A 9-mRNA signature measured from whole blood by a prototype PCR panel predicts 28-day mortality upon admission of critically ill COVID-19 patients.

Immune responses affiliated with COVID-19 severity have been characterized and associated with deleterious outcomes. These approaches were mainly based on research tools not usable in routine clinical practice at the bedside. We observed that a multiplex transcriptomic panel prototype termed Immune Profiling Panel (IPP) could capture the dysregulation of immune responses of ICU COVID-19 patients at admission. Nine transcripts were associated with mortality in univariate analysis and this 9-mRNA signature remained significantly associated with mortality in a multivariate analysis that included age, SOFA and Charlson scores. Using a machine learning model with these 9 mRNA, we could predict the 28-day survival status with an Area Under the Receiver Operating Curve (AUROC) of 0.764. Interestingly, adding patients' age to the model resulted in increased performance to predict the 28-day mortality (AUROC reaching 0.839). This prototype IPP demonstrated that such a tool, upon clinical/analytical validation and clearance by regulatory agencies could be used in clinical routine settings to quickly identify patients with higher risk of death requiring thus early aggressive intensive care.

Development and Validation of a Nomogram for Predicting 28-Day Mortality on Admission in Elderly Patients with Severe Community-Acquired Pneumonia.

Introduction: There were few studies on the mortality of severe community-acquired pneumonia (SCAP) in elderly people. Early prediction of 28-day mortality of hospitalized patients will help in the clinical management of elderly patients (age ≥65 years) with SCAP, but a prediction model that is reliable and valid is still lacking. Methods: The 292 elderly patients with SCAP met the criteria defined by the American Thoracic Society from 33 hospitals in China. Clinical parameters were analyzed by the use of univariable and multivariable logistic regression analysis. A nomogram to predict the 28-day mortality in elderly patients with SCAP was constructed and evaluated using the area under the receiver operating characteristic curve (AUC) and internally verified using the Bootstrap method. Results: A total of 292 elderly patients (227 surviving and 65 died within 28 days) were included in the analysis. Age, Glasgow score, blood platelet, and blood urea nitrogen values were found to be significantly associated with 28-day mortality in elderly patients with SCAP. The AUC of the nomogram was 0.713 and the calibration curve for 28-day mortality also showed high coherence between the predicted and actual probability of mortality. Conclusion: This study provides a nomogram containing age, Glasgow score, blood platelet, and blood urea nitrogen values that can be conveniently used to predict 28-day mortality in elderly patients with SCAP. This model has the potential to assist clinicians in evaluating prognosis of patients with SCAP.