Derivation and validation of clinical phenotypes and associated treatment responses in critically ill COVID-19 patients

Introduction: Due to variability in the host response, a uniform treatment strategy for severe COVID-19 may be inadequate. We applied unsupervised clustering methods to large cohorts of COVID-19 ICU patients to derive and validate clinical phenotypes, and to explore treatment responses in these phenotypes. Method(s): Phenotypes were derived in 13.279 critically ill COVID-19 patients admitted to 82 Dutch ICUs from September 2020 to February 2022. Twenty-one features were selected from clinical characteristics measured within 24 h after ICU admission. Phenotypes were assigned using consensus k means clustering. External validation was performed in 6225 critically ill COVID-19 patients admitted to 55 Spanish ICUs from February 2020 to December 2021. Individual patient data on corticosteroids therapy enabled us to investigate phenotype-specific responses in this cohort. Result(s): Three distinct clinical phenotypes were derived (Fig. 1A). Patients with phenotype 1 (43%) were younger, had lower APACHE IV scores, higher BMI as well as a lower P/F ratio and 90-day in-hospital mortality (18%, Fig. 1A). Phenotype 2 patients (37%) were older and had slightly higher APACHE IV scores compared with phenotype 1, a lower BMI, and higher mortality compared to phenotype 1 (24%, p = 2.95e-07). Phenotype 3 (20%) included the oldest patients with the most comorbidities and highest APACHE IV scores, severe renal and metabolic impairment, and the worst outcome (47% mortality, p = 6.6e-16 and p = 6.6e-16 versus phenotypes 1 and 2, respectively). Phenotype distribution and outcome were very similar in the validation cohort (Fig. 1B). This cohort also revealed that corticosteroid therapy only benefited phenotype 3 (65% vs. 54% mortality, p = 2.5e-03, Fig. 1C). Conclusion(s): COVID-19 ICU phenotypes based on clinical data are related to outcome and treatment responses. This can inform treatment decisions as well as randomized trials employing precision medicine approaches.

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.

Identification of highly immunogenic epitopes in the SARS-CoV-2 Spike protein to produce monoclonal antibodies

Background: In outpatients, monoclonal antibodies to Spike protein reduce viral load and improve outcomes, with a greater effect in serum antibody-negative at baseline. The aim of this study was to find epitope candidates to produce neutralizing monoclonal antibodies (mAb) for COVID-19 treatment. Method(s): IgG COVID-19 patients (N=500) against SARS-CoV-2 was confirmed. Epitope mapping was performed by Luminex technology. A computational pipeline based in predictive models was designed to predict S protein epitopes most likely to be recognized by mAb from COVID-19 convalescent patients. Result(s): Validation Screening: 29 epitopes of the SARS-CoV-2 S protein were predicted by our pipeline and included in the Luminex panel. 40 serum samples from convalescent COVID-19 patients and 126 pre-pandemia negative controls were included in the validation screening. Epitope mapping: 500 serum samples were tested against the 8 epitopes selected in the validation screening. The two epitopes with the highest IgG of participants above the seropositivity cut-offs were selected. The two most immunogenic epitopes were screened in phage library containing 109 clones of antibodies anti-SARS-CoV-2 to produce mAbs by phage display technology. Conclusion(s): The two epitopes with the highest IgG reactivity validated against serum samples from 500 COVID-19 convalescent patients and phage library are good candidates for the production of new neutralizing mAbs against SARS-CoV-2 S protein.

Risk factors for mortality in COPD patients with COVID19 admitted to Intensive Care Unit

Background and aim: Patients with Chronic obstructive pulmonary disease (COPD) and COVID-19 have increased risk of hospitalization and Instensive Care Unit (ICU) admission (1). Aims and objectives: To indentify risk factors for intrahospital mortality in COPD patients with COVID-19 admitted to Spanish ICUs. Method(s): Demographic and clinical data during ICU admission were recorded using REDCap on all patients hospitalized for COVID-19 in 70 Spanish ICUs (2). We described the baseline clinical characteristics of COPD compared to other chronic respiratory disease (CRD) and to the overall population. We identified the risk factors for intrahospital mortality of COPD patients receiving invasive mechanical ventilation (IMV) and for those COPD receiving noninvasive respiratory support (NIRS). Result(s): Two hundred and sixty-eight ICU patients (5%) had COPD out of 5196 included. No differences were found between COPD, CRD or the overall population in the rates of IMV (76-78%) vs NIRS (22-24%). COPD intrahospital mortality was much higher in the IMV subgroup (58%). Independent risk factors for intrahospital mortality in the COPD+IMV or COPD+NIRS were: age and chronic Kidney disease or hypertension, respectively. Previous NIRS in COPD+IMV group was protective for intrahospital mortality (Figure). Conclusion(s): New strategies are needed to reduce the high intrahospital and 90-days mortality of COPD COVID-19 patients admitted to ICU.

Risk factors associated with mortality among elderly patients with COVID-19: Data from 55 intensive care units in Spain.

INTRODUCTION AND OBJECTIVES: Critically-ill elderly ICU patients with COVID-19 have poor outcomes. We aimed to compare the rates of in-hospital mortality between non-elderly and elderly critically-ill COVID-19 ventilated patients, as well as to analyze the characteristics, secondary outcomes and independent risk factors associated with in-hospital mortality of elderly ventilated patients. PATIENTS AND METHODS: We conducted a multicentre, observational cohort study including consecutive critically-ill patients admitted to 55 Spanish ICUs due to severe COVID-19 requiring mechanical ventilation (non-invasive respiratory support [NIRS; include non-invasive mechanical ventilation and high-flow nasal cannula] and invasive mechanical ventilation [IMV]) between February 2020 and October 2021. RESULTS: Out of 5,090 critically-ill ventilated patients, 1,525 (27%) were aged ≥70 years (554 [36%] received NIRS and 971 [64%] received IMV. In the elderly group, median age was 74 years (interquartile range 72-77) and 68% were male. Overall in-hospital mortality was 31% (23% in patients <70 years and 50% in those ≥70 years; p<0.001). In-hospital mortality in the group ≥70 years significantly varied according to the modality of ventilation (40% in NIRS vs. 55% in IMV group; p<0.001). Factors independently associated with in-hospital mortality in elderly ventilated patients were age (sHR 1.07 [95%CI 1.05-1.10], p<0.001); previous admission within the last 30 days (sHR 1.40 [95%CI 1.04-1.89], p = 0.027); chronic heart disease (sHR 1.21 [95%CI 1.01-1.44], p = 0.041); chronic renal failure (sHR 1.43 [95%CI 1.12- 1.82], p = 0.005); platelet count (sHR 0.98 [95% CI 0.98-0.99], p<0.001); IMV at ICU admission (sHR 1.41 [95% CI 1.16- 1.73], p<0.001); and systemic steroids (sHR 0.61 [95%CI 0.48- 0.77], p<0.001). CONCLUSIONS: Amongst critically-ill COVID-19 ventilated patients, those aged ≥70 years presented significantly higher rates of in-hospital mortality than younger patients. Increasing age, previous admission within the last 30 days, chronic heart disease, chronic renal failure, platelet count, IMV at ICU admission and systemic steroids (protective) all comprised independent factors for in-hospital mortality in elderly patients.