Stroke in adults with congenital heart disease: Incidence and predictors
Notice bibliographique
Résumé
Background: Stroke is an important cause of morbidity in adults with congenital heart disease (ACHD). However there is a lack of comprehensive data on the incidence and predictors of stroke in ACHD. Objective: To estimate the cumulative risk and incidence of stroke in ACHD, evaluate the role of different lesion categories and determine the most important predictors of stroke and their impact in ACHD. Methods: This retrospective study of 28,465 ACHD Quebec patients aged 18 to 64 years between 1998 and 2010 was based on aggregated province-wide administrative data. Lesions were classified as severe if they had a high probability of being associated with cyanosis or requiring early surgical intervention and as shunt lesions if defects primarily led to a mixture of oxygenated and deoxygenated blood; the remaining were categorized as right- and left-sided according to lateralization. In this dynamic cohort the cumulative incidence of stroke (ischemic and hemorrhagic combined) was adjusted for the competing risk of death and incidence rates were age- and sex-standardized to reference populations. Previously reported stroke-rates for women in the general population of Quebec in 2002 were 11 per 100,000 population for age-group 15-54 and 82 per 100,000 for age-group 55-64; corresponding rates for men were 18 per 100,000 (age-group 15-54) and 142 per 100,000 (age-group 55-64). By means of Cox proportional hazard (CPH) models with age as the time-scale and adjusted for classic cardiovascular risk factors the independent effect of different lesion categories was evaluated. The relevance of potential predictors was assessed in a nested case-control subcohort by a combination of stepwise model selection and Bayesian model averaging. To focus in on the absolute effect of newly diagnosed heart failure a propensity score matched cohort was created. The potential for information and selection bias was addressed by means of sensitivity analysis. Results: For an 18 year old patient the estimated overall cumulative risk of experiencing a stroke up to age 64 was 8.7% (95%-confidence interval (7.8 -9.5%). In males severe lesions accounted for the highest cumulative incidence with 16.2% (95%-CI: 10.3-21.2%), in females the left-sided lesions with 11.9% (95%-CI: 9.0-14.7%). Women had a 27% lower age-standardized stroke rate than men (incidence rate ratio: 0.73 (95%-CI: 0.60-0.88)). For females incidence rates age-standardized to the mid-year 2002 Quebec population were 11 per 100,000 population in age-group 20-54 and 82 per 100,000 in age-group 55-64; in males rates were 18 (age-group 20-54) and 142 per 100,000 (age-group 55-64) respectively. Contrasting severe to shunt lesions the hazard ratio (HR) of stroke was 3.10 (95%-CI: 2.05-4.51) for patients 18 to 44 years of age and 1.29 (0.80-2.01) for the 45 to 64 years old; for left-sided lesions HRs were 2.24 (1.51, 3.30) and 1.29 (0.98-1.69). Heart failure, diabetes, chronic kidney disease and lesion category emerged as the strongest predictors for stroke from Bayesian model averaging. Patients receiving their first diagnosis of heart failure had an absolute stroke risk of 6.7% (95%-CI 4.4-10.2%) over ten years of follow up compared to a risk of 3.1% (95%-CI: 2.0 – 4.9%) in non-heart failure patients (stratified log-rank test: p-value = 0.01); however CPH-analysis showed that the elevated risk was mainly contained in the first two years of follow-up.Conclusion: Stroke is 10 times more common in ACHD-patients than in the general population below age 55 and 2.5-4.5 times more common in patients aged 55 to 64. Severe and left-sided lesions are the lesion categories conveying the highest risk of stroke, in particular at younger age. Heart failure, diabetes and chronic kidney disease are the comorbidities with the strongest predictive ability for stroke. Further research is required to see if early detection and modifications of these risk factors may reduce the stroke rate in the ACHD population.
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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| 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) | 0,001 | 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 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 ».