What is the role of admission <scp>HbA1c</scp> in managing <scp>COVID</scp>‐19 patients?
Notice bibliographique
Résumé
Some recent articles published in the Journal of Diabetes have raised concerns about the importance of checking glycosylated hemoglobin (HbA1c) upon hospital admission for coronavirus disease 2019 (COVID-19) patients.1-3 It is now undoubtedly clear that absolute hyperglycemia at the time of admission increases the risk of severe outcomes of COVID-19, independent of prior diabetes status,4, 5 and tight glycemic control improves the prognosis of these patients significantly.4, 5 However, the role of admission HbA1c, which reflects average glycemia over the preceding 2 to 3 months, in the management of COVID-19 patients remains uncertain. Some studies have shown a significant association between admission HbA1c and disease progression or mortality in COVID-19 patients, whereas a few others did not (Table 1). While the reasons for this discrepancy are not clear, most of these studies are constrained by a small number of patients,6, 7 a large proportion of missing HbA1c data,8 and inadequate adjustment of potential confounders.8, 9 Previous research has shown that background glycemia mediates the association between admission glucose and outcomes in patients with a variety of medical conditions.10-12 Thus, correcting admission glucose levels for background glycemia estimated by HbA1c, the so-called relative hyperglycemia predicts outcomes in acute health conditions better than absolute hyperglycemia,10, 13 albeit not yet proven in the COVID-19 context. Mortality Markers of inflammation and hypercoagulability, and oxygen saturation Higher mortality rate with increasing HbA1c levels: 9.8% in group A (n = 41, no diabetes and HbA1c ≤6.0%), 11.4% in group B (n = 44, no diabetes and HbA1c >6.0- < 6.5%), and 27.7% in group C (n = 47, diabetes and/or HbA1c ≥6.5%), P = .04 Negative correlation between HbA1c and SaO2 (r = −0.22, P = .01). Positive correlation between HbA1c and ferritin (r = 0.24, P = .01), CRP (r = 0.22, P = .01), fibrinogen (r = 0.27, P < .01), and ESR (r = 0.27, P < .01) Invasive mechanical ventilation or death within 7 days of admission Since HbA1c is relatively unaffected by the stress of acute illness,14 it may help identify newly diagnosed diabetes cases in COVID-19 patients. Newly diagnosed diabetes (new onset or previously undiagnosed) is now increasingly recognized as a common phenomenon in COVID-19 patients.15, 16 More importantly, COVID-19 patients with newly diagnosed diabetes appear to be at a greater risk for poor prognosis not only compared with those without diabetes but also individuals with known diabetes.17-19 To summarize, admission blood glucose is certainly a key biomarker to risk stratify and guide the clinical management of COVID-19 patients, with or without known diabetes. Thus, it is essential that all COVID-19 patients be screened for absolute hyperglycemia upon admission so that early and appropriate treatment can be initiated if required. While the role of admission HbA1c as a marker of COVID-19 severity is yet to be fully established, HbA1c assists in identifying patients with newly diagnosed diabetes.15, 20 These patients are a high-risk group and should be closely monitored for the emergence of cardiometabolic disorders in the long term.16 No funding received. None declared.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».