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Record W4400653709 · doi:10.1097/ccm.0000000000006295

The Omicron Paradox: Is It Omicron or Is It What Happened During the Omicron Period?*

2024· editorial· en· W4400653709 on OpenAlexaff
Letícia Kawano-Dourado, Dena Zeraatkar

Bibliographic record

VenueCritical Care Medicine · 2024
Typeeditorial
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineVaccinationPandemicCohortPsychological interventionHealth careIntensive care medicinePopulationDiseaseIntensive care unitCoronavirus disease 2019 (COVID-19)Emergency medicineInternal medicineInfectious disease (medical specialty)ImmunologyPsychiatryEnvironmental health

Abstract

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The emergence of variants of concern (VOCs), each with potential differences in their virulence and ability to evade the immunity system, challenged healthcare providers and systems during the COVID-19 pandemic (1). It is not certain that viral evolution leads to lower severity; therefore, assessing the impact of new and emerging variants on clinical outcomes may be important for proper clinical care optimization and resource allocation (1–3). The risk of death observed with different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) VOCs has varied, reflecting the net result of a complex interplay between intrinsic VOC characteristics, patient’s individual characteristics, healthcare resources, therapeutics, and infection- and vaccine-related immunity (4–6). In this issue of Critical Care Medicine, Santosa et al (7) present survival trends of adult critically ill COVID-19 patients in Sweden during the first two and a half years of the pandemic, from March 2020 to September 2022. The study, utilizing a population-based cohort of Swedish ICU patients with COVID-19 listed as a primary or secondary diagnosis, investigated the impact of VOCs, patient characteristics, comorbidities, and treatment approaches on mortality. Of the 8975 patients included in the study by Santosa et al (7), 32.6% died. Prognostic predictors of death remained constant throughout different VOC periods: older age, impaired immune system-related disease, greater clinical severity at presentation, and restricted treatment strategy (which meant limiting therapeutic interventions given the low probability of hospital discharge). Conversely, the use of steroids during the ICU stay and booster vaccination (third dose) was associated with reduced mortality risk (7). Interestingly, the ICU mortality rate was highest during the Omicron period (65.9 deaths per 1000 admitted patients) while the ICU mortality rate during the Delta period was 26.4 deaths per 1000, and 18.8 per 1000 during the Alpha period (7). This contradicts previous findings that suggest Omicron is more transmissible but associated with a lower risk of adverse health outcomes (4,8,9). As also described by the authors, this contradiction may be understood in light of potential confounding whereby there were differences in patient characteristics or factors related to care between VOC periods that also may have influenced the risk of death (10). For example, patients admitted to the ICU during the Omicron period had worse prognostic factors compared with other VOC periods (older age, higher prevalence of comorbidities, and lower income). Additionally, more than 80% of patients were treated with steroids during the Delta period but less than 50% of patients during the Omicron period. Likewise, more than half of patients were admitted to the ICU from hospital wards during Delta but less than half during Omicron. These patterns, overall, suggest that there are differences in the types of patients compared between the Delta and Omicron periods. While the authors attempted to adjust for these factors, unknown, unmeasured, or poorly measured confounding factors may still influence results—a phenomenon known as residual confounding. Other findings in the study also suggest confounding. For example, the study reports that booster vaccination was associated with reduced risk of death, particularly for those over 70 years old (as compared with the unvaccinated), while one or two doses of vaccine increased the risk of death, which is inconsistent with other data supporting the efficacy of vaccines (11–13). We speculate whether this apparent contradictory association between one or two doses of vaccines and greater risk of death is, in fact, related to baseline patients’ characteristics: patients under greater risk of death were those prioritized initially for vaccination with the scheme of one and two doses (14). Finally, the study did not genetically confirm SARS-CoV-2 VOCs but rather assumed it according to calendar period. Potential misclassification of VOCs may also contribute for the paradoxical findings, especially since the Delta VOC, which is known for being associated with worse health outcomes, immediately precedes Omicron (4,15). These findings, however, also do not rule out other explanations for increased mortality associated with Omicron VOC: while the Omicron VOC is associated with lower risk of severe illness, patients who do experience severe illness may be at higher risk of death (16). Despite limitations, the study by Santosa et al (7) has many strengths. It was a large-scale, nationwide, multilinkage registered-based study involving all critically ill COVID-19 patients admitted to the ICU during the first two and a half years of the COVID pandemic (17). The large number of patients included allowed authors to investigate a broader number of prognostic factors associated with ICU mortality confirming some of the previous prognostic findings in COVID, such as older age, impaired immune system-related disease, greater clinical severity at presentation, and restricted treatment strategy. In summary, we commend the authors’ careful and thorough work. The study by Santosa et al (7) illustrates well the challenges of evaluating the virulence and sequelae of VOCs within the backdrop of evolving patient characteristics and care patterns. The possibility that future VOCs may exhibit varying levels of virulence underscores the need to invest in VOC surveillance systems that can facilitate timely research and enhance preparedness for future outbreaks.

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)0.008
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.006
Scholarly communication0.0040.009
Open science0.0020.003
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0040.001

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 head0.038
GPT teacher head0.398
Teacher spread0.360 · 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 designNot applicable
Domainnot available
GenreEditorial

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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