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1.
Sci Rep ; 12(1): 7168, 2022 May 03.
Article in English | MEDLINE | ID: covidwho-1821604

ABSTRACT

As global vaccination campaigns against SARS-CoV-2 proceed, there is particular interest in the longevity of immune protection, especially with regard to increasingly infectious virus variants. Neutralizing antibodies (Nabs) targeting the receptor binding domain (RBD) of SARS-CoV-2 are promising correlates of protective immunity and have been successfully used for prevention and therapy. As SARS-CoV-2 variants of concern (VOCs) are known to affect binding to the ACE2 receptor and by extension neutralizing activity, we developed a bead-based multiplex ACE2-RBD inhibition assay (RBDCoV-ACE2) as a highly scalable, time-, cost-, and material-saving alternative to infectious live-virus neutralization tests. By mimicking the interaction between ACE2 and the RBD, this serological multiplex assay allows the simultaneous analysis of ACE2 binding inhibition to the RBDs of all SARS-CoV-2 VOCs and variants of interest (VOIs) in a single well. Following validation against a classical virus neutralization test and comparison of performance against a commercially available assay, we analyzed 266 serum samples from 168 COVID-19 patients of varying severity. ACE2 binding inhibition was reduced for ten out of eleven variants examined compared to wild-type, especially for those displaying the E484K mutation such as VOCs beta and gamma. ACE2 binding inhibition, while highly individualistic, positively correlated with IgG levels. ACE2 binding inhibition also correlated with disease severity up to WHO grade 7, after which it reduced.


Subject(s)
COVID-19 , SARS-CoV-2 , Angiotensin-Converting Enzyme 2 , Humans , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics
2.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-316141

ABSTRACT

Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: . A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: . 1,039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: . Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration. “ClinicalTrials” (clinicaltrials.gov) under NCT04455451

3.
Crit Care ; 25(1): 295, 2021 Aug 17.
Article in English | MEDLINE | ID: covidwho-1362062

ABSTRACT

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. METHODS: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. RESULTS: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict "survival". Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients' age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. CONCLUSIONS: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration "ClinicalTrials" (clinicaltrials.gov) under NCT04455451.


Subject(s)
COVID-19/epidemiology , Critical Illness/epidemiology , Electronic Health Records/statistics & numerical data , Intensive Care Units , Machine Learning , Adult , Aged , COVID-19/therapy , Cohort Studies , Critical Illness/therapy , Emergency Service, Hospital , Female , Germany , Humans , Male , Middle Aged , Outcome Assessment, Health Care
4.
Nat Commun ; 12(1): 3109, 2021 05 25.
Article in English | MEDLINE | ID: covidwho-1243298

ABSTRACT

SARS-CoV-2 is evolving with mutations in the receptor binding domain (RBD) being of particular concern. It is important to know how much cross-protection is offered between strains following vaccination or infection. Here, we obtain serum and saliva samples from groups of vaccinated (Pfizer BNT-162b2), infected and uninfected individuals and characterize the antibody response to RBD mutant strains. Vaccinated individuals have a robust humoral response after the second dose and have high IgG antibody titers in the saliva. Antibody responses however show considerable differences in binding to RBD mutants of emerging variants of concern and substantial reduction in RBD binding and neutralization is observed against a patient-isolated South African variant. Taken together our data reinforce the importance of the second dose of Pfizer BNT-162b2 to acquire high levels of neutralizing antibodies and high antibody titers in saliva suggest that vaccinated individuals may have reduced transmission potential. Substantially reduced neutralization for the South African variant further highlights the importance of surveillance strategies to detect new variants and targeting these in future vaccines.


Subject(s)
Antibodies, Neutralizing/immunology , Antibodies, Viral/immunology , COVID-19/prevention & control , SARS-CoV-2/immunology , Spike Glycoprotein, Coronavirus/immunology , Vaccination , Adult , Antibodies, Neutralizing/blood , Antibodies, Viral/blood , Antibody Formation , COVID-19/blood , Female , Gene Expression , Humans , Immunoglobulin G/blood , Immunoglobulin G/immunology , Male , Middle Aged , Mutation , Neutralization Tests , Protein Binding , Protein Domains/genetics , Receptors, Coronavirus/metabolism , Recombinant Proteins , SARS-CoV-2/genetics , Saliva/immunology , Saliva/virology
5.
J Intensive Care Med ; 36(6): 681-688, 2021 Jun.
Article in English | MEDLINE | ID: covidwho-1119375

ABSTRACT

BACKGROUND: The COVID-19 pandemic reached Germany in spring 2020. No proven treatment for SARS-CoV-2 was available at that time, especially for severe COVID-19-induced ARDS. We determined whether the infusion of mesenchymal stromal cells (MSCs) would help to improve pulmonary function and overall outcome in patients with severe COVID-19 ARDS. We offered MSC infusion as an extended indication to all critically ill COVID-19 patients with a Horovitz index <100. We treated 5 out of 23 patients with severe COVID-19 ARDS with an infusion of MSCs. One million MSCs/kg body weight was infused over 30 minutes, and the process was repeated in 3 patients twice and in 2 patients 3 times. RESULT: Four out of 5 MSC-treated patients compared to 50% of control patients (9 out of 18) received ECMO support (80%). The MSC group showed a higher Murray score on admission than control patients, reflecting more severe pulmonary compromise (3.5 ± 0.2 versus 2.8 ± 0.3). MSC infusion was safe and well tolerated. The MSC group had a significantly higher Horovitz score on discharge than the control group. Compared to controls, patients with MSC treatment showed a significantly lower Murray score upon discharge than controls. In the MSC group, 4 out of 5 patients (80%) survived to discharge and exhibited good pulmonary function, whereas only 8 out of 18 patients (45%) in the control group survived to discharge. CONCLUSION: MSC infusion is a safe treatment for COVID-19 ARDS that improves pulmonary function and overall outcome in this patient population.


Subject(s)
COVID-19/complications , COVID-19/therapy , Critical Care , Mesenchymal Stem Cell Transplantation , Respiratory Distress Syndrome/therapy , Respiratory Distress Syndrome/virology , Adult , Aged , COVID-19/mortality , Cohort Studies , Female , Germany , Humans , Male , Middle Aged , Respiration, Artificial , Respiratory Distress Syndrome/mortality , Survival Rate , Treatment Outcome
6.
Clin Med Insights Circ Respir Pulm Med ; 15: 1179548421992327, 2021.
Article in English | MEDLINE | ID: covidwho-1067105

ABSTRACT

OBJECTIVE: This systematic-review and meta-analysis aimed to assess the prevalence of cardiovascular comorbidities and complications in ICU-admitted coronavirus disease 2019 (COVID-19) patients. DATA SOURCES: PubMed and Web of Science databases were referenced until November 25, 2020. DATA EXTRACTION: We extracted retrospective and prospective observational studies on critically ill COVID-19 patients admitted to an intensive care unit. Only studies reporting on cardiovascular comorbidities and complications during ICU therapy were included. DATA SYNTHESIS: We calculated the pooled prevalence by a random-effects model and determined heterogeneity by Higgins' I 2 test. RESULTS: Of the 6346 studies retrieved, 29 were included in this review. The most common cardiovascular comorbidity was arterial hypertension (50%; 95% confidence interval [CI], 0.42-058; I 2 = 94.8%, low quality of evidence). Among cardiovascular complications in the ICU, shock (of any course) was most common, being present in 39% of the patients (95% CI, 0.20-0.59; I 2 = 95.6%; 6 studies). Seventy-four percent of patients in the ICU required vasopressors to maintain target blood pressure (95% CI, 0.58-0.88; I 2 = 93.6%; 8 studies), and 30% of patients developed cardiac injury in the ICU (95% CI, 0.19-0.42; I 2 = 91%; 14 studies). Severe heterogeneity existed among the studies. CONCLUSIONS: Cardiovascular complications are common in patients admitted to the intensive care unit for COVID-19. However, the existing evidence is highly heterogeneous in terms of study design and outcome measurements. Thus, prospective, observational studies are needed to determine the impact of cardiovascular complications on patient outcome in critically ill COVID-19 patients.

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