Purpose This study aimed to identify predictive (bio-)markers for COVID-19 severity derived from automated quantitative thin slice low dose volumetric CT analysis, clinical chemistry and lung function testing. Methods Seventy-four COVID-19 patients admitted between March 16th and June 3rd 2020 to the Asklepios Lung Clinic Munich-Gauting, Germany, were included in the study. Patients were categorized in a non-severe group including patients hospitalized on general wards only and in a severe group including patients requiring intensive care treatment. Fully automated quantification of CT scans was performed via IMBIO CT Lung Texture analysis™ software. Predictive biomarkers were assessed with receiver-operator-curve and likelihood analysis. Results Fifty-five patients (44% female) presented with non-severe COVID-19 and 19 patients (32% female) with severe disease. Five fatalities were reported in the severe group. Accurate automated CT analysis was possible with 61 CTs (82%). Disease severity was linked to lower residual normal lung (72.5% vs 87%, p = 0.003), increased ground glass opacities (GGO) (8% vs 5%, p = 0.031) and increased reticular pattern (8% vs 2%, p = 0.025). Disease severity was associated with advanced age (76 vs 59 years, p = 0.001) and elevated serum C-reactive protein (CRP, 92.2 vs 36.3 mg/L, p < 0.001), lactate dehydrogenase (LDH, 485 vs 268 IU/L, p < 0.001) and oxygen supplementation (p < 0.001) upon admission. Predictive risk factors for the development of severe COVID-19 were oxygen supplementation, LDH >313 IU/L, CRP >71 mg/L, <70% normal lung texture, >12.5% GGO and >4.5% reticular pattern. Conclusion Automated low dose CT analysis upon admission might be a useful tool to predict COVID-19 severity in patients.
Background The aim of this retrospective study was to investigate the implementation of measures to prevent perioperative COVID-19 in thoracic surgery during the first wave of the COVID-19 pandemic 2020 allowing a continued surgical treatment of patients. Methods The implemented preventive measures in patient management of the thoracic surgery department of the Asklepios Lung Clinic Munich-Gauting, Germany were retrospectively analyzed. Postoperative COVID-19 incidence before and after implementation of preventive measures was investigated. Patients admitted for thoracic surgical procedures between March and May 2020 were included in the study. Patient characteristics were analyzed. For the early detection of putative postoperative COVID-19 symptoms, typical post-discharge symptomatology of thoracic surgery patients was compared to non-surgical patients hospitalized for COVID-19. Results Thirty-five surgical procedures and fifty-seven surgical procedures were performed before and after implementation of the preventive measures, respectively. Three patients undergoing thoracic surgery before implementation of preventive measures developed a COVID-19 pneumonia post-discharge. After implementation of preventive measures, no postoperative COVID-19 cases were identified. Fever, dyspnea, dry cough and diarrhea were significantly more prevalent in COVID-19 patients compared to normally recovering thoracic surgery patients, while anosmia, phlegm, low energy levels, body ache and nausea were similarly frequent in both groups. Conclusions Based on the lessons learned during the first pandemic wave, we here provide a blueprint for successful easily implementable preventive measures minimizing SARS-CoV-2 transmission to thoracic surgery patients perioperatively. While symptoms of COVID-19 and the normal postoperative course of thoracic surgery patients substantially overlap, we found dyspnea, fever, cough, and diarrhea significantly more prevalent in COVID-19 patients than in normally recovering thoracic surgery patients. These symptoms should trigger further diagnostic testing for postoperative COVID-19 in thoracic surgery patients.
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