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Record W3135577040 · doi:10.1101/2021.03.08.21253121

Absent or insufficient anti-SARS-CoV-2 S antibodies at ICU admission are associated to higher viral loads in plasma, antigenemia and mortality in COVID-19 patients

2021· preprint· en· W3135577040 on OpenAlexafffund
María Martin‐Vicente, Raquel Almansa, Isidoro Martínez, Ana P. Tedim, Elena Bustamante, Luís Tamayo, César Aldecoa, Gloria Renedo, José Ángel Berezo, Jamil Cedeño, Nuria Mamolar, Pablo García Olivares, Rubén Herrán, Ramón Cicuéndez, Pedro Enríquez, Alicia Ortega, Noelia Jorge, Amanda de la Fuente, Juan Bustamante‐Munguira, María José Muñoz‐Gómez, Milagros González‐Rivera, Carolina Puertas, Vicente Más, Mónica Vázquez, Felipe Pérez‐García, Jesús Rico-Feijoó, Silvia Martín, Ana Motos, Laia Fernández‐Barat, José María Eirós Bouza, Marta Domínguez‐Gil, Ricard Ferrer, Ferrán Barbé, David J. Kelvin, Jesús F. Bermejo-Martín, Salvador Resino, Antoni Torres

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsDalhousie University
FundersInstituto de Salud Carlos IIICanadian Institutes of Health Research
KeywordsViral loadAntibodyMedicineProportional hazards modelSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ImmunologyInternal medicineCoronavirus disease 2019 (COVID-19)GastroenterologyMultivariate analysisVirologyVirus

Abstract

fetched live from OpenAlex

Abstract Purpose to evaluate the association between anti-SARS-CoV-2 S IgM and IgG antibodies with viral RNA load in plasma, the frequency of antigenemia and with the risk of mortality in critically ill patients with COVID-19. Methods anti-SARS-CoV-2 S antibodies levels, viral RNA load and antigenemia were profiled in plasma of 92 adult patients in the first 24 hours following ICU admission. The impact of these variables on 30-day mortality was assessed by using Kaplan-Meier curves and multivariate Cox regression analysis. Results non survivors showed more frequently absence of anti-SARS-CoV-2 S IgG and IgM antibodies than survivors (26.3% vs 5.6% for IgM and 18.4% vs 5.6% for IgG), and a higher frequency of antigenemia (47.4% vs 22.2%) ( p <0.05). Non survivors showed lower concentrations of anti-S IgG and IgM and higher viral RNA loads in plasma, which were associated to increased 30-day mortality and decreased survival mean time. [Adjusted HR (CI95%), p ]: [S IgM (AUC ≥60): 0.48 (0.24; 0.97), 0.040]; [S IgG (AUC ≥237): 0.47 (0.23; 0.97), 0.042]; [Antigenemia (+): 2.45 (1.27; 4.71), 0.007]; [N1 viral load (≥ 2.156 copies/mL): 2.21 (1.11; 4.39),0.024]; [N2 viral load (≥ 3.035 copies/mL): 2.32 (1.16; 4.63), 0.017]. Frequency of antigenemia was >2.5-fold higher in patients with absence of antibodies. Levels of anti-SARS-CoV-2 S antibodies correlated inversely with viral RNA load. Conclusion absence / insufficient levels of anti-SARS-CoV-2 S antibodies following ICU admission is associated to poor viral control, evidenced by increased viral RNA loads in plasma, higher frequency of antigenemia, and also to increased 30-day mortality. Take-home message absent or low levels of antibodies against the S protein of SARS-CoV- 2 at ICU admission is associated to an increased risk of mortality, higher frequency of antigenemia and higher viral RNA loads in plasma. Profiling anti-SARS-CoV-2 s antibodies at ICU admission could help to predict outcome and to better identify those patients potentially deserving replacement treatment with monoclonal or polyclonal antibodies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.080
GPT teacher head0.385
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2021
Admission routes2
Has abstractyes

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