Snapshot of cardio-oncology practice in Italy
Bibliographic record
Abstract
Abstract Background Several types of patients should be referred to cardio-oncology services, but some may prevail in clinical practice. Moreover, with the global aging of the population of industrialized countries, the patterns of cardio-oncology care of elderly individuals should be object of particular attention. Purpose and methods We retrospectively collected the data of patients evaluated during the first 2 weeks of December 2024 at 9 centres, directed by members of the Cardio-Oncology Study Group of the Italian Society of Cardiology. Results 365 patients were seen during the study period, of whom 238 (65.2%) had solid tumours and 127 (34.8%) haematological tumours. The mean age was 66 (14-93) years and 159 (43.6%) were male. The most common cancers were breast cancer (BC; N=106, 29.0%), non-Hodgkin lymphoma (NHL; N=39, 10.7%) and acute myeloid leukaemia (N=21, 5.8%). The most frequent cancer treatment was immune checkpoint inhibitor (N=76, 20.8%). Consistent with the predominance of BC, chemotherapy agents, HER2-targeting drugs, and endocrine therapy were well represented (Fig. 1A). Sixty-four (17.5%) patients were not scheduled for, nor were receiving cancer treatment at the time of evaluation. The cardio-oncology visit was most often performed for monitoring of cardiovascular (CV) toxicity of ongoing cancer treatment (N=164, 44.9%), or for baseline risk assessment (N=100, 27.4%). The remaining patients were seen because of abnormal cardiac biomarkers, abnormal cardiac imaging, CV symptoms/signs, CV events, or overt CV disease (Fig. 1B). After the cardio-oncological evaluation, follow-up without any intervention was suggested for the majority of subjects (N=240, 65.8%). Another 105 (28.8%) were initiated on CV medications. Cancer treatment was stopped in 9 patients (2.5%), 4 (1.1%) were admitted, and 7 (1.9%) were not further followed (Fig. 1B). Patients with ≥70 years (N=140) were more often male (54.3% vs 36.9%, p=0.001) and had less often BC (17.9% vs 36%, P <0.001) than those younger than 70 years. There were non-significant trends for higher use of tyrosine kinase inhibitor (14.3% vs 8.4%, p=0.08) and lower use of HER2-targeting therapies (6.4% vs 11%, p=0.06) and hematopoietic stem cell transplantation (1.4% vs 5.3%, p=0.06) in older than younger patients. The reasons and outputs of the cardio-oncology assessment were similarly distributed in the 2 groups. Conclusions This cross-sectional study indicates that cardio-oncology practice in Italy largely consists of baseline evaluation and follow-up of patients with cancer, especially BC, with or without optimization of CV therapy, in agreement with the core tasks outlined by the ESC Guidelines on Cardio-Oncology. We did not detect substantial differences in the management of younger vs older patients.Figure 1
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".