CT-Based Risk Stratification for Intensive Care Need and Survival in COVID-19 Patients-A Simple Solution.
CT-Based Risk Stratification for Intensive Care Need and Survival in COVID-19 Patients-A Simple Solution.
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DOI:
10.3390/diagnostics11091616
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发表时间:
2021-09-04
期刊:
影响因子:
--
通讯作者:
Böning G
中科院分区:
文献类型:
--
作者:
Hosse C;Büttner L;Fleckenstein FN;Hamper CM;Jonczyk M;Scholz O;Aigner A;Böning G
We evaluated a simple semi-quantitative (SSQ) method for determining pulmonary involvement in computed tomography (CT) scans of COVID-19 patients. The extent of lung involvement in the first available CT was assessed with the SSQ method and subjectively. We identified risk factors for the need of invasive ventilation, intensive care unit (ICU) admission and for time to death after infection. Additionally, the diagnostic performance of both methods was evaluated. With the SSQ method, a 10% increase in the affected lung area was found to significantly increase the risk for need of ICU treatment with an odds ratio (OR) of 1.68 and for invasive ventilation with an OR of 1.35. Male sex, age, and pre-existing chronic lung disease were also associated with higher risks. A larger affected lung area was associated with a higher instantaneous risk of dying (hazard ratio (HR) of 1.11) independently of other risk factors. SSQ measurement was slightly superior to the subjective approach with an AUC of 73.5% for need of ICU treatment and 72.7% for invasive ventilation. SSQ assessment of the affected lung in the first available CT scans of COVID-19 patients may support early identification of those with higher risks for need of ICU treatment, invasive ventilation, or death.
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影响因子:
19.7
作者:
Kanne JP;Bai H;Bernheim A;Chung M;Haramati LB;Kallmes DF;Little BP;Rubin GD;Sverzellati N
通讯作者:
Sverzellati N
影响因子:
3.9
作者:
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通讯作者:
COVID-19 RISK and Treatments (CORIST) Collaboration
影响因子:
9.6
作者:
Lichtenstein, Daniel A.
通讯作者:
Lichtenstein, Daniel A.
DOI:
10.1148/ryct.2020200075
发表时间:
2020-04-01
期刊:
RADIOLOGY-CARDIOTHORACIC IMAGING
影响因子:
--
作者:
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通讯作者:
Xia, Liming
影响因子:
168.9
作者:
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通讯作者:
Cao, Bin