Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome.
To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 10...
| Publicado en: | Journal of Digital Imaging Vol. 34; no. 2; pp. 320 - 330 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151472681&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151472681 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2021 vid: 34 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151472681 148936204 151472681 151472681 10.1007/s10278-021-00430-9 151472681 ppf: 320 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome. aug: au: Homayounieh, Fatemeh Bezerra Cavalcanti Rockenbach, Marcio Aloisio Ebrahimian, Shadi Doda Khera, Ruhani Bizzo, Bernardo C. Buch, Varun Babaei, Rosa Karimi Mobin, Hadi Mohseni, Iman Mitschke, Matthias Zimmermann, Mathis Durlak, Felix Rauch, Franziska Digumarthy, Subba R Kalra, Mannudeep K. affil: Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA sug: subj: Severity of Illness Pneumonia Tomography, X-Ray Outcomes (Health Care) Deep Learning Human Nonexperimental Studies Multivariate Analysis of Variance Descriptive Statistics Confidence Intervals ab: To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ −200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94–0.97) as well as for predicting patient outcome (AUC 0.7–0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76–0.82, 95% confidence interval (CI) 0.73–0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69–0.77, 95% CI 0.69–0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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