The Omicron Paradox: Is It Omicron or Is It What Happened During the Omicron Period?*
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,006 |
| Communication savante | 0,004 | 0,009 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».