Proteomic determinants of disease, mortality and sequelae in COVID-19 patients requiring invasive mechanical ventilation

Introduction: The molecular mechanisms linked to the pathology of severe COVID-19 and its outcomes are poorly described. Aim(s): To analyze the proteomic profile of bronchial aspirates (BAS) samples from critically ill COVID-19 patients in order to identify factors associated with the disease and its prognosis. Method(s): Multicenter study including 74 critically ill non-COVID-19 and COVID-19 patients. BAS was obtained by bronchoaspiration after invasive mechanical ventilation (IMV) initiation. Proximity extension assay (PEA) technology was used for proteomic profiling. Random forest (RF) statistical models were used to predict the variable importance. Result(s): After adjusting for confounding factors, CST5, NADK, SRPK2 and TGF-alpha showed differences between COVID-19 and non-COVID-19 patients. Reduced levels of ENTPD2 and PTN were observed in non-survivors, even after adjustment. AGR2, NQO2, IL-1alpha, OSM and TRAIL, were the top five strongest predictors for ICU mortality and were used to build a prediction model. PTN (HR=4.00) ENTPD2 (HR=2.14) and the prediction model (HR=6.25) were associated with higher risk of death. In survivors, FCRL1, NTF4 and THOP1 correlated with lung function (DLCO levels) 3-months after hospital discharge. Similar findings were observed for Flt3L and THOP1 and radiological features (TSS). The proteins identified are expressed in immune and non-immune lung cells. A poor control of viral infectivity and an inappropriate reparative response seems to be linked to the disease and fatal outcomes, respectively. Conclusion(s): In critically ill COVID-19 patients, specific proteomic profiles are associated with the pathology, mortality and lung sequelae.

Proteomic profiling of severe lung diffusion impairment in the recovery stage of COVID-19-induced ARDS

Background: Around 80% of patients who developed COVID-19-driven ARDS present lung ailment. There is a lack of knowledge of the mechanisms that mediate the pulmonary outcomes. Aim(s): To characterize the factors linked to diffusion impairment in survivors of severe COVID-19. Method(s): Prospective cohort study including 87 COVID-19-induced ARDS survivors. A complete pulmonary evaluation was performed 3 months after hospital discharge. 364 proteins were quantified using the proximity extension assay (PEA). Partial least square-discriminant analysis (PLS-DA) and random forest (RF) were used for multivariable analyses. Result(s): Moderate to severe diffusion impairment (DLCO<60% predicted) was observed in the 30% of the cohort. 15 proteins were differentially detected [false discovery rate (FDR)<0.05] in the univariate analysis. Pleiotrophin showed the highest differences (fold change=2.22 and FDR=0.001). In continuous analysis, proteins were inversely and independently associated with DLCO, and in some cases showed a robust dose-response relationship. PLS-DA and RF identified proteomic profiles related to the severity of diffusion capacity. Clusters identified were enriched in mediators of cell proliferation and differentiation, tissue remodeling, angiogenesis, coagulation, inflammation, immune response and fibrosis. Proteins are expressed in immune and non-immune lung cells. Conclusion(s): In survivors of COVID-19-driven ARDS, lung dysfunction is linked to plasma factors involved in injury and repair mechanisms. The host proteomic profile provides a novel understanding of post-acute sequelae and may be source of therapeutic strategies and biomarkers.

Risk models for predicting in-hospital mortality from COVID-19 pneumonia in the elderly

Objective. To compare the prognostic value of 3 severity scales: the Pneumonia Severity Index (PSI), the CURB-65 pneumonia severity score, and the Severity Community-Acquired Pneumonia (SCAP) score. To build a new predictive model for in-hospital mortality in patients over the age of 75 years admitted with pneumonia due to the coronavirus disease 2019 (COVID-19). Methods. Retrospective study of patients older than 75 years admitted from the emergency department for COVID-19 pneumonia between March 12 and April 27, 2020. We recorded demographic (age, sex, living in a care facility or not), clinical (symptoms, comorbidities, Charlson Comorbidity Index [CCI]), and analytical (serum biochemistry, blood gases, blood count, and coagulation factors) variables. A risk model was constructed, and the ability of the 3 scales to predict all-cause in-hospital mortality was compared. Results. We included 186 patients with a median age of 85 years (interquartile range, 80-89 years);44.1% were men. Mortality was 47.3%. The areas under the receiver operating characteristic curves (AUCs) were as follows for each tool: PSI, 0.74 (95% CI, 0.64-0.82);CURB-65 score, 0.71 (95% CI, 0.62-0.79);and SCAP score, 0.72 (95% CI, 0.63-0.81). Risk factors included in the model were the presence or absence of symptoms (cough, dyspnea), the CCI, and analytical findings (aspartate aminotransferase, potassium, urea, and lactate dehydrogenase. The AUC for the model was 0.81 (95% CI, 0.73-0.88). Conclusions. This study shows that the predictive power of the PSI for mortality is moderate and perceptibly higher than the CURB-65 and SCAP scores. We propose a new predictive model for mortality that offers significantly better performance than any of the 3 scales compared. However, our model must undergo external validation.