Validation of extracorporeal membrane oxygenation mortality prediction and severity of illness scores in an international COVID-19 cohort.

BACKGROUND: Veno-venous extracorporeal membrane oxygenation (V-V ECMO) is a lifesaving support modality for severe respiratory failure, but its resource-intensive nature led to significant controversy surrounding its use during the COVID-19 pandemic. We report the performance of several ECMO mortality prediction and severity of illness scores at discriminating survival in a large COVID-19 V-V ECMO cohort. METHODS: We validated ECMOnet, PRESET (PREdiction of Survival on ECMO Therapy-Score), Roch, SOFA (Sequential Organ Failure Assessment), APACHE II (acute physiology and chronic health evaluation), 4C (Coronavirus Clinical Characterisation Consortium), and CURB-65 (Confusion, Urea nitrogen, Respiratory Rate, Blood Pressure, age >65 years) scores on the ISARIC (International Severe Acute Respiratory and emerging Infection Consortium) database. We report discrimination via Area Under the Receiver Operative Curve (AUROC) and Area under the Precision Recall Curve (AURPC) and calibration via Brier score. RESULTS: We included 1147 patients and scores were calculated on patients with sufficient variables. ECMO mortality scores had AUROC (0.58-0.62), AUPRC (0.62-0.74), and Brier score (0.286-0.303). Roch score had the highest accuracy (AUROC 0.62), precision (AUPRC 0.74) yet worst calibration (Brier score of 0.3) despite being calculated on the fewest patients (144). Severity of illness scores had AUROC (0.52-0.57), AURPC (0.59-0.64), and Brier Score (0.265-0.471). APACHE II had the highest accuracy (AUROC 0.58), precision (AUPRC 0.64), and best calibration (Brier score 0.26). CONCLUSION: Within a large international multicenter COVID-19 cohort, the evaluated ECMO mortality prediction and severity of illness scores demonstrated inconsistent discrimination and calibration highlighting the need for better clinically applicable decision support tools.

A model to predict the risk of mortality in severely ill COVID-19 patients.

BACKGROUND: To investigate and select the useful prognostic parameters to develop and validate a model to predict the mortality risk for severely and critically ill patients with the coronavirus disease 2019 (COVID-19). METHODS: We established a retrospective cohort of patients with laboratory-confirmed COVID-19 (≥18 years old) from two tertiary hospitals: the People's Hospital of Wuhan University and Leishenshan Hospital between February 16, 2020, and April 14, 2020. The diagnosis of the cases was confirmed according to the WHO interim guidance. The data of consecutive severely and critically ill patients with COVID-19 admitted to these hospitals were analyzed. A total of 566 patients from the People's Hospital of Wuhan University were included in the training cohort and 436 patients from Leishenshan Hospital were included in the validation cohort. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select the variables and build the mortality risk prediction model. RESULTS: The prediction model was presented as a nomograph and developed based on identified predictors, including age, chronic lung disease, C-reactive protein (CRP), D-dimer levels, neutrophil-to-lymphocyte ratio (NLR), creatinine, and total bilirubin. In the training cohort, the model displayed good discrimination with an AUC of 0.912 [95% confidence interval (CI): 0.884-0.940] and good calibration (intercept = 0; slope = 1). In the validation cohort, the model had an AUC of 0.922 [95% confidence interval (CI): 0.891-0.953] and a good calibration (intercept = 0.056; slope = 1.161). The decision curve analysis (DCA) demonstrated that the nomogram was clinically useful. CONCLUSION: A risk score for severely and critically ill COVID-19 patients' mortality was developed and externally validated. This model can help clinicians to identify individual patients at a high mortality risk.

Multi-horizon predictive models for guiding extracorporeal resource allocation in critically ill COVID-19 patients.

OBJECTIVE: Extracorporeal membrane oxygenation (ECMO) resource allocation tools are currently lacking. We developed machine learning (ML) models for predicting COVID-19 patients at risk of receiving ECMO to guide patient triage and resource allocation. MATERIAL AND METHODS: We included COVID-19 patients admitted to intensive care units for >24 h from March 2020 to October 2021, divided into training and testing development and testing-only holdout cohorts. We developed ECMO deployment timely prediction model ForecastECMO using Gradient Boosting Tree (GBT), with pre-ECMO prediction horizons from 0 to 48 h, compared to PaO2/FiO2 ratio, Sequential Organ Failure Assessment score, PREdiction of Survival on ECMO Therapy score, logistic regression, and 30 pre-selected clinical variables GBT Clinical GBT models, with area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics. RESULTS: ECMO prevalence was 2.89% and 1.73% in development and holdout cohorts. ForecastECMO had the best performance in both cohorts. At the 18-h prediction horizon, a potentially clinically actionable pre-ECMO window, ForecastECMO, had the highest AUROC (0.94 and 0.95) and AUPRC (0.54 and 0.37) in development and holdout cohorts in identifying ECMO patients without data 18 h prior to ECMO. DISCUSSION AND CONCLUSIONS: We developed a multi-horizon model, ForecastECMO, with high performance in identifying patients receiving ECMO at various prediction horizons. This model has potential to be used as early alert tool to guide ECMO resource allocation for COVID-19 patients. Future prospective multicenter validation would provide evidence for generalizability and real-world application of such models to improve patient outcomes.

Transcriptome analysis of cepharanthine against a SARS-CoV-2-related coronavirus.

Antiviral therapies targeting the pandemic coronavirus disease 2019 (COVID-19) are urgently required. We studied an already-approved botanical drug cepharanthine (CEP) in a cell culture model of GX_P2V, a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related virus. RNA-sequencing results showed the virus perturbed the expression of multiple genes including those associated with cellular stress responses such as endoplasmic reticulum (ER) stress and heat shock factor 1 (HSF1)-mediated heat shock response, of which heat shock response-related genes and pathways were at the core. CEP was potent to reverse most dysregulated genes and pathways in infected cells including ER stress/unfolded protein response and HSF1-mediated heat shock response. Additionally, single-cell transcriptomes also confirmed that genes of cellular stress responses and autophagy pathways were enriched in several peripheral blood mononuclear cells populations from COVID-19 patients. In summary, this study uncovered the transcriptome of a SARS-CoV-2-related coronavirus infection model and anti-viral activities of CEP, providing evidence for CEP as a promising therapeutic option for SARS-CoV-2 infection.

Serum Uric Acid Concentrations and Risk of Adverse Outcomes in Patients With COVID-19.

Background: Although hyperuricemia frequently associates with respiratory diseases, patients with severe coronavirus disease 2019 (COVID-19) and severe acute respiratory syndrome (SARS) can show marked hypouricemia. Previous studies on the association of serum uric acid with risk of adverse outcomes related to COVID-19 have produced contradictory results. The precise relationship between admission serum uric acid and adverse outcomes in hospitalized patients is unknown. Methods: Data of patients affected by laboratory-confirmed COVID-19 and admitted to Leishenshan Hospital were retrospectively analyzed. The primary outcome was composite and comprised events, such as intensive care unit (ICU) admission, mechanical ventilation, or mortality. Logistic regression analysis was performed to explore the association between serum concentrations of uric acid and the composite outcome, as well as each of its components. To determine the association between serum uric acid and in-hospital adverse outcomes, serum uric acid was also categorized by restricted cubic spline, and the 95% confidence interval (CI) was used to estimate odds ratios (OR). Results: The study cohort included 1854 patients (mean age, 58 years; 52% women). The overall mean ± SD of serum levels of uric acid was 308 ± 96 µmol/L. Among them, 95 patients were admitted to ICU, 75 patients received mechanical ventilation, and 38 died. In total, 114 patients reached composite end-points (have either ICU admission, mechanical ventilation or death) during hospitalization. Compared with a reference group with estimated baseline serum uric acid of 279-422 µmol/L, serum uric acid values ≥ 423 µmol/L were associated with an increased risk of composite outcome (OR, 2.60; 95% CI, 1.07- 6.29) and mechanical ventilation (OR, 3.01; 95% CI, 1.06- 8.51). Serum uric acid ≤ 278 µmol/L was associated with an increased risk of the composite outcome (OR, 2.07; 95% CI, 1.18- 3.65), ICU admission (OR, 2.18; 95% CI, 1.17- 4.05]), and mechanical ventilation (OR, 2.13; 95% CI, 1.06- 4.28), as assessed by multivariate analysis. Conclusions: This study shows that the association between admission serum uric acid and composite outcome of COVID-19 patients was U-shaped. In particular, we found that compared with baseline serum uric acid levels of 279-422 µmol/L, values ≥ 423 µmol/L were associated with an increased risk of composite outcome and mechanical ventilation, whereas levels ≤ 278 µmol/L associated with increased risk of composite outcome, ICU admission and mechanical ventilation.