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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Digital Diagnostics</journal-id><journal-title-group><journal-title xml:lang="en">Digital Diagnostics</journal-title><trans-title-group xml:lang="ru"><trans-title>Digital Diagnostics</trans-title></trans-title-group><trans-title-group xml:lang="zh"><trans-title>Digital Diagnostics</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2712-8490</issn><issn publication-format="electronic">2712-8962</issn><publisher><publisher-name xml:lang="en">Eco-Vector</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">46818</article-id><article-id pub-id-type="doi">10.17816/DD46818</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Original Study Articles</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Оригинальные исследования</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="zh"><subject>原创性科研成果</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Diagnostic accuracy of computed tomography for identifying hospitalizations for patients with COVID-19</article-title><trans-title-group xml:lang="ru"><trans-title>Диагностическая точность компьютерной томографии для определения необходимости госпитализации пациентов с COVID-19</trans-title></trans-title-group><trans-title-group xml:lang="zh"><trans-title>CT诊断的准确率，以确定COVID-19患者的住院需求</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-6545-6170</contrib-id><contrib-id contrib-id-type="spin">8542-1720</contrib-id><name-alternatives><name xml:lang="en"><surname>Morozov</surname><given-names>Sergey P.</given-names></name><name xml:lang="ru"><surname>Морозов</surname><given-names>Сергей Павлович</given-names></name><name xml:lang="zh"><surname></surname><given-names></given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Dr.Sci. (Med), Professor</p></bio><bio xml:lang="ru"><p>д.м.н., профессор</p></bio><email>morozov@npcmr.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-9661-0254</contrib-id><contrib-id contrib-id-type="spin">8592-0558</contrib-id><name-alternatives><name xml:lang="en"><surname>Reshetnikov</surname><given-names>Roman V.</given-names></name><name xml:lang="ru"><surname>Решетников</surname><given-names>Роман Владимирович</given-names></name><name xml:lang="zh"><surname></surname><given-names></given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>Cand.Sci. (Phys-Math)</p></bio><bio xml:lang="ru"><p>к.ф.-м.н.</p></bio><email>reshetnikov@fbb.msu.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-1816-1315</contrib-id><contrib-id contrib-id-type="spin">6810-3279</contrib-id><name-alternatives><name xml:lang="en"><surname>Gombolevskiy</surname><given-names>Victor A.</given-names></name><name xml:lang="ru"><surname>Гомболевский</surname><given-names>Виктор Александрович</given-names></name><name xml:lang="zh"><surname></surname><given-names></given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Cand.Sci. (Med)</p></bio><bio xml:lang="ru"><p>к.м.н.</p></bio><email>gombolevskiy@npcmr.ru</email><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="spin">6907-5936</contrib-id><name-alternatives><name xml:lang="en"><surname>Ledikhova</surname><given-names>Natalya V.</given-names></name><name xml:lang="ru"><surname>Ледихова</surname><given-names>Наталья Владимировна</given-names></name><name xml:lang="zh"><surname></surname><given-names></given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>n.ledikhova@npcmr.ru</email><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2681-9378</contrib-id><contrib-id contrib-id-type="spin">3306-1387</contrib-id><name-alternatives><name xml:lang="en"><surname>Blokhin</surname><given-names>Ivan A.</given-names></name><name xml:lang="ru"><surname>Блохин</surname><given-names>Иван Андреевич</given-names></name><name xml:lang="zh"><surname></surname><given-names></given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><email>i.blokhin@npcmr.ru</email><xref ref-type="aff" rid="aff3"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-7826-5135</contrib-id><contrib-id contrib-id-type="spin">8088-9921</contrib-id><name-alternatives><name xml:lang="en"><surname>Mokienko</surname><given-names>Olesya A.</given-names></name><name xml:lang="ru"><surname>Мокиенко</surname><given-names>Олеся Александровна</given-names></name><name xml:lang="zh"><surname></surname><given-names></given-names></name></name-alternatives><address><country country="RU">Russian Federation</country></address><bio xml:lang="en"><p>MD, Cand.Sci. (Med)</p></bio><bio xml:lang="ru"><p>к.м.н.</p></bio><email>o.mokienko@npcmr.ru</email><xref ref-type="aff" rid="aff3"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Moscow Center for Diagnostics and Telemedicine</institution></aff><aff><institution xml:lang="ru">Научно-практический клинический центр диагностики и телемедицинских технологий Департамента здравоохранения города Москвы</institution></aff><aff><institution xml:lang="zh"></institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">I.M. Sechenov First Moscow State Medical University (Sechenov University)</institution></aff><aff><institution xml:lang="ru">Первый Московский государственный медицинский университет имени И.М. Сеченова (Сеченовский Университет)</institution></aff><aff><institution xml:lang="zh"></institution></aff></aff-alternatives><aff-alternatives id="aff3"><aff><institution xml:lang="en">Moscow Center for Diagnostics and Telemedicine</institution></aff><aff><institution xml:lang="ru">Научно-практический клинический центр диагностики и телемедицинских технологий Департамента здравоохранения города Москвы</institution></aff></aff-alternatives><pub-date date-type="preprint" iso-8601-date="2021-03-30" publication-format="electronic"><day>30</day><month>03</month><year>2021</year></pub-date><pub-date date-type="pub" iso-8601-date="2021-04-30" publication-format="electronic"><day>30</day><month>04</month><year>2021</year></pub-date><volume>2</volume><issue>1</issue><issue-title xml:lang="en"/><issue-title xml:lang="ru"/><issue-title xml:lang="zh"/><fpage>5</fpage><lpage>16</lpage><history><date date-type="received" iso-8601-date="2020-10-12"><day>12</day><month>10</month><year>2020</year></date><date date-type="accepted" iso-8601-date="2021-02-09"><day>09</day><month>02</month><year>2021</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2021, Morozov S.P., Reshetnikov R.V., Gombolevskiy V.A., Ledikhova N.V., Blokhin I.A., Mokienko O.A.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2021, Морозов С.П., Решетников Р.В., Гомболевский В.А., Ледихова Н.В., Блохин И.А., Мокиенко О.А.</copyright-statement><copyright-statement xml:lang="zh">Copyright ©; 2021, Morozov S., Reshetnikov R., Gombolevskiy V., Ledikhova N., Blokhin I., Mokienko O.</copyright-statement><copyright-year>2021</copyright-year><copyright-holder xml:lang="en">Morozov S.P., Reshetnikov R.V., Gombolevskiy V.A., Ledikhova N.V., Blokhin I.A., Mokienko O.A.</copyright-holder><copyright-holder xml:lang="ru">Морозов С.П., Решетников Р.В., Гомболевский В.А., Ледихова Н.В., Блохин И.А., Мокиенко О.А.</copyright-holder><copyright-holder xml:lang="zh">Morozov S., Reshetnikov R., Gombolevskiy V., Ledikhova N., Blokhin I., Mokienko O.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by-nc-nd/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://jdigitaldiagnostics.com/DD/article/view/46818">https://jdigitaldiagnostics.com/DD/article/view/46818</self-uri><abstract xml:lang="en"><p><bold><italic>BACKGROUND</italic></bold><italic>:</italic> In Russia, a semi-quantitative CT 0–4 scoring system is used in the analysis of thoracic computed tomography (CT) scans of COVID-19 patients to grade the severity of lung lesions. Despite the widespread use of this approach, the scoring system’s diagnostic accuracy for identification hospitalizations for patients with the disease is currently unknown.</p> <p><bold><italic>AIM</italic></bold><italic>: </italic>To evaluate the sensitivity, specificity, positive (PPV) and negative (NPV) predictive value of the CT 0–4 system for the triage of COVID-19 patients.</p> <p><bold><italic>MATERIALS AND METHODS</italic></bold><italic>: </italic>This retrospective study enrolled 575 patients of Moscow clinics with laboratory-verified COVID-19, aged 57.2±13.9 years, 55% females. All patients were examined with four consecutive chest CT scans, and the disease severity was assessed using the CT 0–4 scoring system. Sensitivity and specificity were calculated as conditional probabilities that a patient would experience clinical improvement or deterioration, depending on the preceding CT examination results. For the calculation of the NPV and PPV, we estimated the COVID-19 prevalence in Moscow. The data on total cases of COVID-19 from March 6 to November 28, 2020, were taken from the Rospotrebnadzor website. We used several ARIMA and EST models with different parameters to fit the data and forecast the incidence.</p> <p><bold><italic>RESULTS</italic></bold><italic>: </italic>The median specificity of the CT 0–4 scoring system was 69% (95% CI 32%, 100%), and the sensitivity was 92% (95% CI 74%, 100%). The best statistical model describing the epidemiological situation in Moscow was ARIMA (0,2,1). According to our calculations, with the predicted point prevalence of 9.6%, the values of PPV and NPV were 56% and 97%, correspondingly.</p> <p><bold><italic>CONCLUSION</italic></bold><italic>:</italic> The maximum Youden’s index was observed for the period between the first and the second chest CT examinations when the majority of the included patients experienced clinical deterioration. The CT 0–4 scoring system makes it possible to safely exclude the development of pathological changes in patients with mild and moderate disease (categories CT-0 and CT-1), thereby optimizing the burden on hospitals in an unfavorable epidemic situation.</p></abstract><trans-abstract xml:lang="ru"><p><bold><italic>Обоснование</italic></bold>. Для выявления COVID-19-пневмоний, их осложнений и дифференциальной диагностики с другими заболеваниями лёгких, а также с целью сортировки пациентов в Российской Федерации применяют компьютерную томографию органов грудной клетки (КТ ОГК) с оценкой изменений по визуальной полуколичественной шкале КТ 0–4. Несмотря на широкое применение инструмента, численные показатели его диагностической точности в определении необходимости госпитализации пациентов с COVID-19 на настоящий момент неизвестны.</p> <p><bold><italic>Цель</italic></bold><italic> ―</italic> определение значений чувствительности, специфичности, положительной и отрицательной прогностической значимости шкалы.</p> <p><bold><italic>Материал и методы</italic></bold>. К участию в исследовании привлекли 575 пациентов (55% женщины) в возрасте 57,2±13,9 лет с лабораторно подтверждённым COVID-19. Для каждого пациента проводили по четыре последовательных исследования КТ ОГК с оценкой степени тяжести заболевания по шкале КТ 0–4. Чувствительность и специфичность рассчитывали как условную вероятность ухудшения или улучшения состояния пациента в зависимости от результатов предыдущего исследования КТ. Для расчёта положительной (PPV) и отрицательной (NPV) прогностической значимости проводили оценку распространённости COVID-19 в Москве. Данные обо всех случаях заболевания COVID-19 в период с 6 марта по 28 ноября 2020 г. взяты с сайта Роспотребнадзора. Использовали ряд моделей ARIMA и EST с различными параметрами для подбора наилучшего соответствия имеющимся данным и прогноза развития заболеваемости.</p> <p><bold><italic>Результаты</italic></bold>. Шкала оценки КТ 0–4 продемонстрировала медианные специфичность 69% и чувствительность 92%. Лучшей статистической моделью для описания эпидемиологической ситуации в Москве являлась ARIMA (0,2,1). Согласно проведённым подсчётам, при предсказанной годовой заболеваемости в 9,6% значения PPV и NPV составляют 56 и 97% соответственно.</p> <p><bold><italic>Заключение</italic></bold>. Максимальный индекс Юдена наблюдали на этапе между первым и вторым исследованием КТ ОГК, когда большинство пациентов в выборке демонстрировали тенденцию к ухудшению клинического состояния. Шкала КТ 0–4 позволяет безопасно исключить развитие патологических изменений у пациентов с лёгким и среднетяжёлым течением заболевания (категории КТ0 и КТ1), способствуя оптимизации нагрузки на стационары при неблагоприятной эпидемической обстановке.</p></trans-abstract><trans-abstract xml:lang="zh"><p><bold>论证</bold>：在俄罗斯联邦，为了检测COVID-19肺炎及其并发症和与其他肺部疾病的鉴别诊断，以及对患者进行分类，使用了胸部CT，并在CT 0–4的半定量视觉尺度上评估变化。尽管胸部CT广泛使用，但其用于确定COVID-19患者住院需求的诊断准确性的数字指标目前尚不清楚。</p> <p><bold>目的</bold>： 是确定该量表的敏感性、特异性、阳性预测值、阴性预测值。</p> <p><bold>材料与方法</bold>：研究涉及575名经实验室确诊的COVID-19患者（55%为女性），年龄为57.2±13.9岁。对于每个患者，进行了4次连续的胸部CT研究，并对疾病的严重程度进行了CT评分（0–4）。根据既往CT研究结果，将敏感性和特异性作为患者病情恶化或改善的条件概率进行计算。为计算阳性预测值（PPV）和阴性预测值（NPV），对COVID-19在莫斯科的流行情况进行了估计。2020年3月6日至11月28日期间所有COVID-19病例的数据来自俄国国家管理的保护消费者服务机构（Rospotrebnadzor）网站。使用了许多具有不同参数的ARIMA和EST模型来选择与现有数据最匹配的模型，并预测发病率的发展。</p> <p><bold>结果</bold>：0–4 CT分级的中位特异性为69%，敏感性为92%。描述莫斯科流行病学情况的最佳统计模型是ARIMA（0,2,1）。经计算，预测年发病率为9.6%，PPV值为56，NPV值为97%。</p> <p><bold>结果</bold>：Yuden指数最大的阶段出现在胸部CT第一次研究和第二次研究之间，此时样本中大多数患者表现出临床病情恶化的趋势。0–4 CT分级可以安全地排除轻、中度病程（CT0、CT1类）患者的病理变化发展，有助于优化患者在疫情不利的情况下住院。</p></trans-abstract><kwd-group xml:lang="en"><kwd>COVID-19</kwd><kwd>computed tomography</kwd><kwd>sensitivity</kwd><kwd>specificity</kwd><kwd>triage</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>COVID-19</kwd><kwd>компьютерная томография</kwd><kwd>чувствительность</kwd><kwd>специфичность</kwd><kwd>сортировка пациентов</kwd></kwd-group><kwd-group xml:lang="zh"><kwd>COVID-19</kwd><kwd>计算机断层扫描</kwd><kwd>敏感性</kwd><kwd>特异性</kwd><kwd>病人排序</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Coronavirus update (live) [cited 2002 Oct 20]. 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