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.

Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study.

BACKGROUND: Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio <1 in CT) and COVID-19 infections severity, wherein severity is defined as requiring invasive mechanical ventilation, extracorporeal membrane oxygenation, death. METHODS: DeHFt comprises two steps. First, a deep-learning-based segmentation model (3D residual-UNet) is trained (N.ß=.ß80) to segment the liver and spleen. Second, CT attenuation is estimated using slice-based and volumetric-based methods. DeHFt-based mean liver and liver-to-spleen attenuation are compared with an expert's ROI-based measurements. We further obtained the liver-to-spleen attenuation ratio in a large multi-site cohort of patients with COVID-19 infections (D1, N.ß=.ß805; D2, N.ß=.ß1917; D3, N.ß=.ß169) using the DeHFt pipeline and investigated the association between HS and COVID-19 infections severity. FINDINGS: The DeHFt pipeline achieved a dice coefficient of 0.95, 95% CI [0.93...0.96] on the independent validation cohort (N.ß=.ß49). The automated slice-based and volumetric-based liver and liver-to-spleen attenuation estimations strongly correlated with expert's measurement. In the COVID-19 cohorts, severe infections had a higher proportion of patients with HS than non-severe infections (pooled OR.ß=.ß1.50, 95% CI [1.20...1.88], P.ß<.ß.001). INTERPRETATION: The DeHFt pipeline enabled accurate segmentation of liver and spleen on non-contrast CTs and automated estimation of liver and liver-to-spleen attenuation ratio. In three cohorts of patients with COVID-19 infections (N.ß=.ß2891), HS was associated with disease severity. Pending validation, DeHFt provides an automated CT-based metabolic risk assessment. FUNDING: For a full list of funding bodies, please see the Acknowledgements.

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.

Gastrointestinal Symptoms Onset in COVID-19 Patients in Wuhan, China.

BACKGROUND: Early detection is critical in limiting the spread of 2019 novel coronavirus (COVID-19). Although previous data revealed characteristics of GI symptoms in COVID-19, for patients with only GI symptoms onset, their diagnostic process and potential transmission risk are still unclear. METHODS: We retrospectively reviewed 205 COVID-19 cases from January 16 to March 30, 2020, in Renmin Hospital of Wuhan University. All patients were confirmed by virus nuclei acid tests. The clinical features and laboratory and chest tomographic (CT) data were recorded and analyzed. RESULTS: A total of 171 patients with classic symptoms (group A) and 34 patients with only GI symptoms (group B) were included. In patients with classical COVID-19 symptoms, GI symptoms occurred more frequently in severe cases compared to non-severe cases (20/43 vs. 91/128, respectively, p < 0.05). In group B, 91.2% (31/34) patients were non-severe, while 73.5% (25/34) patients had obvious infiltrates in their first CT scans. Compared to group A, group B patients had a prolonged time to clinic services (5.0 days vs. 2.6 days, p < 0.01) and a longer time to a positive viral swab normalized to the time of admission (6.9 days vs. 3.3 days, respectively, p < 0.01). Two patients in group B had family clusters of SARS-CoV-2 infection. CONCLUSION: Patients with only GI symptoms of COVID-19 may take a longer time to present to healthcare services and receive a confirmed diagnosis. In areas where infection is rampant, physicians must remain vigilant of patients presenting with acute gastrointestinal symptoms and should do appropriate personal protective equipment.

Do underlying cardiovascular diseases have any impact on hospitalised patients with COVID-19?

OBJECTIVES: An outbreak of the highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has sickened thousands of people in China. The purpose of this study was to explore the early clinical characteristics of COVID-19 patients with cardiovascular disease (CVD). METHODS: This is a retrospective analysis of patients with COVID-19 from a single centre. All patients underwent real-time reverse transcription PCR for SARS-CoV-2 on admission. Demographic and clinical factors and laboratory data were reviewed and collected to evaluate for significant associations. RESULTS: The study included 541 patients with COVID-19. A total of 144 (26.6%) patients had a history of CVD. The mortality of patients with CVD reached 22.2%, which was higher than that of the overall population of this study (9.8%). Patients with CVD were also more likely to develop liver function abnormality, elevated blood creatinine and lactic dehydrogenase (p<0.05). Symptoms of sputum production were more common in patients with CVD (p=0.026). Lymphocytes, haemoglobin and albumin below the normal range were pervasive in the CVD group (p<0.05). The proportion of critically ill patients in the CVD group (27.8%) was significantly higher than that in the non-CVD group (8.8%). Multivariable logistic regression analysis revealed that CVD (OR: 2.735 (95% CI 1.495 to 5.003), p=0.001) was associated with critical COVID-19 condition, while patients with coronary heart disease were less likely to reach recovery standards (OR: 0.331 (95% CI 0.125 to 0.880), p=0.027). CONCLUSIONS: Considering the high prevalence of CVD, a thorough CVD assessment at diagnosis and early intervention are recommended in COVID-19 patients with CVD. Patients with CVD are more vulnerable to deterioration.

Characteristics of disease progress in patients with coronavirus disease 2019 in Wuhan, China.

Coronavirus disease 2019 (COVID-19) patients were classified into four clinical stages (uncomplicated illness, mild, severe and critical pneumonia) depending on disease severity. We aim to investigate the corresponding clinical, radiological and laboratory characteristics between different clinical stages. A retrospective, single-centre study of 101 confirmed patients with COVID-19 at Renmin Hospital of Wuhan University from 2 January to 28 January 2020 was enrolled; follow-up endpoint was on 8 February 2020. Clinical data were collected and compared during the course of illness. The median age of the 101 patients was 51.0 years and 33.6% were medical staff. Fever (68%), cough (50%) and fatigue (23%) are the most common symptoms. About 26% patients underwent the mechanical ventilation and 98% patients were treated with antibiotics. Thirty-seven per cent patients were cured and 11 died. On admission, the number of patients with uncomplicated illness, mild, severe and critical pneumonia were 2 [2%], 86 [85%], 11 [11%] and 2 [2%]. Forty-four of the 86 mild pneumonia progressed to severe illness within 4 days, with nine patients worsened due to critical pneumonia within 4 days. Two of the 11 severe patients improved to mild condition while three others deteriorated. Significant differences were observed among groups of different clinical stages in numbers of influenced pulmonary segments (6 vs. 12 vs. 17, P < 0.001). A significantly upward trend was witnessed in ground-glass opacities overlapped with striped shadows (33% vs. 42% vs. 55% vs. 80%, P < 0.001), while pure ground-glass opacities gradually decreased as disease progressed (45% vs. 35% vs. 24% vs. 13%, P < 0.001) within 12 days. Lymphocytes, prealbumin and albumin showed a downtrend as disease progressed from mild to severe or critical condition, an uptrend was found in white blood cells, C-reactive protein, neutrophils and lactate dehydrogenase. The proportions of serum amyloid A > 300 mg/l in mild, severe and critical conditions were 18%, 46% and 71%, respectively.