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- Ingberg, Edvin, 1988-, et al.
(author)
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RT-PCR cycle threshold value in combination with visual scoring of chest computed tomography at hospital admission predicts outcome in COVID-19
- 2022
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In: Infectious Diseases. - : Taylor & Francis. - 2374-4235 .- 2374-4243. ; 54:6, s. 431-440
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Journal article (peer-reviewed)abstract
- BACKGROUND: COVID-19 has a most variable prognosis. Several risk factors for an unfavourable outcome have been identified including extensive lung involvement on chest CT and high viral load estimated by RT-PCR cycle threshold (Ct) values. We investigated Ct value for outcome prediction, relation between Ct value and extent of lung involvement on chest CT and the combination of Ct value and chest CT lung involvement to predict outcome in COVID-19.METHODS: Population-based retrospective study on all patients (n = 286) hospitalised for COVID-19 in Örebro Region, Sweden, between 1 March and 31 August 2020. Nasopharyngeal samples and chest CT at hospital admission were evaluated in relation to outcome of COVID-19.RESULTS: Both Ct value and chest CT lung involvement were independently associated with risk for ICU admission or death. Lung involvement was superior as a single parameter, but addition of Ct value increased the prediction performance. Ct value was especially useful to identify patients with high risk for severe disease despite limited lung involvement.CONCLUSIONS: The addition of RT-PCR Ct value to the assessment of lung involvement on chest CT adds valuable prognostic information in COVID-19. We believe that this information can be used to support clinical decision-making when managing COVID-19 patients.
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| 2. |
- Ahlstrand, Erik, 1974-, et al.
(author)
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Visual scoring of chest CT at hospital admission predicts hospitalization time and intensive care admission in Covid-19
- 2021
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In: Infectious Diseases. - : Taylor & Francis. - 2374-4235 .- 2374-4243. ; 53:8, s. 622-632
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Journal article (peer-reviewed)abstract
- BACKGROUND: Chest CT is prognostic in Covid-19 but there is a lack of consensus on how to report the CT findings. A chest CT scoring system, ÖCoS, was implemented in clinical routine on 1 April 2020, in Örebro Region, Sweden. The ÖCoS-severity score measures the extent of lung involvement. The objective of the study was to evaluate the ÖCoS scores as predictors of the clinical course of Covid-19.METHODS: Population based study including data from all hospitalized patients with Covid-19 in Örebro Region during March to July 2020. We evaluated the correlations between CT scores at the time of admission to hospital and intensive care in relation to hospital and intensive care length of stay (LoS), intensive care admission and death. C-reactive protein and lymphocyte count were included as covariates in multivariate regression analyses.RESULTS: In 381 included patients, the ÖCoS-severity score at admission closely correlated to hospital length of stay, and intensive care admission or death. At admission to intensive care, the ÖCoS-severity score correlated with intensive care length of stay. The ÖCoS-severity score was superior to basic inflammatory biomarkers in predicting clinical outcomes.CONCLUSION: Chest CT visual scoring at admission to hospital predicted the clinical course of Covid-19 pneumonia.
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| 3. |
- Lidén, Mats, 1974-
(author)
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The stack mode review of volumetric datasets : applications for urinary stone disease
- 2013
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Doctoral thesis (other academic/artistic)abstract
- During the last decades the acquisition and visualization of radiological images have rapidly evolved. The increasing amounts of volumetric image data particularly from modern CT systems necessitate a constant evolution of the radiological visualization techniques.The dominating display mode for volumetric images has been the stack mode display since its introduction in computerized image review. In the increasing amounts of image data, the stack mode display needs to be analyzed so that the information content in the high resolution datasets can be transformed into clinically relevant information for the management of the individual patient. In the present thesis some aspects of the stack mode display were analyzed using for the most part the size estimation of urinary stones in unenhanced CT as a model.The estimated size has an important correlation to the prognosis for spontaneous passage of an obstructing ureteral stone. In the present thesis the reader variations in the size estimation of urinary stones were quantified, using different visualization parameters and after an attempt to reduce the variations with a training session for the readers. The influence on the estimated stone size of CT image post processing parameters was quantified. A segmentation algorithm was developed and demonstrated to reduce the reader variability through reader independent computer aid. One limitation of the stack mode display concerns three-dimensional shapes, which was modeled by a comparison between the estimated length and width of urinary stones in two- and three-dimensional segmentation. The increasing number of image slices in the acquisitions introduces a need for efficient navigation of the image volumes. In the present thesis the navigation of CT datasets using different user interface devices was evaluated.The rapid evolution of the MRI and CT systems necessitates a constant refinement and evaluation of the cornerstone for radiological volumetric reviewing – the stack mode display of volumetric datasets.
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