Abstract 318: Heart Failure and Osteoporosis- an Evidence Based Approach
Notice bibliographique
Résumé
Background: Heart disease occurs when there is reduced blood flow to the heart. It refers to a range of conditions that affect the heart. These conditions include ischemic heart disease (which can lead to acute myocardial infarction), angina, arrhythmia, atrial fibrillation, and heart failure, among others. Currently, there are 600,000 Canadians living with heart failure (HF) with 50,000 Canadians being diagnosed every year with HF. Community-based heart failure clinics have been proven to reduce hospitalizations, but Alberta has only one. The CHARM (Community Heart Failure Assessment, Rehabilitation and Management) clinic at Advanced Cardiology, Calgary, Alberta is a community based, charity funded clinic providing outpatient care, which is physician-directed but RN managed. We intended to investigate whether there was any relationship between patients with heart failure and their pre-disposition to osteoporosis and vice versa. Methods: We performed an observational case series with a retrospective chart review of 600 CHARM clinic patients who have been diagnosed with Heart Failure till June 2019. The primary endpoint was to look at CHARM clinic's prevalence of HF, patients who ever had Bone Mineral Density (BMD) done and if there was an intertwining relationship by comparing co-existence of heart failure with osteoporosis. Results: •Total # of pts above 65 years of age from a pool of 356 clinic pts = 169•Total # of pts above 65 years of age who had BMD done before June 2019 = 60•Total # of pts above 65 years of age who have Not had BMD done before June 2019 =108•Percentage of patients above 65 years of age who had BMD done before June 2019 = 35%•Percentage of patients above 65 years of age who have not had BMD done before June 2019 = 65%We also compared our finding with studies from Raymond b. Et al and Ezekowitz and it was found that out of 623 patients with Heart failure, 12% had moderate to severe vertebral compression fracture, 55% of those people have multiple fractures. It was seen that CHF patients with osteoporosis were the features that they were mostly female, Caucasian, smoker, obese, hypertensive, COPD, and patients with prevalence of diabetes. Conclusion: It was noticed from our clinic data that 65% of the patients with CHF did not have a BMD done (till June2019) and there by the presence of osteoporosis could have gone undiagnosed. What complicates CHF and osteoporosis is the age factor (mostly elderly), shared risk factors(factors include advanced age, hypovitaminosis D, renal disease and diabetes mellitus), medication use like (loop diuretics) and common pathogenic mechanisms(activation of the renin-angiotensin-aldosterone system) affect both HF and osteoporosis. It is to be noted that CHF is a major risk factor for mortality following fracture as the patient becomes immobile and it is important to carefully assess osteoporosis and take measures to reduce the risk of osteoporotic fractures.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 ».