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Objective: To predict short-term outcomes in hospitalized COVID-19 patients using a model incorporating clinical variables with automated convolutional neural network (CNN) chest radiograph analysis. Methods: A retrospective single center study was performed on patients consecutively admitted with COVID-19 between March 14th and April 21st2020. Demographic, clinical and laboratory data were collected, and automated CNN scoring of the admission chest radiograph was performed. The two outcomes of disease progression were intubation or death within 7 days and death within 14 days following admission. Multiple imputation was performed for missing predictor variables and, for each imputed data set, a penalized logistic regression model was constructed to identify predictors and their functional relationship to each outcome. Cross-validated area under the characteristic (AUC) curves were estimated to quantify the discriminative ability of each model. Results: 801 patients (median age 59; interquartile range 46–73 years, 469 men) were evaluated. 36 patients were deceased and 207 were intubated at 7 days and 65 were deceased at 14 days. Cross-validated AUC values for predictive models were 0.82 (95% CI, 0.79–0.86) for death or intubation within 7 days and 0.82 (0.78–0.87) for death within 14 days. Automated CNN chest radiograph score was an important variable in predicting both outcomes. Conclusion: Automated CNN chest radiograph analysis, in combination with clinical variables, predicts short term intubation and death in patients hospitalized for COVID-19 infection. Chest radiograph scoring of more severe disease was associated with a greater probability of adverse short-term outcome. Advances in knowledge: Model-based predictions of intubation and death in COVID-19 can be performed with high discriminative performance using admission clinical data and convolutional neural networked-based scoring of chest radiograph severity.

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Objective: Nonalcoholic fatty liver and iron overload can lead to cirrhosis requiring early detection. Magnetic resonance (MR) imaging utilizing chemical shift-encoded sequences and multi-Time of Echo single-voxel spectroscopy (SVS) are frequently used for assessment. The purpose of this study was to assess various quality factors of technical acceptability and any deficiencies in technologist performance in these fat/iron MR quantification studies.Methods: Institutional review board waived retrospective quality improvement review of 87 fat/iron MR studies performed over a 6-month period was evaluated. Technical acceptability/unacceptability for chemical shift-encoded sequences (q-Dixon and IDEAL-IQ) included data handling errors (missing maps), liver field coverage, fat/water swap, motion, or other artifacts. Similarly, data handling (missing table/spectroscopy), curve-fit, fat-and water-peak separation, and water-peak sharpness were evaluated for SVS technical acceptability.Results: Data handling errors were found in 11% (10/87) of studies with missing maps or entire sequence (SVS or q-Dixon). Twenty-seven percent (23/86) of the q-Dixon/IDEAL-IQ were technically unacceptable (incomplete liver-field [39%], other artifacts [35%], significant/severe motion [18%], global fat/water swap [4%], and multiple reasons [4%]). Twenty-eight percent (21/75) of SVS sequences were unacceptable (water-peak broadness [67%], poor curve-fit [19%] overlapping fat and water peaks [5%], and multiple reasons [9%]). Conclusions:A high rate of preventable errors in fat/iron MR quantification studies indicates the need for routine quality control and evaluation of technologist performance and technical deficiencies that may exist within a radiology practice. Potential solutions such as instituting a checklist for technologists during each acquisition procedure and routine auditing may be required.