Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings.

OBJECTIVE: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. MATERIALS AND METHODS: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. RESULTS: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88). CONCLUSION: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

Survival from methicillin-sensitive Staphylococcus aureus bloodstream infections over 20 years: a cohort of 1328 patients.

PURPOSE: Staphylococcus aureus bloodstream infections (SA BSI) are associated with substantial mortality. The rapid emergence of methicillin-resistant S. aureus (MRSA), known to be associated with worse outcome, may blur advances made regarding mortality attributed to SA BSI caused by methicillin-sensitive S. aureus (MSSA) strains. In the unusual setting of a very low MRSA prevalence institution, we investigated incidence, mortality and trends of BSI caused by MSSA over the last 20 years. OBJECTIVE: To evaluate and demonstrate trends in incidence and mortality of MSSA BSI as well as risk factors for mortality. METHODS: Retrospective, observational analysis of the prospective bloodstream infection cohort at the University Hospital Basel between January 1993 and December 2013. All patients with blood cultures positive for MSSA were included. All patients were analysed regarding pertinent demographic, clinical and antimicrobial treatment data. We calculated incidence, temporal trends and mortality of MSSA BSI. RESULTS: 1328 episodes of MSSA BSI were identified, accounting for a yearly incidence ranging from 2.1 to 4.5 per 10 000 patient-days (p = 0.2 for trend). Overall mortality was 19.3% and did not improve over time. Community-acquired MSSA BSI significantly increased over time, while nosocomial cases decreased (p <0.05). CONCLUSIONS: Mortality related to MSSA BSI remains high and unchanged over the last 20 years. Despite advances in treatment and supportive care in medicine during the last 20 years survival did not improve and, therefore, new approaches are required to lower mortality in MSSA BSI.