To characterize CT-ndings of COVID-19 pneumonia and their value in diagnosis and outcome prediction. METHODS Chest CTs of 182 patients with a con rmed diagnosis of COVID-19 infection by RT-PCR were evaluated for the presence of CT-abnormalities and their frequency. Regarding the patient outcome each patient was categorized in 5 progressive stages and the duration of hospitalization was determined. Regression analysis was performed to nd which CT ndings are predictive for patient outcome and to assess prognostic factors for the hospitalization duration. RESULTS Multivariate statistical analysis con rmed a higher age (OR= 1.023, p= 0.025), a higher total visual severity score (OR= 1.038, p= 0.002) and the presence of crazy paving (OR= 2.160, p= 0.034) as predictive parameters for patient outcome. A higher total visual severity score (+ 0.134 days; p= 0.012) and the presence of pleural effusion (+ 13.985 days, p= 0.005) were predictive parameters for a longer hospitalization duration. CONCLUSIONS An increasing percentage of lung opacity as well as the presence of crazy paving and a higher age are associated with a worse patient outcome. The presence of a higher total visual severity score and pleural effusion are signi cant predictors for a longer hospitalization duration.

Purpose: To assess the interobserver and intraobserver agreement of fellowship trained chest radiologists, nonchest fellowship-trained radiologists, and fifth-year radiology residents for COVID-19-related imaging findings based on the consensus statement released by the Radiological Society of North America (RSNA). Methods: A survey of 70 chest CTs of polymerase chain reaction (PCR)-confirmed COVID-19 positive and COVID-19 negative patients was distributed to three groups of participating radiologists: five fellowship-trained chest radiologists, five nonchest fellowshiptrained radiologists, and five fifth-year radiology residents. The survey asked participants to broadly classify the findings of each chest CT into one of the four RSNA COVID-19 imaging categories, then select which imaging features led to their categorization. A 1-week washout period followed by a second survey comprised of randomly selected exams from the initial survey was given to the participating radiologists. Results: There was moderate overall interobserver agreement in each group (k coefficient range 0.45-0.52 § 0.02). There was substantial overall intraobserver agreement across the chest and nonchest groups (k coefficient range 0.61-0.67 § 0.06) and moderate overall intraobserver agreement within the resident group (k coefficient 0.58 § 0.06). For the image features that led to categorization, there were varied levels of agreement in the interobserver and intraobserver components that ranged from fair to perfect kappa values. When assessing agreement with PCR-confirmed COVID status as the key, we observed moderate overall agreement within each group. Conclusion: Our results support the reliability of the RSNA consensus classification system for COVID-19-related image findings.

Distributed design systems fundamentally preserve individual design subsystem secrecy by limiting communication across subsystems. The natural secrecy of distributed design makes it difficult for design process managers to determine the appropriate order of subsystems in the design process. In this paper, we discuss a social network theory based heuristic to prescribe the optimal order of design subsystems. We call the order of the design subsystems process architecture and we leverage concepts like ‘distance,’ ‘bridging,’ and degree centrality’ to analyze the aggregate design system and identify preferable solution process architectures. Our network theory approach only requires a manager to know which subsystems share design information. We distinguish this research from previous work by empirically validating the heuristic against a genetic algorithm for 80 randomly generated distributed design systems. The heuristic performs well against the genetic algorithm and beats it in the majority of cases. Moreover, it does so without requiring any function evaluations.

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