Chest computed tomography for outcome prediction in laboratory-confirmed COVID-19: A retrospective analysis of 38,051 cases

Morozov, S. P.; Chernina, Valeria; Blokhin, Ivan; Gombolevskiy, Victor A.

doi:10.17816/dd46791

Cited by 17 publications

(18 citation statements)

References 16 publications

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“…All patients referred to LDCT triage underwent brief physical examination, including pulse oximetry. A local computed tomography score (CT-score) was implemented in Russia [21]. It has five gradations (0, 1, 2, 3, 4) which are related to the volume of involved lung segments (0, < 25%, 25–50%, 50–75%, 75–100%).…”

Section: Methodsmentioning

confidence: 99%

“…Partial vaccination status was assigned to the patients who failed to meet the above criteria for complete vaccination, but had reported receiving one dose at least 14 days prior to the referral. In the secondary analyses, we used the CT-score [21] and oxygen saturation as the secondary outcomes to assess the VE against COVID-19 lung injury. Oxygen saturation was grouped in the ranges according to the NEWS2 score [23] which was well-validated in non-COVID-19 settings.…”

Section: Methodsmentioning

confidence: 99%

“…A local computed tomography score (CTscore) was implemented in Russia [20]. It has five gradations (0, 1, 2, 3, 4) which are related to the volume of involved lung segments (0, < 25%, 25-50%, 50-75%, 75-100%).…”

Section: Population and Study Designmentioning

confidence: 99%

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Vaccine Effectiveness against Referral to Hospital and Severe Lung Injury Associated with COVID-19: A Case-Control Study in St. Petersburg, Russia

Барчук

Cherkashin²,

Bulina

et al. 2021

Preprint

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Background: Results of a randomised trial showed the safety and efficacy of Gam-COVID-Vac against COVID-19. However, compared to other vaccines used across the globe, the real-world data on the effectiveness of Gam-COVID-Vac, especially against the disease caused by Delta variant of concern, was not available. We aimed to assess the effectiveness of vaccination mainly conducted with Gam-COVID-Vac in St. Petersburg, Russia. Methods: We designed a case-control study to assess the vaccine effectiveness (VE) against lung injury and referral to hospital. Self-reported vaccination status was collected for individuals with confirmed SARS-CoV-2 infection who were referred for initial low-dose computed tomography triage in two outpatient centres in July 3 -- August 9, 2021 in St. Petersburg, Russia. We used logistic regression models to estimate the adjusted (for age, and triage centre) VE for complete (>14 days after the second dose) and partial vaccination (>14 days after the first dose). We estimated the VE against referral for hospital admission, COVID-19-related lung injury assessed with LDCT, and decline in oxygen saturation. Results: In the final analysis, 13,894 patients were included, 1,291 (9.3%) of patients met our criteria for complete vaccination status, and 495 (3.6%) were referred to hospital. In the primary analysis, the adjusted VE against referral to hospital was 81% (95% CI: 68-88) for complete vaccination. The effect of the partial vaccination was not statistically significant. The VE against referral to hospital was more pronounced in women (84%, 95% CI: 66-92) compared to men (76%, 95% CI: 51-88). Vaccine protective effect increased with increasing lung injury categories, from 54% (95% CI: 48-60) against any sign of lung injury to 76% (95% CI: 59-86) against more than 50% lung involvement. A sharp increase was observed in the probability of hospital admission with age for non-vaccinated patients in relation to an almost flat relationship for the completely vaccinated group. Conclusions: COVID-19 vaccination was effective against referral to hospital in patients with symptomatic SARS-CoV-2 infection in St. Petersburg, Russia. This protection is probably mediated through VE against lung injury associated with COVID-19.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Vaccine Effectiveness against Referral to Hospital and Severe Lung Injury Associated with COVID-19: A Case-Control Study in St. Petersburg, Russia

Барчук

Cherkashin²,

Bulina

et al. 2021

Preprint

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“…Морозова и соавт. по прогнозированию летальных исходов при COVID-19 по данным КТ органов грудной клетки, при переходе из одной категории КТ в следующую риск увеличивался в среднем на 38% (95% доверительный интервал 17,1-62,6), а в дополнительном исследовании среди пациентов с лабораторно верифицированным COVID-19 риск смерти с категорией КТ-4 был выше в 3 раза, чем при КТ-0 [12,20]. В нашем исследовании…”

Section: обсуждение основного результата исследованияunclassified

“…2020; 2(1) in press DOI: https://doi.org/10.17816/DD60040 продемонстрировано, что категорий КТ-4 в тестовой подгруппе меньше, чем в контрольной. В предыдущих исследованиях показано, что оценка степени поражения лёгких, соответствующая КТ-4, связана с летальными исходами при COVID-19 больше, чем все остальные категории (КТ 0-3) [12,20]. Именно поэтому изменение количества пациентов с категориями КТ-4 имеет важное значение для формирования калькуляторов рисков летальных исходов для пациентов с COVID-19.…”

Section: N P R E S Sunclassified

How does artificial intelligence effect on the assessment of lung damage in COVID-19 on chest CT scan?

Morozov

Chernina

Andreychenko

et al. 2021

Digital Diagnostics

Self Cite

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BACKGROUND: During the pandemic, computed tomography (CT) was one of the most important tools for assessing COVID-19-related lung changes. In COVID-19 patients, radiologists in Moscow used the adapted CT0-4 scale to visually assess the dependence of the severity of the general condition on the nature and severity of radiological signs of changes in the lungs based on computed tomography. In a large stream of scans, the doctor may miss findings and make errors in assessing the volume of lung damage, so the use of AI services in outpatient healthcare during a pandemic can be beneficial. AIM: The goal of this study is to compare the distribution of CT0-4 categories designed by radiologists with the results of AI services processing and categories formed without AI services. METHODS: We used retrospective study design, full study protocol is registered on ClinicalTrials.gov (NCT04489992). The results of primary CT scans with the CT0-4 categories were analyzed in outpatient medical institutions of the Health Department from April 08, 2020, to December 01, 2020, and separately for November (from November 01, 2020, to December 01, 2020). CT was performed on 48 computed tomographs in accordance with standard protocols, and the data was processed by the single radiology information systems. CTs in the test group received AI services, while CTs in the control group did not. The analysis includes five AI services: RADLogics COVID-19 (RADLogics, USA), COVID-IRA (IRA labs, Russia), Care Mentor AI, COVID (Care Mentor AI, Russia), Third Opinion. CT-COVID-19 (Third Opinion, Russia), and COVID-MULTIVOX (Gammamed, Russia). Moreover, AI services are encoded at random. RESULTS: The CT scan results of 260,594 patients were examined (m/f % = 44/56, mean age = 49.5). The test group consisted of 115,618 CT scans, while the control group consisted of 144,976 CT scans. Depending on the specific AI service, CT0 was established by 2.318.5% less than the control group for different subgroups of categories. The categories CT3-4 were established by 4.727.6% less than without AI, and the categories CT4 by 4060% less than without AI (p 0.0001). For November (from November 01, 2020, to December 01, 2020), the CT scan results of 41,386 patients were analyzed (m/f % = 44/56, average age = 53.2 years). The test group consisted of 28,881 CT scans, while the control group included 12,505 CT scans. Depending on the specific AI service, CT0 was established by 12.6% less than the control group for different subgroups of categories. Further, the categories CT3CT4 were established by 0.215.7% less than without AI, and the categories CT4 were established by 25% less than without AI (p = 0.001). CONCLUSION: The use of AI services for primary CT scans on an outpatient basis reduces the number of CT0 and CT3CT4 results, which can influence the therapeutic approach for COVID-19 patients.

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CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification

Goncharov

Pisov

Shevtsov

et al. 2021

Medical Image Analysis

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Chest computed tomography for outcome prediction in laboratory-confirmed COVID-19: A retrospective analysis of 38,051 cases

Cited by 17 publications

References 16 publications

Vaccine Effectiveness against Referral to Hospital and Severe Lung Injury Associated with COVID-19: A Case-Control Study in St. Petersburg, Russia

Vaccine Effectiveness against Referral to Hospital and Severe Lung Injury Associated with COVID-19: A Case-Control Study in St. Petersburg, Russia

How does artificial intelligence effect on the assessment of lung damage in COVID-19 on chest CT scan?

CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification

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