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.

Cardiogenic shock temporally associated with COVID-19 vaccination after prior COVID-19 infection: A case report.

The introduction of coronavirus 2019 (COVID-19) vaccination has been an integral force in stopping the spread of COVID-19 across the globe. While reported side effects of vaccination have predominantly been mild, in the last year reports have emerged of myocarditis following the BNT162b2 (Pfizer-BioNtech) and mRNA-1273 (Moderna) vaccinations. The adolescent and young adult population have been the population most reported, with over 1000 cases under review by the Centers for Disease Control (CDC) since April 2021. Here we report a case of a previously healthy 21-year-old male who developed Multisystem Inflammatory Syndrome in Adults (MIS-A) and following the second dose of the Pfizer-BioNtech vaccine. The young male initially presented with fever, leukocytosis with high neutrophil-lymphocyte ratio, severe cardiac illness, and positive COVID-19 nucleocapsid serology, consistent with MIS-A diagnosis. His case was complicated by cardiogenic shock, requiring brief venoarterial extracorporeal membrane oxygenation (VA-ECMO) support. While this report does not detract from the overwhelming benefit of vaccination from COVID-19, clinicians should be aware of this possible relationship in the future.

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.

COVID-19 infection triggering Thrombotic Thrombocytopenic Purpura.

We present a 62-year-old gentleman with history of Crohn's disease, G6PD deficiency, who presented with immune-mediated thrombotic thrombocytopenia purpura (iTTP) one week after the diagnosis of COVID-19 infection. He was admitted with worsening dyspnea, acute renal failure, and profound thrombocytopenia with marked schistocytosis on peripheral smear. ADAMTS13 level was severely deficient. He was treated with oral prednisone, plasma exchange and rituximab with complete clinical resolution. Given the temporal association of this recurrent episode of iTTP with COVID-19 infection and no other discernible cause, COVID-19 infection was the most likely trigger.