ABSTRACT
Background RSNA COVID-19 chest CT consensus guidelines are widely used, but their true positive rate for COVID-19 pneumonia has not been assessed among vaccinated patients. Purpose To assess true positive rate of RSNA typical chest CT findings of COVID-19 among fully vaccinated subjects with PCR-confirmed COVID-19 infection compared with unvaccinated subjects. Materials and Methods Patients with COVID Typical chest CT findings and one positive or two negative PCR tests for COVID-19 within 7 days of their chest CT between January 2021 - January 2022 at a quaternary academic medical center were included. True positives were defined as chest CTs interpreted as COVID Typical and PCR-confirmed COVID-19 infection within 7 days. Logistic regression models were constructed to quantify the association between PCR results and vaccination status, vaccination status and COVID-19 variants, and vaccination status and months. Results 652 subjects (median age 59, [IQR, 48-72]); 371 [57%] men) with CT scans classified as COVID Typical were included. 483 (74%) were unvaccinated and 169 (26%) were fully vaccinated. The overall true positive rate of COVID Typical CTs was lower among vaccinated versus unvaccinated (70/169 [41%; 95% CI: 34, 49%] vs 352/483 [73%; 69, 77%]; OR (95% CI): 3.8 (2.6, 5.5); P < .001). Unvaccinated subjects were more likely to have true positive CTs compared with fully vaccinated subjects during the peaks of COVID-19 variants Alpha (OR, 16 [95% CI: 6.1, 42]; P < .001) and Delta (OR, 8.3 [95% CI: 4.2, 16]; P < .001), but no statistical differences were found during the peak of Omicron variant (OR, 1.7 [95% CI: 0.27, 11]; P = .56) Conclusion Fully vaccinated subjects with confirmed COVID-19 breakthrough infections had lower true positive rates of COVID Typical chest CT findings.
ABSTRACT
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.
ABSTRACT
While COVID-19 research has seen an explosion in the literature, the impact of pandemic-related societal and lifestyle disruptions on brain health among the uninfected remains underexplored. However, a global increase in the prevalence of fatigue, brain fog, depression and other "sickness behavior"-like symptoms implicates a possible dysregulation in neuroimmune mechanisms even among those never infected by the virus. We compared fifty-seven 'Pre-Pandemic' and fifteen 'Pandemic' datasets from individuals originally enrolled as control subjects for various completed, or ongoing, research studies available in our records, with a confirmed negative test for SARS-CoV-2 antibodies. We used a combination of multimodal molecular brain imaging (simultaneous positron emission tomography / magnetic resonance spectroscopy), behavioral measurements, imaging transcriptomics and serum testing to uncover links between pandemic-related stressors and neuroinflammation. Healthy individuals examined after the enforcement of 2020 lockdown/stay-at-home measures demonstrated elevated brain levels of two independent neuroinflammatory markers (the 18 kDa translocator protein, TSPO, and myoinositol) compared to pre-lockdown subjects. The serum levels of two inflammatory markers (interleukin-16 and monocyte chemoattractant protein-1) were also elevated, although these effects did not reach statistical significance after correcting for multiple comparisons. Subjects endorsing higher symptom burden showed higher TSPO signal in the hippocampus (mood alteration, mental fatigue), intraparietal sulcus and precuneus (physical fatigue), compared to those reporting little/no symptoms. Post-lockdown TSPO signal changes were spatially aligned with the constitutive expression of several genes involved in immune/neuroimmune functions. This work implicates neuroimmune activation as a possible mechanism underlying the non-virally-mediated symptoms experienced by many during the COVID-19 pandemic. Future studies will be needed to corroborate and further interpret these preliminary findings.