Coronavirus Disease 2019: A Pandemic Spawning an Infodemic*
Notice bibliographique
Résumé
The coronavirus disease 2019 (COVID-19) pandemic continues to have a devastating impact on all aspects of life, worldwide. It has been challenging. Professionally, all aspects of medical care and research have been altered. At the time of writing this “Special Report,” 141 million individuals have endured severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection with over 3.01 million succumbing to this tragedy. Children are largely spared from death due to infection, with a mortality less than 400 cases in the United States and Canada of over a million cases. However, we now have a different concern. There is a novel postinfectious phenotype described in children that has now been in reported in over 3,000 cases in the United States and likely has an impact on children worldwide. This multisystem inflammatory syndrome in children (MIS-C) (1) is not described in other viral infections, and we need more data to accurately assess global epidemiologic impact and burden on children worldwide. In this “Special Report,” we highlight what we consider as key issues for Pediatric Critical Care Medicine as it responds to reports about COVID-19. MOVING FORWARD WITH “NEW INFORMATION” From the beginning, this pandemic was shrouded in many unknowns, and pediatric practitioners have been frantically trying to disseminate their experience in order to understand the epidemiology, pathogenesis, clinical features, and outcomes. This has resulted in almost 10,000 manuscripts about COVID-19 and children in about 15 months (2). Despite this tsunami of reports, defining the clinical characteristics or outcomes of children presenting to hospitalized medical attention with acute SARS-CoV-2 infection or MIS-C has been challenging and restricted to small cases series or observational studies. In this issue of Pediatric Critical Care Medicine, Tripathi et al (3) report clinical data from the Society of Critical Care Medicine (SCCM) Discovery Network registry on 394 patients from 38 PICUs in the United States. The authors delineate subjects with MIS-C versus acute infection within target organs, and they compare clinical presenting epidemiology, symptomology, management, and outcomes between these two groups. The report by Tripathi et al (3) presented data on 394 subjects, including 178 original patients and 216 patients that overlap with other previously reported studies (4–6). Although the pooled analyses are descriptive in delineating differences between acute and post infectious diseases will be useful to readers, readers should be aware of the overlap of children included in all of these studies (3–6) and likely in more studies to come from this and others’ work. That said, in the present report, Tripathi et al (3) have followed the now accepted convention of transparent reporting (see the Supplemental Files in reference). The reasoning here is to ensure that readers are aware of any potential “double counting” in future meta-analyses of disease and outcomes, as well as the limitations and possible harms to comparing publications describing overlapping patients (7). MOVING FORWARD FROM PUBLISHED “RESEARCH” Given the above cautionary point about overlapping case series, the current “cohort” of COVID-19 children does show differences between acute and postinfectious COVID-19 cases. Such knowledge may be useful in guiding clinical care as well as informing the feasibility and design of studies, including randomized controlled trials (RCTs) of therapeutic interventions. RCTs are definitely needed now because our management of acute COVID-19 and MIS-C has been governed by a combination of expert opinion and a common sense of physiologic rationale based on presumed mechanistic or causal pathways of disease. In this context, the “SCCM Discovery Network” report (3) highlights two issues that will be important as the field moves forward in developing RCTs. First, multicenter collaboration across multiple professional groups is needed (8). This requirement goes beyond the usual groups of medical and nursing specialists involved in adult and pediatric critical care, to now include medical subspecialists in rheumatology, cardiology, infectious disease, hematology, etc. Second, we have to concede that all research in this field—even RCTs—will be carried out by practitioners primarily working in an overburdened clinical environment. There has been rapid dissemination of knowledge and sharing of research information globally such that vaccines have been produced and deployed in record time, and therapies are being evaluated using innovative trial design and platforms. Overall, these are tremendous for the field of medicine, but separating what is important from the trivial is posing a dilemma for journals and their editorial staff. It is time to pay heed to the words of Altman (9)—of the “Bland Altman plot”—who stated almost 3 decades ago that “we need less research, better research and research done for the right reasons.” One year later, Kramer (10) admonished us to think that research should done with the ultimate benefit of improving health. Although we should all strive for improvement in care, we also need to appreciate the need to understand the epidemiology, pathophysiology, treatments, and outcomes in COVID-19. Hence, in 2020, “…The existence of a pandemic does not lower the bar for standards of evidence” but rather raises the bar to protect the safety of those that we endeavor to heal (11). MOVING FORWARD WITH “NEW ARTICLES” There is now a continuous stream of submissions about COVID-19, most Journals and readers will need to be aware of how manuscripts are processed in general (7), as well as how they are considered and reviewed pre publication. This process is now even more important because manuscripts about COVID-19 are available in the public domain before peer-review or in a rapid peer-reviewed format in order “get information out first” (12,13). Although this frenzy to get information out may have served us well early in the pandemic, presently the scientific and medical communities have been faced with an “infodemic” in which hundreds of articles are published every day with the labels of “COVID-19” or “SARS-CoV-2” or “MIS-C” without the ability for the practitioners to best understand how these data can be converted or translated into frontline clinical care. Expert opinions based on highly limited or inadequate information have been used to guide best practices in the face of uncertainty, likely out of a need to rapidly present solutions in a pandemic. Some of these expert position statements or recommendations have led to failed therapies, but they may also have been harmful (e.g., hydroxychloroquine, azithromycin, and many others) (13). However, unlike in adult medicine with millions of deaths, the sparing of children should give us the opportunity to be more cautious in our approach to gain insight and return to the standard operating procedures of the scientific method, before embarking on biologic-modifying medications or other therapies are attempted. Hence, beyond the usual structure required in a clinical research article (14), there are a number of questions that authors should consider as they embark on writing-up a manuscript about COVID-19 (see Box): What is different in this (potential) manuscript? What is novel? Does the information build on previous data? How can these data inform practice? Are patients treated in analyses as a function of clinical phenotypes or are they all lumped together in a single heterogeneous group that makes analyses difficult to understand by readers? Are comorbidities considered in the reporting of data, including malnutrition, coinfection, diabetes mellitus, hypertension, obesity? In such cases, one might consider that analyses include a robust evaluation of each treatment/intervention against laboratory and clinical findings rather than just descriptive reporting. In addition, robust and rigorous multivariable analyses and predictive modeling studies should provide the best approach to perform these analyses, following current recommendations (14). Do the analyses take account of different waves of COVID-19 outbreaks, stay at home orders, no school, or entry of vaccine into a community? MOVING FORWARD WITH “NEW PROJECTS AND COLLABORATIVES” As practitioners, we are now faced with attempting to understand the nuances of whether a new syndrome such as MIS-C is a separate entity in the taxonomy of disease, or is it something on the spectrum of pathophysiology seen in the evolution of sepsis, hemophagocytic lymphohistiocytosis, and macrophage activation syndrome? Now, a year since the first presentation of MIS-C, it is time to move away from a focus on case series or observational descriptive studies. In order to get clinical answers to our questions, we need research efforts from large collaboratives and consortiums rather than single centers presenting underpowered studies. As we see it, there are two—but not mutually exclusive either in importance or activity—streams of research work that our field so desperately needs. One, a deeper understanding of the “why” and “how” children with SARS-CoV-2 infection develop MIS-C, with basic mechanistic and biological translational evaluations (15). The scientific world is blowing-up a storm of potential new knowledge and innovative disease evaluations of COVID-19, but studies must be carefully considered in light of what is known, what we do not know, and how we can potentially bridge these gaps with the highest caliber of scientific evaluation (16). Two, movement to interventional RCTs or novel diagnostic studies that include relevant groups for comparison: ICU versus non-ICU cases and COVID-19 ICU versus non–COVID-19 ICU cases. Of note, it would help if all such studies—whether prospective or phased or historical controls—used the same validated measures of ICU severity-of-illness such as the Pediatric Logistic Organ Dysfunction, Pediatric Risk of Mortality, Pediatric Index of Mortality, and Sequential Organ Failure Assessment scores. In this way, the work can allow for potential pooling of data between centers. Taking all of the above together, it is time for our field to “move forward.” Over a short period, some of the best science and resources have been applied to children with COVID-19 or MIS-C (17,18). Clinicians and scientists in our field now have a different objective and that is to translate what we learn and know into the highest level of care for our patients. Our field and patients expect it.
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 | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,007 |
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 ».