301. Detection of Pneumococcal Pneumonia During SARS-CoV-2 Infection
Bibliographic record
Abstract
Abstract Background Streptococcus pneumoniae (pneumococcus) is a common colonizer of the upper respiratory tract and can progress to cause invasive and mucosal disease. Additionally, infection with pneumococcus can complicate respiratory viral infections (influenza, respiratory syncytial virus, etc.) by exacerbating the initial disease. Limited data exist describing the potential relationship of SARS-CoV-2 infection with pneumococcus and the role of co-infection in influencing COVID-19 severity. Methods Inpatients and healthcare workers testing positive for SARS-CoV-2 during March-August 2020 were tested for pneumococcus through culture-enrichment of saliva followed by RT-qPCR (to identify carriage) and for inpatients only, serotype-specific urine antigen detection (UAD) assays (to identify pneumococcal pneumonia). A multinomial multivariate regression model was used to examine the relationship between pneumococcal detection and COVID-19 severity. Results Among the 126 subjects who tested positive for SARS-CoV-2, the median age was 62 years; 54.9% of subjects were male; 88.89% were inpatients; 23.5% had an ICU stay; and 13.5% died. Pneumococcus was detected in 17 subjects (13.5%) by any method, including 5 subjects (4.0%) by RT-qPCR and 12 subjects (13.6%) by UAD. Little to no bacterial growth was observed on 21/235 culture plates. Detection by UAD was associated with both moderate and severe COVID-19 disease while RT-qPCR detection in saliva was not associated with severity. None of the 12 individuals who were UAD-positive died. Conclusion Pneumococcal pneumonia (as determined by UAD) continues to occur during the ongoing pandemic and may be associated with more serious COVID-19 outcomes. Detection of pneumococcal carriage may be masked by high levels of antibiotic use. Future studies should better characterize the relationship between pneumococcus and SARS-CoV-2 across all disease severity levels. Disclosures Akiko Iwasaki, PhD, 4Bio (Consultant, Advisor or Review Panel member)Adaptive Biotechnologies (Consultant, Advisor or Review Panel member)Blavatnik (Grant/Research Support)HHMI (Grant/Research Support)Mathers (Grant/Research Support)NIH (Grant/Research Support)Spring Discovery (Grant/Research Support)Spring Discovery (Consultant, Advisor or Review Panel member)Vedanta InProTher (Consultant, Advisor or Review Panel member)Yale School of Medicine (Grant/Research Support) Nathan D. Grubaugh, PhD, Tempus Labs (Consultant) Ronika Alexander-Parrish, RN, MAEd, Pfizer (Employee, Shareholder) Adriano Arguedas, MD, Pfizer (Employee) Bradford D. Gessner, MD, MPH, Pfizer Inc. (Employee) Daniel Weinberger, PhD, Affinivax (Consultant)Merck (Consultant, Grant/Research Support)Pfizer (Consultant, Grant/Research Support) Anne Wyllie, PhD, Global Diagnostic Systems (Consultant)Pfizer (Advisor or Review Panel member, Research Grant or Support)PPS Health (Consultant)Tempus Labs, Inc (Research Grant or Support)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".