Dettorre GM, Dolly S, Loizidou A, et al. Systemic pro-inflammatory response identifies patients with cancer with adverse outcomes from SARS-CoV‑2 infection: the OnCovid Inflammatory Score. J Immunother Cancer. 2021;9(3):e002277. DOI:10.1136/jitc‑2020‑002277
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Авторы
М. И. Секачева, А. С. Русанов*, Я. Ю. Кохановская
ФГАОУ ВО «Первый Московский государственный медицинский университет им. И. М. Сеченова» Минздрава России (Сеченовский Университет), Москва, Россия
*rusanov_a_s@staff.sechenov.ru
Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
*rusanov_a_s@staff.sechenov.ru
ФГАОУ ВО «Первый Московский государственный медицинский университет им. И. М. Сеченова» Минздрава России (Сеченовский Университет), Москва, Россия
*rusanov_a_s@staff.sechenov.ru
________________________________________________
Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
*rusanov_a_s@staff.sechenov.ru
Цель портала OmniDoctor – предоставление профессиональной информации врачам, провизорам и фармацевтам.