The diagnostic accuracy of experienced physicians to clinically discriminate type 1 myocardial infarction from type 2 myocardial infarction or myocardial injury
Notice bibliographique
Résumé
Abstract Background The early differentiation of patients with type 1 myocardial infarction (T1MI) from type 2 myocardial infarction (T2MI) or myocardial injury is crucial and has immediate clinical implications. It remains uncertain how precise experienced cardiologists (>10years experience) can discern a T1MI from T2MI or myocardial injury by solely integrating clinical information. Methods From a prospective, multicenter cohort study 1636 patients referred for urgent angiography for suspected T1MI, cardiologists were asked to estimate the likelihood of an underlying T1MI prior and following invasive angiography. All cases were independently reviewed and adjudicated for their final diagnosis (T1MI or T2MI/ myocardial injury). Results Finally, 1354 and 282 were adjudicated with T1MI and T2MI/myocardial injury, whereas 173 (17.2%) patients presented with myocardial injury, see Figure below. There was a male predominance, especially among patients with T1MI (1016 (75%), p<0.001). The prevalence of NSTEMI presentation was significantly higher in patients with T2MI/ myocardial injury. The level of hs-TnT at baseline was significantly higher in T1M1 compared to T2MI/ myocardial injury patients (133 (38; 571) ng/L versus 42 (26;92) ng/L and 68 (31; 212) ng/L, respectively; p=0.001). Overall, the number of angiography and PCI-related complications was low, but higher among T1MI compared to T2MI/ myocardial injury patients (0.4% versus 1.8%/ 0.0%, p=0.047, respectively). In terms of in-hospital adverse outcomes, the number of clinically relevant bleedings and revascularization procedures was higher in T1M1 compared to T2MI/ myocardial injury patients (9 (0.6%) versus 2 (1.8%), and 34 (2.5%) versus 3 (2.7%)). After 1-year follow-up (in unadjusted analyzes), the risk for MACE was higher among T1M1 compared to T2MI/ myocardial injury patients (230 (17%) versus 28 (7.3%), p=0.008). This was mainly driven by a higher risk for repeat revascularization procedures and MIs. Of note, there was no significant difference in death. Overall, we found that including the angiographic information had a significant impact on the diagnostic accuracy (p<0.001) (Figure). Prior to the invasive angiogram, the diagnostic accuracy of the cardiologists´ clinical evaluation and submitted likelihood for T1M1 reached an AUC 0.87 (95% confidence interval (95%CI) 0.85–0.90), and after integrating the angiographic information, the AUC increased (0.96 (95%CI 0.94–0.98). Conclusions Patients diagnose with T1M1 compared to T2MI/myocardial injury relevantly differ in their presentation and demographics as well as clinical outcomes. In order to reliably identify patients with T2MI/ myocardial injury and accordingly guide their further management, physicians should aim for delineation of the coronary anatomy. In this context, using invasive angiography with contemporary practice seems safe.Accuracy of clinical judgment for T1M1CONSORT diagram - study cohort
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 | 0,005 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».