ABSTRACT
Background: Convalescent plasma treatment of severe and critically ill Corona Virus Disease 2019(COVID-19) patients is still controversial. Objective: To evaluate the efficacy and safety of convalescent plasma in patients with severe COVID-19 infection and critically ill patients, We performed a meta-analysis and systematic review of convalescent plasma therapy in severe and critically ill COVID-19 patients. Methods: : We conducted a literature search in electronic data and citations of previously published systematic reviews. We included only randomized controlled studies on convalescent plasma for the treatment of severe and critically ill COVID-19 patients. Results: : A total of 7 randomized controlled trials and 1363 patients were included in the meta-analysis. Compared to patients of the control group, there was no difference in clinical improvement (Four studies, RR 1.06, 95% CI 0.96 to 1.17, p = 0.22, moderate certainty) and mortality (seven studies, RR 0.86, 95% CI 0.66 to 1.11, p = 0.48, moderate certainty) for patients of convalescent plasma therapy group. Conclusion: Convalescent plasma does not reduce the improvement of symptoms and the risk of death in severely infected and critically ill COVID-19 patients
Subject(s)
COVID-19 , Virus DiseasesABSTRACT
Background: Extracorporeal membrane oxygenation (ECMO) is a rapidly evolving therapy for acute lung and/or heart failure. However, information on the application of ECMO in severe coronavirus disease 2019 (COVID-19) is limited, such as the initiation time, especially in the ECMO instrument shortages period and regions, not all the listed patients could be treated with ECMO in time. This study aims to investigate and clear the timing of ECMO initiation related to the prognosis of severe COVID-19 patients. And emphasize the initiation time of ECOM application no more than 24 hours, when the ECMO completion trigger is tripped.Methods: In this retrospective, multi-center cohort study, we enrolled all ECMO patients with confirmed COVID-19 at three hospitals between Dec 29, 2019 and Apr 5, 2020. Demographic data, clinical presentation, laboratory profile, clinical course, treatments, complications and outcomes were collected. The primary outcomes were analyzed by ECMO weaning rate and 60-day mortality after ECMO.Results: A total of 31 patients were included in the analysis, 60-day mortality rate after ECMO was 71% and ECMO weaning rate was 26%. Due to ECMO instrument shortages, patients were divided into delayed ECMO groups (3 [IQR, 2-5] days) and early ECMO groups (0.5 [IQR, 0-1] days) based on the initiation time of ECMO. There were 14 patients in the early treatment group and 17 patients in the delayed group. Early initiation of ECMO was associated with decreased 60-day mortality after ECMO (50% vs. 88%, P=0.044) and increased ECMO weaning rate (50% vs. 6%, P=0.011).Conclusions: In the ECMO supported COVID-19 patients, delayed initiation of ECMO is a risk factor and associated with a poorer prognosis for these patients.Trial Registration: Chinese Clinical Trial Registry identifier: ChiCTR2000030947.Funding Statement: Not applicable.Declaration of Interests: The authors declare that they have no competing interests.Ethics Approval Statement: The study was approved by Jinyintan Hospital ethics board.
Subject(s)
COVID-19 , Heart FailureABSTRACT
Background: Non-invasive respiratory therapies (NIRTs) (high flow nasal cannula and non-invasive ventilation) are widely used in COVID-19 patients who developed acute respiratory failure. However, use of these therapies may delay initiation of invasive mechanical ventilation in some patients and hence worsen their outcome. This study set out to identify early predictors of NIRT failure and to develop a simple-to-use nomogram and an online calculator identifying patients at high risk of NIRT failure. Methods: A retrospective cohort of 652 COVID-19 patients with ARF who received NIRTs, was used to develop early predictors of NIRT failure, defined as subsequent need for invasive mechanical ventilation or death within 28 days after ICU admission. Multivariate logistic analysis was used to develop the nomogram and ten-fold cross-validation was applied to internally validate the stability of the model. Findings: The failure rate of NITRs was 63% (415/652). The ROX index (ratio of pulse oximetry oxygen saturation/fraction of inspired oxygen to respiratory rate), age, GCS score, and use of vasoprEssors on the first day of NIRTs were independent risk factors for NIRT failure (RAGE factors). Based on the multivariate analysis, the RAGE nomogram for NIRTs failure had a C-statistics of 0 . 83 (95% CI:0 . 80–0 . 87). An internal validation demonstrated that the mean C-statistic remained stable (C-statistics=0 . 84±0 . 03). Internal calibration was excellent (calibration slope=1). Interpretation: The nomogram and online calculator are relatively simple-to-use and able to predict the risk of NIRT failure in COVID-19 patients with acute respiratory failure. Funding Statement: This work was supported by Key Research and Development Plan of Jiangsu Province (BE2018743 and BE2019749) and Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences (CIFMS) from Chinese Academy of Medical Sciences (2016-I2M-1-014).Declaration of Interests: All authors have completed the Unified Competing Interest form (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.Ethics Approval Statement: The study was approved by the ethics committee of Jin Yintan Hospital (KY-2020-10.02). Informed consent was waived due to the retrospective and observational nature of the study.
Subject(s)
COVID-19 , Respiratory InsufficiencyABSTRACT
Background: High-flow nasal cannula (HFNC) oxygen therapy provides effective respiratory support in patients with hypoxemic respiratory failure. However, the efficacy of HFNC for patients with COVID-19 has not been established. This study was performed to assess the efficacy of HFNC for patients with COVID-19 and describe early predictors of HFNC treatment success in order to develop a prediction tool that accurately identifies the need for invasive mechanical ventilation (IMV). Methods: We retrospectively reviewed the records of patients with COVID-19 who underwent HFNC in 2 hospitals in Wuhan between 1 January and 1 March 2020. Overall survival, the success rate of HFNC treatment and respiratory variables to predict the outcome of HFNC treatment were evaluated.Results: A total of 105 patients were analyzed. Of these, 65 patients (61.9%) showed improved oxygenation and were successfully withdrawn from HFNC. The oxygenation index (PaO2/FiO2), Oxygen saturation index (SpO2/FiO2) and respiratory rate-oxygenation index (ROX index: SpO2/FiO2*RR) at 6h, 12h and 24h of HFNC initiation were closely related to the prognosis. The best predictor was the ROX index at 24h after initiating HFNC (area under the receiver operating characteristic curve, 0.874). In the multivariate logistic regression analysis, young age, gender of female, and lower SOFA score all have predictive value, while a ROX index greater than 6.10 at 24 h after initiation was significantly associated with HFNC success (OR, 104.212; 95% CI, 11.399-952.757; p＜0.001).Conclusions: Our study indicated that HFNC was an effective way of respiratory support in the treatment of severe COVID-19. The ROX index greater than 6.10 at 24 h after initiating HFNC was a good predictor of successful HFNC treatment.
Subject(s)
COVID-19 , Respiratory InsufficiencyABSTRACT
Background: COVID-19 pandemic has developed rapidly and the ability to stratify the most vulnerable patients is vital. However, routinely used severity scoring systems are often low on diagnosis, even in non-survivors. Therefore, clinical prediction models for mortality are urgently required. Methods: We developed and internally validated a multivariable logistic regression model to predict inpatient mortality in COVID-19 positive patients using data collected retrospectively from Tongji Hospital, Wuhan (299 patients). External validation was conducted using a retrospective cohort from Jinyintan Hospital, Wuhan (145 patients). Nine variables commonly measured in these acute settings were considered for model development, including age, biomarkers and comorbidities. Backwards stepwise selection and bootstrap resampling were used for model development and internal validation. We assessed discrimination via the C statistic, and calibration using calibration-in-the-large, calibration slopes and plots. Findings: The final model included age, lymphocyte count, lactate dehydrogenase and SpO2 as independent predictors of mortality. Discrimination of the model was excellent in both internal (c=0.89) and external (c=0.98) validation. Internal calibration was excellent (calibration slope=1). External validation showed some over-prediction of risk in low-risk individuals and under-prediction of risk in high-risk individuals prior to recalibration. Recalibration of the intercept and slope led to excellent performance of the model in independent data. Interpretation: COVID-19 is a new disease and behaves differently from common critical illnesses. This study provides a new prediction model to identify patients with lethal COVID-19. Its practical reliance on commonly available parameters should improve usage of limited healthcare resources and patient survival rate.