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Снижение рентгеновской плотности печени как потенциальный предиктор тяжелого течения COVID-19: ретроспективное когортное исследование
Шумская Ю.Ф., Ахмедзянова Д.А., Мнацаканян М.Г. Снижение рентгеновской плотности печени как потенциальный предиктор тяжелого течения COVID-19: ретроспективное когортное исследование. Consilium Medicum. 2023;25(5):351–356. DOI: 10.26442/20751753.2023.5.202251
© ООО «КОНСИЛИУМ МЕДИКУМ», 2023 г.
© ООО «КОНСИЛИУМ МЕДИКУМ», 2023 г.
________________________________________________
Shumskaya YuF, Akhmedzyanova DA, Mnatsakanyan MM. Decreased liver density as a potential predictor of severe COVID-19: a retrospective cohort study. Consilium Medicum. 2023;25(5):351–356.
DOI: 10.26442/20751753.2023.5.202251
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Аннотация
Обоснование. Для стратификации риска у пациентов с COVID-19 важно знать параметры, которые предрасполагают к тяжелому течению. Описаны следующие факторы риска: возраст старше 60 лет, избыточная масса тела, мужской пол, хронические заболевания (гипертоническая болезнь, сахарный диабет 2-го типа). Низкая плотность печени по данным компьютерной томографии (КТ) также рассматривается как потенциальной фактор риска.
Цель. Оценка возможности использования низких показателей плотности печени как предиктора тяжелого течения COVID-19.
Материалы и методы. Ретроспективное одноцентровое когортное исследование. Включены пациенты с COVID-19, проходившие лечение в условиях стационара, которым выполнены две КТ органов грудной клетки в динамике. Пациенты распределены по группам согласно тяжести течения (группы среднетяжелого, тяжелого течения и летального исхода). Связь исследуемых факторов определена с использованием регрессионного анализа.
Результаты. Включены 99 человек, сформированы 3 группы сравнения: среднетяжелое течение (n=37), тяжелое течение (n=52), летальный исход (n=8). Все группы достоверно различались по уровню С-реактивного белка. По результатам многофакторного регрессионного анализа на тяжесть COVID-19 влияло отношение плотности печени к плотности селезенки по данным КТ при поступлении [отношение шансов 12,18 (95% доверительный интервал 1,67–89,07); p=0,008]. При этом ни один из названных факторов не является предиктором летального исхода COVID-19 (p>0,05).
Заключение. Сниженная плотность печени по данным КТ при обследовании пациента с COVID-19 может служить предиктором тяжелого течения новой коронавирусной инфекции.
Ключевые слова: плотность печени, COVID-19, компьютерная томография, прогноз
Цель. Оценка возможности использования низких показателей плотности печени как предиктора тяжелого течения COVID-19.
Материалы и методы. Ретроспективное одноцентровое когортное исследование. Включены пациенты с COVID-19, проходившие лечение в условиях стационара, которым выполнены две КТ органов грудной клетки в динамике. Пациенты распределены по группам согласно тяжести течения (группы среднетяжелого, тяжелого течения и летального исхода). Связь исследуемых факторов определена с использованием регрессионного анализа.
Результаты. Включены 99 человек, сформированы 3 группы сравнения: среднетяжелое течение (n=37), тяжелое течение (n=52), летальный исход (n=8). Все группы достоверно различались по уровню С-реактивного белка. По результатам многофакторного регрессионного анализа на тяжесть COVID-19 влияло отношение плотности печени к плотности селезенки по данным КТ при поступлении [отношение шансов 12,18 (95% доверительный интервал 1,67–89,07); p=0,008]. При этом ни один из названных факторов не является предиктором летального исхода COVID-19 (p>0,05).
Заключение. Сниженная плотность печени по данным КТ при обследовании пациента с COVID-19 может служить предиктором тяжелого течения новой коронавирусной инфекции.
Ключевые слова: плотность печени, COVID-19, компьютерная томография, прогноз
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Background. To stratify the risk in patients with COVID-19, it is important to understand the parameters that predispose to a severe course. Following risk factors were described: age over 60 years, overweight, male gender, chronic diseases: hypertension, diabetes mellitus. Low liver density on computed tomography (CT) is also considered as a potential risk factor.
Aim. To evaluation whether low liver density can be used as a predictor of severe COVID-19.
Materials and methods. Retrospective single-center cohort study. Patients with COVID-19 treated in a hospital setting, who underwent two CT scans of the thoracic organs in dynamics, were included. The patients were divided into groups according to the severity of the course (groups of moderate course, severe course and lethal outcome). Relation of the investigated factors was estimated using regression analysis.
Results. 99 patients were enrolled; 3 comparison groups were formed (moderate-severe course n=37, severe course n=52, lethal outcome n=8). All groups significantly differed in C-reactive protein levels. According to multivariate regression analysis, COVID-19 severity was influenced by the liver to spleen density ratio as measured by CT scan on admission [odds ratio 12.18 (95% confidence interval 1.67–89.07); p=0.008].
Conclusion. Reduced liver density on CT scan in a patient with COVID-19 may be a predictor of severe course of novel coronavirus infection.
Keywords: liver density, COVID-19, computed tomography, prognosis
Полный текст
Список литературы
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28. Graffy PM, Sandfort V, Summers RM, Pickhardt PJ. Automated liver fat quantification at nonenhanced abdominal CT for population-based steatosis assessment. Radiology. 2019;293(2):334-42. DOI:10.1148/radiol.2019190512
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30. Shumskaya YF, Gonchar AP, Mnatsakanyan MG, et al. Influence of Liver Attenuation on the Severity of Course COVID-19: A Retrospective Cohort Study. medRxiv. 2023. DOI:10.1101/2023.02.27.23286488
2. Grasselli G, Greco M, Zanella A, et al. Risk Factors Associated With Mortality Among Patients With COVID-19 in Intensive Care Units in Lombardy, Italy [published correction appears in JAMA Intern Med. 2021;181(7):1021]. JAMA Intern Med. 2020;180(10):1345-55. DOI:10.1001/jamainternmed.2020.3539
3. Gupta S, Hayek SS, Wang W, et al. Factors Associated With Death in Critically Ill Patients With Coronavirus Disease 2019 in the US [published correction appears in JAMA Intern Med. 2020;180(11):1555] [published correction appears in JAMA Intern Med. 2021;181(8):1144]. JAMA Intern Med. 2020;180(11):1436-47. DOI:10.1001/jamainternmed.2020.3596
4. Docherty AB, Harrison EM, Green CA, et al. Features of 20 133 UK patients in hospital with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ. 2020;369:m1985. DOI:10.1136/bmj.m1985
5. Karagiannidis C, Mostert C, Hentschker C, et al. Case characteristics, resource use, and outcomes of 10 021 patients with COVID-19 admitted to 920 German hospitals: an observational study. Lancet Respir Med. 2020;8(9):853-62. DOI:10.1016/S2213-2600(20)30316-7
6. 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 [published correction appears in JAMA Intern Med. 2020;180(7):1031]. JAMA Intern Med. 2020;180(7):934-43. DOI:10.1001/jamainternmed.2020.0994
7. 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 [published correction appears in Lancet. 2020;395(10229):1038] [published correction appears in Lancet. 2020;395(10229):1038]. Lancet. 2020;395(10229):1054-62. DOI:10.1016/S0140-6736(20)30566-3
8. Garibaldi BT, Fiksel J, Muschelli J, et al. Patient Trajectories Among Persons Hospitalized for COVID-19: A Cohort Study [published correction appears in Ann Intern Med. 2021;174(1):144]. Ann Intern Med. 2021;174(1):33-41. DOI:10.7326/M20-3905
9. Dongiovanni P, Meroni M, Longo M, Fracanzani AL. MAFLD in COVID-19 patients: an insidious enemy. Expert Rev Gastroenterol Hepatol. 2020;14(10):867-72. DOI:10.1080/17474124.2020.1801417
10. Yang RX, Zheng RD, Fan JG. Etiology and management of liver injury in patients with COVID-19. World J Gastroenterol. 2020;26(32):4753-62. DOI:10.3748/wjg.v26.i32.4753
11. Мнацаканян М.Г., Погромов А.П., Лишута А.С., и др. Механизмы повреждения печени при COVID-19. Терапевтический архив. 2021;93(4):427-30 [Mnatsakanyan MG, Pogromov AP, Lishuta AS, et al. Liver and COVID-19: possible mechanisms of damage. Terapevticheskii Arkhiv (Ter. Arkh.). 2021;93(4):427-30 (in Russian)]. DOI:10.26442/00403660.2021.04.200733
12. Bangash MN, Patel J, Parekh D. COVID-19 and the liver: little cause for concern. Lancet Gastroenterol Hepatol. 2020;5(6):529-30. DOI:10.1016/S2468-1253(20)30084-4
13. Shiralkar K, Chinapuvvula N, Ocazionez D. Cross-Sectional Abdominal Imaging Findings in Patients With COVID-19. Cureus. 2020;12(8):e9538. DOI:10.7759/cureus.9538
14. Синицын В.Е., Тюрин И.Е., Митьков В.В. Временные методические рекомендации Российского общества рентгенологов и радиологов (РОРР) и Российской ассоциации специалистов ультразвуковой диагностики в медицине (РАСУДМ) «Методы лучевой диагностики пневмонии при новой коронавирусной инфекции при COVID-19» Российское общество рентгенологов и радиологов (РОРР), Российская ассоциация специалистов ультразвуковой диагностики в медицине (РАСУДМ). 2020 [Sinitsyn VE, Tiurin IE, Mit'kov VV. Vremennye metodicheskie rekomendatsii Rossiiskogo obshchestva rentgenologov i radiologov (RORR) i Rossiiskoi assotsiatsii spetsialistov ul'trazvukovoi diagnostiki v meditsine (RASUDM) “Metody luchevoi diagnostiki pnevmonii pri novoi koronavirusnoi infektsii pri COVID-19” Rossiiskoe obshchestvo rentgenologov i radiologov (RORR), Rossiiskaia assotsiatsiia spetsialistov ul'trazvukovoi diagnostiki v meditsine (RASUDM). 2020 (in Russian)].
15. Морозов С.П., Проценко Д.Н., Сметанина С.В., и др. Лучевая диагностика коронавирусной болезни (COVID-19): организация, методология, интерпретация результатов: методические рекомендации. Серия «Лучшие практики лучевой и инструментальной диагностики». Вып. 93. М., 2021 [Morozov SP, Protsenko DN, Smetanina SV, et al. Luchevaia diagnostika koronavirusnoi bolezni (COVID-19): organizatsiia, metodologiia, interpretatsiia rezul'tatov: metodicheskie rekomendatsii. Seriia “Luchshie praktiki luchevoi i instrumental'noi diagnostiki”. Vyp. 93. Moscow, 2021 (in Russian)].
16. Morozov SP, Reshetnikov RV, Gombolevskiy VA, et al. Diagnostic accuracy of computed tomography for identifying hospitalizations for patients with COVID-19. Digital Diagnostics. 2021;2(1):5-16. DOI:10.17816/DD46818
17. Morozov SP, Chernina VYu, Blokhin AI, Gombolevskiy VA. Chest computed tomography for outcome prediction in laboratory-confirmed COVID-19: A retrospective analysis of 38,051 cases. Digital Diagnostics. 2020;1(1):27-36. DOI:10.17816/DD46791
18. Pickhardt PJ, Graffy PM, Reeder SB, et al. Quantification of liver fat content with unenhanced mdct: phantom and clinical correlation with mri proton density fat fraction. Am J Roentgenol. 2018;211(3):W151-7. DOI:10.2214/AJR.17.19391
19. Ji D, Qin E, Xu J, et al. Non-alcoholic fatty liver diseases in patients with COVID-19: A retrospective study. J Hepatol. 2020;73(2):451-3. DOI:10.1016/j.jhep.2020.03.044
20. Richardson S, Hirsch JS, Narasimhan M, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA. 2020;323(20):2052. DOI:10.1001/jama.2020.6775
21. Wang X, Fang X, Cai Z, et al. Comorbid chronic diseases and acute organ injuries are strongly correlated with disease severity and mortality among covid-19 patients: a systemic review and meta-analysis. Research. 2020;2020/2402961. DOI:10.34133/2020/2402961
22. Vila-Corcoles A, Satue-Gracia E, Vila-Rovira A, et al. COVID19-related and all-cause mortality risk among middle-aged and older adults across the first epidemic wave of SARS-COV-2 infection: a population-based cohort study in Southern Catalonia, Spain, March–June 2020. BMC Public Health. 2021;21(1):1795. DOI:10.1186/s12889-021-11879-2
23. Lei P, Zhang L, Han P, et al. Liver injury in patients with COVID-19: clinical profiles, CT findings, the correlation of the severity with liver injury. Hepatol Int. 2020;14(5):733-42. DOI:10.1007/s12072-020-10087-1
24. Uchida Y, Uemura H, Yamaba S, et al. Significance of liver dysfunction associated with decreased hepatic CT attenuation values in Japanese patients with severe COVID-19. J Gastroenterol. 2020;55(11):1098-106. DOI:10.1007/s00535-020-01717-4
25. Parlak S, Çıvgın E, Beşler M, Kayıpmaz A. The effect of hepatic steatosis on COVID-19 severity: Chest computed tomography findings. Saudi J Gastroenterol. 2021;27(2):105. DOI:10.4103/sjg.sjg_540_20
26. Guler E, Unal NG, Cinkooglu A, et al. Correlation of liver-to-spleen ratio, lung CT scores, clinical, and laboratory findings of COVID-19 patients with two consecutive CT scans. Abdom Radiol. 2021;46(4):1543-551. DOI:10.1007/s00261-020-02805-y
27. Zanon M, Neri M, Pizzolitto S, et al. Liver pathology in COVID-19 related death and leading role of autopsy in the pandemic. World J Gastroenterol. 2023;29(1):200-20. DOI:10.3748/wjg.v29.i1.200
28. Graffy PM, Sandfort V, Summers RM, Pickhardt PJ. Automated liver fat quantification at nonenhanced abdominal CT for population-based steatosis assessment. Radiology. 2019;293(2):334-42. DOI:10.1148/radiol.2019190512
29. Demkina AE, Morozov SP, Vladzymyrskyy AV, et al. Risk Factors for Outcomes of COVID-19 Patients: An Observational Study of 795 572 Patients in Russia. medRxiv. 2020. DOI:10.1101/2020.11.02.20224253
30. Shumskaya YF, Gonchar AP, Mnatsakanyan MG, et al. Influence of Liver Attenuation on the Severity of Course COVID-19: A Retrospective Cohort Study. medRxiv. 2023. DOI:10.1101/2023.02.27.23286488
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1. Liu YC, Kuo RL, Shih SR. COVID-19: The first documented coronavirus pandemic in history. Biomed J. 2020;43(4):328-33. DOI:10.1016/j.bj.2020.04.007
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Авторы
Ю.Ф. Шумская1,2, Д.А. Ахмедзянова2, М.Г. Мнацаканян*2
1 ГБУЗ «Научно-практический клинический центр диагностики и телемедицинских технологий» Департамента здравоохранения г. Москвы, Москва, Россия;
2 ФГАОУ ВО «Первый Московский государственный медицинский университет им. И.М. Сеченова» Минздрава России (Сеченовский Университет), Москва, Россия
*mnatsakanyan08@mail.ru
1 ГБУЗ «Научно-практический клинический центр диагностики и телемедицинских технологий» Департамента здравоохранения г. Москвы, Москва, Россия;
2 ФГАОУ ВО «Первый Московский государственный медицинский университет им. И.М. Сеченова» Минздрава России (Сеченовский Университет), Москва, Россия
*mnatsakanyan08@mail.ru
________________________________________________
Yuliya F. Shumskaya1,2, Dina A. Akhmedzyanova2, Marina M. Mnatsakanyan*2
1 Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies, Moscow, Russia;
2 Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
*mnatsakanyan08@mail.ru
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