Background Acute respiratory distress syndrome (ARDS) is a fatal complication of coronavirus disease 2019 (COVID-19). There are a few reports of allogeneic human mesenchymal stem cells (MSCs) as a potential treatment for ARDS. In this phase 1 clinical trial, we present the safety, feasibility, and tolerability of the multiple infusions of high dose MSCs, which originated from the placenta and umbilical cord, in critically ill COVID-19-induced ARDS patients. Methods A total of 11 patients diagnosed with COVID-19-induced ARDS who were admitted to the intensive care units (ICUs) of two hospitals enrolled in this study. The patients were critically ill with severe hypoxemia and required mechanical ventilation. The patients received three intravenous infusions (200 × 106 cells) every other day for a total of 600 × 106 human umbilical cord MSCs (UC-MSCs; 6 cases) or placental MSCs (PL-MSCs; 5 cases). Findings There were eight men and three women who were 42 to 66 years of age. Of these, six (55%) patients had comorbidities of diabetes, hypertension, chronic lymphocytic leukemia (CLL), and cardiomyopathy (CMP). There were no serious adverse events reported 24–48 h after the cell infusions. We observed reduced dyspnea and increased SpO2 within 48–96 h after the first infusion in seven patients. Of these seven patients, five were discharged from the ICU within 2–7 days (average: 4 days), one patient who had signs of acute renal and hepatic failure was discharged from the ICU on day 18, and the last patient suddenly developed cardiac arrest on day 7 of the cell infusion. Significant reductions in serum levels of tumor necrosis factor-alpha (TNF-α; P < 0.01), IL-8 (P < 0.05), and C-reactive protein (CRP) (P < 0.01) were seen in all six survivors. IL-6 levels decreased in five (P = 0.06) patients and interferon gamma (IFN-γ) levels decreased in four (P = 0.14) patients. Four patients who had signs of multi-organ failure or sepsis died in 5–19 days (average: 10 days) after the first MSC infusion. A low percentage of lymphocytes (< 10%) and leukocytosis were associated with poor outcome (P = 0.02). All six survivors were well with no complaints of dyspnea on day 60 post-infusion. Radiological parameters of the lung computed tomography (CT) scans showed remarkable signs of recovery. Interpretation We suggest that multiple infusions of high dose allogeneic prenatal MSCs are safe and can rapidly improve respiratory distress and reduce inflammatory biomarkers in some critically ill COVID-19-induced ARDS cases. Patients that develop sepsis or multi-organ failure may not be good candidates for stem cell therapy. Large randomized multicenter clinical trials are needed to discern the exact therapeutic potentials of MSC in COVID-19-induced ARDS.

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Objective To develop prognostic models for survival (alive or deceased status) prediction of COVID-19 patients using clinical data (demographics and history, laboratory tests, visual scoring by radiologists) and lung/lesion radiomic features extracted from chest CT images. Methods Overall, 152 patients were enrolled in this study protocol. These were divided into 106 training/validation and 46 test datasets (untouched during training), respectively. Radiomic features were extracted from the segmented lungs and infectious lesions separately from chest CT images. Clinical data, including patients’ history and demographics, laboratory tests and radiological scores were also collected. Univariate analysis was first performed (q-value reported after false discovery rate (FDR) correction) to determine the most predictive features among all imaging and clinical data. Prognostic modeling of survival was performed using radiomic features and clinical data, separately or in combination. Maximum relevance minimum redundancy (MRMR) and XGBoost were used for feature selection and classification. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), sensitivity, specificity, and accuracy were used to assess the prognostic performance of the models on the test datasets. Results For clinical data, cancer comorbidity (q-value < 0.01), consciousness level (q-value < 0.05) and radiological score involved zone (q-value < 0.02) were found to have high correlated features with outcome. Oxygen saturation (AUC = 0.73, q-value < 0.01) and Blood Urea Nitrogen (AUC = 0.72, q-value = 0.72) were identified as high clinical features. For lung radiomic features, SAHGLE (AUC = 0.70) and HGLZE (AUC = 0.67) from GLSZM were identified as most prognostic features. Amongst lesion radiomic features, RLNU from GLRLM (AUC = 0.73), HGLZE from GLSZM (AUC = 0.73) had the highest performance. In multivariate analysis, combining lung, lesion and clinical features was determined to provide the most accurate prognostic model (AUC = 0.95 ± 0.029 (95%CI: 0.95-0.96), accuracy = 0.88 ± 0.046 (95% CI: 0.88-0.89), sensitivity = 0.88 ± 0.066 (95% CI = 0.87-0.9) and specificity = 0.89 ± 0.07 (95% CI = 0.87-0.9)). Conclusion Combination of radiomic features and clinical data can effectively predict outcome in COVID-19 patients. The developed model has significant potential for improved management of COVID-19 patients.

Highlights CT severity score (CSS) could predict ICU admission, intubation, and mortality. Reticular pattern in lung CT scans, could be protective against adverse outcomes. CSS was weakly correlated with initial qSOFA score. CSS could not predict the length of stay in hospital.