Abstract 318: Heart Failure and Osteoporosis- an Evidence Based Approach
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".