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Enregistrement W3038021344 · doi:10.1159/000508088

Misleading Numbers: Is the Risk of Acute Kidney Injury with COVID-19 Truly This Low?

2020· letter· en· W3038021344 sur OpenAlexafffundabout
Samuel A. Silver, Edward G. Clark, Swapnil Hiremath

Notice bibliographique

RevueAmerican Journal of Nephrology · 2020
Typeletter
Langueen
DomaineMedicine
ThématiqueAcute Kidney Injury Research
Établissements canadiensUniversity of OttawaQueen's University
Organismes subventionnairesUniversity of Ottawa
Mots-clésMedicineAcute kidney injuryPopulationCohortCoronavirus disease 2019 (COVID-19)Intensive care medicineComorbidityKidney diseaseDiseaseCohort studySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Internal medicineEmergency medicinePediatricsInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

Dear Editor,The case series from Wang et al. [1] of 116 patients from Remnin Hospital with coronavirus disease 2019 (COVID-19) reports that none developed acute kidney injury (AKI). This finding and several other aspects of this study cause us concern. In particular, 0% of patients with AKI is strikingly inconsistent with the reported literature so far. In addition, two other case series published as preprints from the same institution reported that 23–32% of deaths were accompanied by AKI [2, 3]. We speculate that this discrepancy may have occurred for several reasons.First, as it is unclear how the authors assembled this cohort, it is not possible to determine which fraction of patients admitted to the institution was included. Was it a specialized COVID-19 ward for a selected patient population? Without more details, it is possible that this case series consists of a highly selected group of patients, and hence is not representative of the general patient population. This is suggested by the small number of patients with any preexisting comorbidities. Although 5 patients had end-stage kidney disease at baseline, none of the others had CKD despite a definition of CKD which was quite broad. Another potential source of selection bias is that it is unclear where patients were captured with respect to the trajectory of their COVID-19 infections. Were they all new admissions? Were some patients already hospitalized at the time of their COVID-19 diagnosis? Were some already recovering? The patients most at risk for AKI would be those captured during the acute phase of their illness. Without having more details about how patients were identified for inclusion, the results are difficult to interpret.Second, no data are provided regarding the number of patients who had a baseline serum Cr available. These definitions result in bidirectional misclassification of AKI incidence, and so clear numbers are needed [4, 5].Third, outcome ascertainment for AKI is unclear. It is unclear how often serum Cr and urine output were measured to ascertain AKI. To some extent, the availability of renal replacement therapy could have affected ascertainment if patients died with (or recovered from) AKI before having had follow-up kidney function testing. Table 2 [1] from the study suggests that testing was done weekly, which may not have been sufficiently frequent in certain instances. Additionally, it is unclear how to interpret what is meant by serum Cr values and kidney deterioration for the 5 patients with end-stage kidney disease who already were on dialysis.Last, given that AKI affects between 10 and 25% of hospitalized patients, we would expect patients with COVID-19 to at least fall somewhere within this range or above it if specifically assessing a critically ill population.For the reasons stated above, we believe that the results reported by Wang et al. [1] should be taken with caution. We suggest that clinicians should continue to closely monitor for AKI in patients with COVID-19 as they would in other hospitalized patients and while we await further evidence of any potentially unique AKI risks in this population.E.G.C. and S.H. would like to acknowledge research salary support from the Department of Medicine, University of Ottawa.The authors have no conflicts of interest to declare.The authors did not receive any funding.The letter was drafted by all three authors.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,004
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,008
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,018
Tête enseignante GPT0,320
Écart entre enseignants0,303 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2020
Routes d'admission3
Résumé présentoui

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