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Enregistrement W4234691867 · doi:10.1213/01.ane.0000227133.67685.8b

Hypoglycemia and Cardiac Arrest in a Critically Ill Patient on Strict Glycemic Control

2006· article· en· W4234691867 sur OpenAlexaboutno aff
Anuj Bhatia, Brit Cadman, Iain Mackenzie

Notice bibliographique

RevueAnesthesia & Analgesia · 2006
Typearticle
Langueen
DomaineMedicine
ThématiqueHyperglycemia and glycemic control in critically ill and hospitalized patients
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineGlycemicRegimenHypoglycemiaIntensive care medicineInclusion and exclusion criteriaIntensive careIntensive care unitPopulationAssertionObservational studyIncidence (geometry)Critically illPediatricsInternal medicinePathologyAlternative medicine

Résumé

récupéré en direct d'OpenAlex

In Response: Predefined inclusion and exclusion criteria characterize a study population. If renal failure, however defined, had been an a priori exclusion criterion, then Dr. Al-Ansari's assertion that this patient should have been excluded would have been quite correct (1). Renal failure was not an exclusion criterion in the study we conducted (2). On the other hand, if Dr. Al-Ansari is suggesting that renal failure should have been one of our study's exclusion criteria, we would have to ask why. When we designed the study, there was no reason to believe that tight glycemic control might be either less effective or possibly harmful in patients with renal dysfunction. On the contrary, data available at the time suggested that tight glycemic control reduced the incidence of renal failure (3). These data were subsequently corroborated by others (4,5). Moreover, renal dysfunction on admission to intensive care has not been an exclusion criterion in either of the two randomized studies published to date (3,5), and it is not an exclusion criterion in the continuing Australasian/Canadian study (6). The patient we described in our case report weighed 57.3 kg, and she was being fed according to the usual practice in our intensive care unit. This feeding regimen was a deliberate feature of the study because we had concerns, as others have more recently echoed (7), that van den Berghe et al.'s original study may have demonstrated increased mortality in patients rendered hyperglycemic by an aggressive feeding regimen. Dr. Al-Ansari's assertion that a lower caloric intake is itself a risk factor for hypoglycemia is not supported by a recent, nested, case-control study (8). Ideally, a study should be performed, which prospectively randomized patients into two groups, both receiving tight glycemic control using the same algorithm but different target caloric intakes. We agree that the algorithm being used at the time was not ideal, and indeed our current algorithm considers the lessons that we have learned, including more frequent blood glucose measurements during periods of hypoglycemia. However, the inadequacy of the algorithm has no bearing on the phenomenon we describe or its interpretation. Dr. Al-Ansari's question regarding the frequency of blood gas analysis is difficult to interpret, and its relevance to the subject of our case report is unclear. Our nursing staff is encouraged to perform blood gas analyses when they feel it is clinically indicated. Our blood gas analyzer provides a highly reliable and accurate measure of blood potassium and glucose concentrations, as well as the usual indices of gas exchange. Given that this patient had respiratory and renal failure, was receiving tight glycemic control, and had unstable blood glucose concentrations, the frequency of blood gas analysis reported does not seem unreasonable. Finally, we are delighted that Dr. Al-Ansari gave us the opportunity to highlight a feature of our case report (2) that corroborates our interpretation of events. Figure 2 in our case report shows a striking similarity in the concentration/time profiles for glucose (middle panel) and potassium (top panel), most particularly because of the peaks recorded at 04:30 h and 06:00 h. This similarity reveals that the phenomenon of glucose-associated hyperkalemia was not only reproducible, but it also suggests a dose-response relationship, both peaks having occurred immediately after the administration of different volumes of 50% dextrose, the first of 25 mL and the second of 50 mL. Anuj Bhatia, MD Brit Cadman, MD Iain Mackenzie, MD Department of Anaesthesia and John V. Farman Intensive Care Unit Department of Pharmacy Addenbrooke's Hospital Cambridge, United Kingdom [email protected]

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,010
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Étude de cas · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,012

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,010
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,006
Tête enseignante GPT0,230
Écart entre enseignants0,224 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeÉtude de cas
Domainenon disponible
GenreEmpirique

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

En bref

Citations5
Publié2006
Routes d'admission1
Résumé présentoui

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