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Introduction: Coronavirus disease-19 (COVID-19) is a new type of epidemic pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The population is generally susceptible to COVID-19, which mainly causes lung injury. Some cases may develop severe acute respiratory distress syndrome (ARDS). Currently, ARDS treatment is mainly mechanical ventilation, but mechanical ventilation often causes ventilator-induced lung injury (VILI) accompanied by hypercapnia in 14% of patients. Extracorporeal carbon dioxide removal (ECCO2R) can remove carbon dioxide from the blood of patients with ARDS, correct the respiratory acidosis, reduce the tidal volume and airway pressure, and reduce the incidence of VILI., Case Report: Two patients with critical COVID-19 combined with multiple organ failure undertook mechanical ventilation and suffered from hypercapnia. ECCO2R, combined with continuous renal replacement therapy (CRRT), was conducted concomitantly. In both cases (No. 1 and 2), the tidal volume and positive end-expiratory pressure (PEEP) were down-regulated before the treatment and at 1.5 hours, one day, three days, five days, eight days, and ten days after the treatment, together with a noticeable decrease in PCO2 and clear increase in PO2, while FiO2 decreased to approximately 40%. In case No 2, compared with the condition before treatment, the PCO2 decreased significantly with down-regulation in the tidal volume and PEEP and improvement in the pulmonary edema and ARDS after the treatment., Conclusion: ECCO2R combined with continuous blood purification therapy in patients with COVID-19 who are criti-cally ill and have ARDS and hypercapnia might gain both time and opportunity in the treatment, down-regulate the ventilator parameters, reduce the incidence of VILI and achieve favorable therapeutic outcomes.

Background: COVID-19 has become a major global threat. The present study aimed to develop a nomogram model to predict the survival of COVID-19 patients based on their clinical and laboratory data at admission., Methods: COVID-19 patients who were admitted at Hankou Hospital and Huoshenshan Hospital in Wuhan, China from January 12, 2020 to March 20, 2020, whose outcome during the hospitalization was known, were retrospectively reviewed. The categorical variables were compared using Pearson's χ 2 -test or Fisher's exact test, and continuous variables were analyzed using Student's t-test or Mann Whitney U-test, as appropriate. Then, variables with a P-value of ≤0.1 were included in the log-binomial model, and merely these independent risk factors were used to establish the nomogram model. The discrimination of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), and internally verified using the Bootstrap method., Results: A total of 262 patients (134 surviving and 128 non-surviving patients) were included in the analysis. Seven variables, which included age (relative risk [RR]: 0.905, 95% confidence interval [CI]: 0.868-0.944; P < 0.001), chronic heart disease (CHD, RR: 0.045, 95% CI: 0.0097-0.205; P < 0.001, the percentage of lymphocytes (Lym%, RR: 1.125, 95% CI: 1.041-1.216; P = 0.0029), platelets (RR: 1.008, 95% CI: 1.003-1.012; P = 0.001), C-reaction protein (RR: 0.982, 95% CI: 0.973-0.991; P < 0.001), lactate dehydrogenase (LDH, RR: 0.993, 95% CI: 0.990-0.997; P < 0.001) and D-dimer (RR: 0.734, 95% CI: 0.617-0.879; P < 0.001), were identified as the independent risk factors. The nomogram model based on these factors exhibited a good discrimination, with an AUC of 0.948 (95% CI: 0.923-0.973)., Conclusions: A nomogram based on age, CHD, Lym%, platelets, C-reaction protein, LDH and D-dimer was established to accurately predict the prognosis of COVID-19 patients. This can be used as an alerting tool for clinicians to take early intervention measures, when necessary.