Incidence, trends, characteristics, and outcomes in myocardial infarction with nonobstructive coronary artery disease (MINOCA) in Manitoba
Notice bibliographique
Résumé
Abstract Introduction Myocardial infarction with nonobstructive coronary arteries (MINOCA) is a clinical condition characterized by acute myocardial infarction (MI) in the presence of normal or minimally obstructed (≤50% stenosis) coronary arteries. MINOCA accounts for 5-15% of acute coronary syndrome (ACS) presentations; however, it remains underdiagnosed. The condition has diverse etiologies, including coronary abnormalities such as spontaneous coronary artery dissection (SCAD) and noncoronary causes such as Takotsubo cardiomyopathy and myocarditis. A comprehensive diagnostic workup is essential to identify the underlying mechanism, as management strategies depend on the specific cause. Although coronary angiography is routinely performed, additional diagnostic tools such as intravascular imaging and cardiac magnetic resonance imaging (MRI) are underutilized. Accurate diagnosis is critical, given the variability in long-term prognosis based on etiology. Purpose The purpose of our study was to describe the demographic and clinical characteristics of MINOCA patients in Manitoba, evaluate common diagnostic methods, and assess in-hospital and one-year outcomes in these patients. Methods This retrospective cohort study included all ACS patients over 18 years old who underwent coronary angiography without stenting between January 2019-December 2020. The cardiac catheterization laboratory electronic system was used to generate a list of all the patients diagnosed with MINOCA. A chart review was performed to obtain all relevant data. Data was analyzed using Excel. Results A total of 511 patients were included. The median age was 63 years (54-74), with the majority being female (61%). The most common cardiovascular risk factors were hypertension (52%), previous/current smoking (33%), dyslipidemia (30%), and diabetes mellitus (21%). Chest pain was the leading presenting symptom (83%), followed by dyspnea (23%) and nausea/emesis (11%). Only 4% of patients had intravascular imaging performed and 10% had cardiac MRI. For patients that had additional diagnostic work up, an underlying cause for MINOCA was identified in over half of the patients (58% of patients had cardiac MRI and 53% had intravascular imaging). Over two-thirds of MINOCA cases (70%) did not have an identifiable etiology, of which only 8% of them had additional testing (cardiac MRI and/or intravascular imaging). One-year mortality was 9%, with other short-term complications being rare. Conclusions Intravascular imaging and cardiac MRI are underutilized in the diagnosis of MINOCA patients. Among the few patients who underwent further investigations, an underlying etiology was identified in over half of the cases. MINOCA patients are still at risk of adverse cardiovascular outcomes despite it being an uncommon cause of MI. Our results will help facilitate quality improvement initiatives to increase local awareness about the diagnostic workup and treatment modalities for MINOCA patients.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».