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Record W3186832153 · doi:10.2196/29638

Early Experience With Neutralizing Monoclonal Antibody Therapy for COVID-19: Retrospective Cohort Survival Analysis and Descriptive Study

2021· article· en· W3186832153 on OpenAlexvenueno aff
Mark Jarrett, Warren Licht, Kevin Bock, Zenobia Brown, Jamie S. Hirsch, Kevin Coppa, Rajdeep Brar, Stephen Bello, Ira S. Nash

Bibliographic record

VenueJMIRx Med · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRetrospective cohort studyHazard ratioProportional hazards modelInternal medicineCohortSurvival analysisEmergency medicineConfidence interval

Abstract

fetched live from OpenAlex

Background Neutralizing monoclonal antibody (MAB) therapies may benefit patients with mild to moderate COVID-19 at high risk for progressing to severe COVID-19 or hospitalization. Studies documenting approaches to deliver MAB infusions and demonstrating their efficacy are lacking. Objective We describe our experience and the outcomes of almost 3000 patients who received MAB infusion therapy at Northwell Health, a large integrated health care system in New York. Methods This is a descriptive study of adult patients who received MAB therapy between November 20, 2020, to January 31, 2021, and a retrospective cohort survival analysis comparing patients who received MAB therapy prior to admission versus those who did not. A multivariable Cox model with inverse probability weighting according to the propensity score including covariates (sociodemographic, comorbidities, and presenting vital signs) was used. The primary outcome was in-hospital mortality; additional evaluations included emergency department use and hospitalization within 28 days of a positive COVID-19 test for patients who received MAB therapy. Results During the study period, 2818 adult patients received MAB infusion. Following therapy and within 28 days of a COVID-19 test, 123 (4.4%) patients presented to the emergency department and were released, and 145 (5.1%) patients were hospitalized. These 145 patients were compared with 200 controls who were eligible for but did not receive MAB therapy and were hospitalized. In the MAB group, 16 (11%) patients met the primary outcome of in-hospital mortality, versus 21 (10.5%) in the control group. In an unadjusted Cox model, the hazard ratio (HR) for time to in-hospital mortality for the MAB group was 1.38 (95% CI 0.696-2.719). Models adjusting for demographics (HR 1.1, 95% CI 0.53-2.23), demographics and Charlson Comorbidity Index (HR 1.22, 95% CI 0.573-2.59), and with inverse probability weighting according to propensity scores (HR 1.19, 95% CI 0.619-2.29) did not demonstrate significance. The hospitalization rate was 4.4% for patients who received MAB therapy within 0 to 4 days, 5% within 5 to 7 days, and 6.1% in ≥8 days of symptom onset (P=.15). Conclusions Establishing the capability to provide neutralizing MAB infusion therapy requires substantial planning and coordination. Although this therapy may be an important treatment option for early mild to moderate COVID-19 in patients who are at high risk, further investigations are needed to define the optimal timing of MAB treatment to reduce hospitalization and mortality.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.065
GPT teacher head0.409
Teacher spread0.344 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2021
Admission routes1
Has abstractyes

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