THE STUDY OF CARDIOVASCULAR RISK ATTAINMENT AMONG PATIENTS WITH LUPUS AND RHEUMATOID ARTHRITIS
Notice bibliographique
Résumé
PV220 / #517 Poster Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes Background/Purpose It has been well studied that patients with rheumatologic disease have an increased risk for cardiovascular (CV) events and those who are underserved are at especially high risk. Rheumatoid arthritis (RA and lupus (SLE) are the most common rheumatologic conditions. Therefore, goal of this project is to identify the prevalence of cardiovascular risk factors in patients with SLE or RA within the Yale rheumatology and to evaluate the success of current efforts to minimize CV risk. Methods In this 1 center, cross-sectional study, cardiovascular risk factor data, which include hyperlipidemia (HLD), hypertension (HTN), smoking, obesity, and diabetes (DM), were extracted from Yale health system from 2010-2023 with related ICD codes to investigate the prevalence and target attainment of traditional risk factors in patients with SLE and RA. A survey of knowledge gap of managing CV risk factors in rheumatological diseases was performed in Yale primary care residents who are primary providers serving the underserved patient population including those with rheumatological diseases. Results Cardiovascular disease (CVD) in this study includes coronary artery disease, cerebral vascular disease and peripheral vascular disease. Of total 295 patients with SLE, 138 (46.8%) had CVD and 157 (53.2%) had no CVD. Of those SLE with CVD vs. without, we found HLD (69.9% vs. 47.8%), HTN (76.8% vs.56.1%), smoking (52.9% vs. 41.1%), obesity (64.2% vs. 24.2%) and DM (20% vs. 14.2%). Of total 1680 patients with RA, 607 (36.1%) had CVD and 1073 (63.9%) had no CVD. Of those RA with CVD vs. without, we found HLD (88.8% vs. 56.6%), HTN (82.3% vs.54.9%), smoking (17.1% vs. 11.2%), obesity (51.9% vs. 45.2%) and DM (48.6% vs. 25.1%). 55 patients with SLE and 32 patients with RA were assessed the attainment of traditional risk factors for CVD. We found there were 68.1% with LDL <100, 78.7% with TG <150, 72.7% HTN <130/80, 90.1% non /former smoker, 30.9% of patients with BMI less than 30, 100% with HbA1c <7 in SLE patients, and 59.4% with LDL <100, 78.1% TG, 59.4% HTN <130/80, 75.0% non /former smoker, 21.9% with BMI less than 30, 61.3% with HbA1c <7 in RA patients. Of the total 31 medical residents completed the survey for the knowledge evaluation in CV risk management. Approximately 25.80% acknowledged all rheumatological diseases associated with increased risk for CVD, 58.06% identified appropriate CV risk factors, 25.8% can identify all appropriate orders for patients at risk, and 58.84% expressed lack of experience in working directly with patients with rheumatological conditions. Conclusions This study showed a strong prevalence for traditional risk factors among rheumatic patients with inadequate control. We also found an education gap in medical training regarding CV screening and management in patients with rheumatological diseases, namely SLE and RA in this study. For future studies, further investigation into improving knowledge in medical residency training as well as patient awareness should be investigated.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».