Severe Hypoglycemia Rates Are Highest among those with Suboptimal Reporting Behaviour—Results from the InHypo-DM Study
Notice bibliographique
Résumé
Many patients fail to report severe hypoglycemia (SH) events to their healthcare provider (HCP). This limits the ability of HCPs to adjust treatment regimens and optimize glycemia. This study aimed to explore the independent association between patients’ disclosure of SH events to their HCP and self-reported rates of SH. Data were obtained from the InHypo-DM population-based study, Canada’s largest hypoglycemia-specific survey. To indicate SH disclosure, patients at risk for hypoglycemia rated how often they reported their SH events (using ADA definitions) to their HCP on a scale from Never to Always. Univariable analyses identified significant clinical or demographic confounders (p≤0.2). Based on the distribution of SH events, zero-inflated negative binomial (ZINB) regression was used to test the relationship between hypoglycemia disclosure and the retrospective incidence of SH events. A total of 552 people with T1DM (17%) or T2DM (83%) completed the InHypo-DM survey. Forty percent of respondents reported at least one or more SH events. The overall incidence rate of SH was 2.45 events/person-year. Thirty-four percent of respondents indicated that they always report SH events. Results of the ZINB model showed that the odds of experiencing any event was not significantly associated with disclosure behaviour (p=0.86). However, the rate of repeated events was almost double (IRR=1.9, 95% CI: 1.2 to 3.0, p=0.004) among individuals who under-reported their SH as compared to those who did not. These results persisted after adjusting for age, medication type, income, most recent HbA1c, and presence of comorbidities. This population-based study revealed a nearly two-fold higher rate of SH among those who under-report to their HCP. Thus, HCPs should be aware that patient reporting may not be a true reflection of SH events and make efforts to elicit accurate information regarding SH events from their patients, especially for those at risk for repeated SH. Disclosure A. Ratzki-Leewing: None. J.E. Black: None. S. Mequanint: None. N.H. Au: None. B.L. Ryan: None. S.M. Reichert: Other Relationship; Self; Novo Nordisk Inc., Sanofi, Abbott, AstraZeneca. Advisory Panel; Self; Servier. Speaker's Bureau; Self; Eli Lilly and Company. Other Relationship; Self; Boehringer Ingelheim Pharmaceuticals, Inc.. Speaker's Bureau; Self; Merck & Co., Inc., Janssen Pharmaceuticals, Inc.. J.B. Brown: None. S. Harris: Advisory Panel; Self; Novo Nordisk A/S, Sanofi, Merck, AstraZeneca, Amgen Inc., Lilly/Boehringer Ingelheim, Abbott, Janssen. Consultant; Self; Novo Nordisk A/S, Sanofi, Merck, AstraZeneca, Lilly/Boehringer Ingelheim, Abbott, Janssen. Research Support; Self; Novo Nordisk A/S, Sanofi, Merck, Abbott, AstraZeneca, Janssen. Other Relationship; Self; CIHR, CDA, The Lawson Foundation.
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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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».