key: cord-0817554-eofo5fw7 authors: Meiler, Stefanie; Schaible, Jan; Poschenrieder, Florian; Scharf, Gregor; Zeman, Florian; Rennert, Janine; Pregler, Benedikt; Kleine, Henning; Stroszczynski, Christian; Zorger, Niels; Hamer, Okka W. title: Can CT performed in the early disease phase predict outcome of patients with COVID 19 pneumonia? Analysis of a cohort of 64 patients from Germany date: 2020-08-28 journal: Eur J Radiol DOI: 10.1016/j.ejrad.2020.109256 sha: 9585cda2310eb15c9fa3fdf8d6e5f35f7dd89cff doc_id: 817554 cord_uid: eofo5fw7 PURPOSE: The aim of this study was to investigate if CT performed in the early disease phase can predict the course of COVID-19 pneumonia in a German cohort. METHOD: All patients with RT-PCR proven COVID-19 pneumonia and chest CT performed within 10 days of symptom onset between March 1 st and April 15th 2020 were retrospectively identified from two tertiary care hospitals. 12 CT features, their distribution in the lung and the global extent of opacifications were evaluated. For analysis of prognosis two compound outcomes were defined: positive outcome was defined as either discharge or regular ward care; negative outcome was defined as need for mechanical ventilation, treatment on intensive care unit, extracorporeal membrane oxygenation or death. Follow-up was performed until June 19th. For statistical analysis uni- und multivariable logistic regression models were calculated. RESULTS: 64 patients were included in the study. By univariable analysis the following parameters predicted a negative outcome: consolidation (p = 0.034), crazy paving (p = 0.004), geographic shape of opacification (p = 0.022), dilatation of bronchi (p = 0.002), air bronchogram (p = 0.013), vessel enlargement (p = 0.014), pleural effusion (p = 0.05), bilateral disease (p = 0.004), involvement of the upper lobes (p = 0.004, p = 0.015) or the right middle lobe (p < 0.001) and severe extent of opacifications (p = 0.002). Multivariable analysis revealed crazy paving and severe extent of parenchymal involvement to be independently predictive for a poor outcome. CONCLUSIONS: Easy to assess CT features in the early phase of disease independently predicted an adverse outcome of patients with COVID-19 pneumonia. email: stefanie.meiler@ukr.de Purpose The aim of this study was to investigate if CT performed in the early disease phase can predict the course of COVID-19 pneumonia in a German cohort. All patients with RT-PCR proven COVID-19 pneumonia and chest CT performed within 10 days of symptom onset between March 1st and April 15th 2020 were retrospectively identified from two tertiary care hospitals. The standard of reference for the diagnosis of COVID-19 is RT-PCR. Initially restricted to Wuhan, the infection spread rapidly reaching the status of a pandemic in March 2020 [2] . Meanwhile over 10 million cases have been confirmed worldwide, among them 194910 in Germany, as of June 29 2020 [3]. The majority of patients suffering from COVID-19 have no or only mild symptoms and restitute quickly. However, 15% develop severe pneumonia, and 5% critical disease including acute respiratory distress syndrome (ARDS), septic shock and multi organ failure, eventually leading to death [4] . Several studies reporting the morphology of COVID-19 pneumonia in chest computed tomography (CT) have been published [5] [6] [7] [8] [9] [10] [11] . CT features of COVID-19 pneumonia seem to be amazingly similar for many patients. CT morphology can be so suggestive that radiologists might be able to distinguish COVID-19 from pneumonias caused by other viruses [12] [13] [14] [15] . However, there are non-infectious differential diagnoses like in particular organizing pneumonia which can look very similar to COVID-19 pneumonia. Regarding sensitivity of CT studies consistently report very high numbers (circa. 95%) with some of them reporting even higher sensitivities of initial CT as compared to initial RT-PCR [17] [18] [19] . Apart from this CT plays an important role for quantification of parenchymal involvement, detection of complications (like pulmonary embolism or superinfection), detection of COVID-19 pneumonia as an "incidental" finding and even for triage in case of constrained resources. Hence, CT has emerged to a widely used tool in the care of patients with suspected or proven COVID-19. Only few studies evaluated CT features which may allow prediction of course of disease [20] [21] [22] [23] [24] . Most of these studies described Chinese cohorts. However, it has to be considered that the course of disease and patient outcome are influenced by many parameters. Among them host features (which again are not only influenced by individual factors but also by ethnicity) and viral genome variability due to mutations might affect outcome. Also organization and resources of the respective health care system determines patient outcome. Thus, evaluation of the prognostic value of CT in a European cohort seems worthwhile. So far there is only one study from Italy linking the amount of parenchymal involvement with patient outcome [25] . Italian hospitals, however, have been intermittently overwhelmed by patient number with consecutive constraints for patient care. J o u r n a l P r e -p r o o f Germany so far has been in the fortunate situation to be able to provide sufficient resources at any time. The aim of this study was to investigate if CT performed in the early disease phase can predict the course of COVID-19 pneumonia and patient outcome in a German cohort. This retrospective study was approved by the institutional ethics committee. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Written informed consent was waived. The inclusion criteria were consecutive adult patients (≥ 18 years old) with RT-PCR positive for SARS-CoV-2 and a chest CT performed within 10 days of symptom onset between March 1st and April 15th 2020. Exclusion criteria were a negative result of RT-PCR for SARS-CoV-2 and non-diagnostic CT, for example due to motion-artifacts. Patients were identified by means of a full-text database query of all CT-scans performed between March 1st and April 15th 2020 using the term "*COVID*" and *SARS* in the Radiological Information System (RIS, Nexus.medRIS, Version 8.42, Nexus, Villingen-Schwenningen, Germany). Patient characteristics (age, gender, comorbidities), symptoms, date of symptom onset, RT-PCR results and patient outcome were extracted from electronic patient records. All patients had at least one CT scan of the chest. In case of several scans per patient the first CT performed per patient was included into the analysis. Patients were followed until June 19th 2020. The patients underwent CT scans at two tertiary care hospitals. Chest CTs were performed on two Images were analyzed in consensus by two radiologists. The radiologists were blinded to clinical data, laboratory data and patient status. The Fleischner Society definition of CT features were applied when appropriate [26] . The following parameters were analyzed: ground-glass-opacities (GGO, hazy Two compound outcomes were defined as the endpoint: positive outcome was defined as either discharge or regular ward care (= group 1); negative outcome was defined to be need for intubation or need for admission to intensive care unit or need for extracorporeal membrane oxygenation (ECMO) or death (group 2). Age is presented as mean (standard deviation) and all categorical variables as absolute and relative frequencies. Univariable logistic regression models were used to analyze the predictive value of all parameters (demographic data, clinical data, comorbidities and CT findings) on the endpoint (positive vs negative outcome). Furthermore, a multivariable logistic regression model analyzing only the CT findings was calculated. At first, all significant CT findings were added to a model followed by a manual stepwise backward elimination until only predictor variables with p < 0.1 were left. A second multivariable model was built the same way including characteristics of parenchymal involvement. Due to quasi-separated data regarding the bronchial dilatation, the penalized likelihood method by and taste dysfunction (n = 11; 25%). Most common comorbidities were high blood pressure (n=23, 37%), diabetes (n=8, 12%) and coronary heart disease (n=6, 9%). Prevalence of obesity and smoking history could not be determined because of frequent lack of corresponding data in patient charts. 9 patients (14%) deceased during the observation period. Patient characteristics are presented in table 1 . 51 CTs (80%) were acquired without contrast agent, 13 CTs (20%) were contrast enhanced. Ground glass opacities (97%, n=62) and consolidation (77%, n=49) were the most commonly observed patterns. Crazy paving was identified in 25% of the scans (n=16). Bronchial dilatation was seen in 14% (n=9), air bronchogram in 67% (n=43) and vessel enlargement in 48% (n=31) of CTs. Pleural effusion was observed in 20% (n=13) and lymphadenopathy in 28% (n=18) of CTs. Most of the lesions were at least to some extent sharply marginated (84%, n=54). In 30% of CTs (n=19) opacifications were curvilinear shaped, round in 61% (n=39) and geographic in 41% (n=26). In the vast majority of CTs, lesions were found bilaterally (89%, n=57). The right lower lobe (95%, n=61) and the left lower lobe (92%, n=59) were most often affected. Distribution in the axial plane was predominantly peripheral (n = 47, 73%) and posterior (83%, n=53). In 41% (n=26) of CTs extent of opacifications was mild (Fig. 1) , in 36% (n=23) moderate (Fig. 2 ) and in 23% (n=15) severe (Fig. 3) . CT findings are presented in table 2. Restriction of opacifications to one side (unilateral) (OR n.c., p=0.022) was the only parameter correlated with a favorable outcome (Fig. 4) . Several CT features predicted an adverse outcome: Prognosis of COVID-19 pneumonia is highly variable. While circa 80% of patients show only mild or even no symptoms, 20% suffer from severe or even critical disease and eventually die [4] . Identification of factors predicting prognosis already in the early phase of disease would enable physicians to direct patients into optimal therapeutic pathways. Health care resources could be employed precisely where they are needed most. CT plays a major role in the care of patients infected with SARS-CoV-2. Few studies investigated if CT might be helpful in predicting prognosis [20] [21] [22] [23] [24] . However, most of the studies are from China. Evaluation of European cohorts in this regard is scarce [25] . Considering that outcome is influenced by many factors (eg health care resources, organization and preparedness of health care systems, host factors and viral factors influenced for example by genomic variations) we sought to investigate CT features predicting patient prognosis in a German cohort. For this we evaluated a cohort of 64 patients with RT-PCR proven COVID-19 pneumonia treated at two tertiary care centers in Regensburg, Southern Germany. We restricted the analysis to CTs performed in the early disease phase (within 10 days of symptom onset). CT morphology of the presented cohort was in accordance with previously described features. A combination of bilateral GGO and consolidation located in the periphery of the posterior segments predominantly of the lower lobes was most often seen and seems to be characteristic for COVID-19 pneumonia. For To test for our hypothesis we performed a multivariable regression analysis. The results revealed that in our cohort crazy paving was the only CT feature that independently predicted an unfavorable course of disease. The differential diagnosis of crazy paving is wide and includes pulmonary edema and ARDS [32, 33] . Both phenomena are thought to be the major pathophysiological events in severe COVID-19 pneumonia [34] . Thus, crazy paving might be an easy to assess surrogate parameter for severe lung injury by COVID-19 pneumonia. The presence of consolidation and bronchial dilatation did not reach bronchiectasis can also be linked to ARDS being a consequence of fibroproliferative processes in the surrounding lung tissue during the proliferative, second phase [35, 36] . Hence, bronchial dilatation might have been a surrogate for such a tissue response with an immanent adverse outcome. Another strong independent predictor for a poor outcome was severity of parenchymal involvement even though extent of parenchymal involvement was assessed subjectively. Our study has limitations. CT scans were performed at the discretion of the referring physician. Hence, there might be a bias towards seriously ill patients. Both recruiting hospitals were tertiary care institutions with one of them being the regional center for critically ill patients. Thus, the proportion of patients with moderately to severely involved lung parenchyma was relatively high. Differences in treatment were not systematically analyzed. We investigated the prognostic relevance of chest CT performed in the early phase of disease for 64 patients from Germany suffering from RT-PCR proven COVID-19 pneumonia. Independent predictors for an adverse outcome were crazy paving and severe parenchymal involvement. These are easy to asses CT parameters. Hence, CT might be a valuable modality to predict the course of disease and to help clinicians to adapt patient management accordingly. Table 5b Multivariable logistic regression to analyze the predictive value of significant characteristics of parenchymal involvement on the endpoint (positive vs. negative outcome) A Novel Coronavirus from Patients with Pneumonia in China World Health Organization. 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