Abdominal Imaging Findings on Computed Tomography as a Tool for COVID-19 Mortality Risk Assessment: Comparison With Chest Radiograph Severity Scores.

OBJECTIVE: To quantify the association between computed tomography abdomen and pelvis with contrast (CTAP) findings and chest radiograph (CXR) severity score, and the incremental effect of incorporating CTAP findings into predictive models of COVID-19 mortality. METHODS: This retrospective study was performed at a large quaternary care medical center. All adult patients who presented to our institution between March and June 2020 with the diagnosis of COVID-19 and had a CXR up to 48 hours before a CTAP were included. Primary outcomes were the severity of lung disease before CTAP and mortality within 14 and 30 days. Logistic regression models were constructed to quantify the association between CXR score and CTAP findings. Penalized logistic regression models and random forests were constructed to identify key predictors (demographics, CTAP findings, and CXR score) of mortality. The discriminatory performance of these models, with and without CTAP findings, was summarized using area under the characteristic (AUC) curves. RESULTS: One hundred ninety-five patients (median age, 63 years; 119 men) were included. The odds of having CTAP findings was 3.89 times greater when a CXR score was classified as severe compared with mild (P = 0.002). When CTAP findings were included in the feature set, the AUCs for 14-day mortality were 0.67 (penalized logistic regression) and 0.71 (random forests). Similar values for 30-day mortality were 0.76 and 0.75. When CTAP findings were omitted, all AUC values were attenuated. CONCLUSIONS: The CTAP findings were associated with more severe CXR score and may serve as predictors of COVID-19 mortality.

Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data.

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 14 and April 21 2020. 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 network-based scoring of chest radiograph severity.

Health for All: A Report of the 2020 Association of American Medical Colleges Learn Serve Lead Meeting.

Learn Serve Lead (LSL) is the signature annual conference of the Association of American Medical Colleges (AAMC), which focuses on the most pressing issues facing American medical practice and education. Unsurprisingly, the recent AAMC LSL conference at the end of 2020 centered on the multifaceted impacts of the COVID-19 pandemic and racial inequity upon the medical community. At the LSL meeting, national leaders, practicing physicians from diverse specialties, and medical trainees discussed the impact of these challenges and ongoing strategies to overcome them. These efforts paralleled the AAMC mission areas of community collaborations, medical education, clinical care, and research. Additionally, this focus aligns with the ACR's core purpose: to serve patients and society by empowering members to advance the practice, science, and professions of radiological care. ACR is a member of the AAMC Council of Faculty and Academic Society and seeks to collaborate with other medical specialties to promote interdisciplinary collaboration, contribute to medical education, and voice the value of medical imaging for patient care. We summarize the major insights of this interdisciplinary conference and present tailored recommendations for applying these insights specifically within the radiology community. In addition, we review the parallels between the ACR and the AAMC strategic plans.