An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study

Objective: The disease COVID-19 has caused a widespread global pandemic with ~3. 93 million deaths worldwide. In this work, we present three models—radiomics (MRM), clinical (MCM), and combined clinical–radiomics (MRCM) nomogram to predict COVID-19-positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans. Methods: We performed a retrospective multicohort study of individuals with COVID-19-positive findings for a total of 897 patients from two different institutions (Renmin Hospital of Wuhan University, D1 = 787, and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training, Results: The three out of the top five features identified using Conclusion: The novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially require mechanical ventilation.

Integrated Clinical and CT Based Artificial Intelligence Nomogram for Predicting Severity and Need for Ventilator Support in COVID-19 Patients: A Multi-Site Study.

Almost 25% of COVID-19 patients end up in ICU needing critical mechanical ventilation support. There is currently no validated objective way to predict which patients will end up needing ventilator support, when the disease is mild and not progressed. N = 869 patients from two sites (D1: N = 822, D2: N = 47) with baseline clinical characteristics and chest CT scans were considered for this study. The entire dataset was randomly divided into 70% training, D1train (N = 606) and 30% test-set (Dtest: D1test (N = 216) + D2 (N = 47)). An expert radiologist delineated ground-glass-opacities (GGOs) and consolidation regions on a subset of D1train, (D1train_sub, N = 88). These regions were automatically segmented and used along with their corresponding CT volumes to train an imaging AI predictor (AIP) on D1train to predict the need of mechanical ventilators for COVID-19 patients. Finally, top five prognostic clinical factors selected using univariate analysis were integrated with AIP to construct an integrated clinical and AI imaging nomogram (ClAIN). Univariate analysis identified lactate dehydrogenase, prothrombin time, aspartate aminotransferase, %lymphocytes, albumin as top five prognostic clinical features. AIP yielded an AUC of 0.81 on Dtest and was independently prognostic irrespective of other clinical parameters on multivariable analysis (p<0.001). ClAIN improved the performance over AIP yielding an AUC of 0.84 (p = 0.04) on Dtest. ClAIN outperformed AIP in predicting which COVID-19 patients ended up needing a ventilator. Our results across multiple sites suggest that ClAIN could help identify COVID-19 with severe disease more precisely and likely to end up on a life-saving mechanical ventilation.

Tuberculosis in times of COVID-19.

The COVID-19 pandemic has caused widespread disruptions to tuberculosis (TB) care and service delivery in 2020, setting back progress in the fight against TB by several years. As newer COVID-19 variants continue to devastate many low and middle-income countries in 2021, the extent of this setback is likely to increase. Despite these challenges, the TB community can draw on the comprehensive approaches used to manage COVID-19 to help restore progress and mitigate the impact of COVID-19 on TB. Our team developed the 'Swiss Cheese Model for Ending TB' to illustrate that it is only through multisectoral collaborations that address the personal, societal and health system layers of care that we will end TB. In this paper, we examine how COVID-19 has impacted the different layers of TB care presented in the model and explore how we can leverage some of the lessons and outcomes of the COVID-19 pandemic to strengthen the global TB response.

SARS-CoV-2 infection in a patient on chronic hydroxychloroquine therapy: Implications for prophylaxis.

People exposed to COVID-19 have a risk of developing disease, and health care workers are at risk at a time when they are badly needed during a health care crisis. Hydroxychloroquine and chloroquine have been used as treatment and are being considered as prophylaxis. Our patient developed COVID-19 while on hydroxychloroquine and although more work is needed, this calls into question the role of these medications as preventive therapy.