Clinical Experience of Patients Referred to a Multidisciplinary Cardio-Oncology Clinic: An Observational Cohort Study
Bibliographic record
Abstract
Introduction: Cardiovascular disease is the 2nd leading cause of long-term morbidity and mortality in cancer survivors. Cardio-oncology clinics (COCS) have emerged to address the issue; however, there is a paucity of data about the demographics and clinical outcomes of patients seen in the COC setting. Methods: Cancer patients referred to The Ottawa Hospital COC were included in this retrospective observational study. Data collected were patient demographics, cancer type and stage, reason for referral, cardiac risk factors, cardiac assessments and treatment, and clinical outcomes. Results:Between 2008 and 2015, 779 patients (516 women, 66%; 263 men, 34%) were referred to the COC. Median age of the patients at cancer diagnosis was 60 years (range: 18–90 years). The most frequent reasons for referral were decreased left ventricular ejection fraction (33%), pre-chemotherapy assessment (14%), and arrhythmia (14%). Treatment with cardiac medication was given in 322 patients (41%), 181 (56%) of whom received more than 2 cardiac medications, with 57 (18%) receiving an angiotensin-converting enzyme inhibitor (ACEi), 46 (14%) receiving an acei and a beta-blocker, and 38 (12%) receiving a beta-blocker. Of 163 breast cancer patients, 129 (79%) were able to complete targeted therapy with COC co-management. Most of the 779 patients (n = 643, 83%) were alive at the time of the last data collection. Conclusions: This cohort study is one of the largest to report characteristics and clinical outcomes of patients referred to a COC. Collaboration between oncologists and cardiologists resulted in completion of cancer therapy in most patients. Ongoing analysis of referral patterns, management plans, and patient outcomes will help to guide the cardiac care of oncology patients, ultimately optimizing cancer and cardiac outcomes alike.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".