An Interdisciplinary Ecosystem for the Prevention of Cardiotoxicity in Older Patients With Breast Cancer: Protocol for a Prospective and Multicentric Study
Bibliographic record
Abstract
BACKGROUND: Over 50% of newly diagnosed patients with breast cancer are aged ≥65 years. Due to age-related factors and the presence of comorbidities, these patients are particularly vulnerable to developing cardiac toxicity associated with cancer treatments, which may lead to suboptimal interventions and undertreatment, resulting in poorer health outcomes, quality of life (QoL) deterioration, and increased health care costs. Given the underrepresentation of older patients with breast cancer in clinical trials and the increasing recognition of the impact of psychosocial and behavioral factors on cardiovascular disease onset, broader and interdisciplinary studies are required to develop new and innovative best practices for this clinical population. OBJECTIVE: Using an innovative eHealth approach combining the CARDIOCARE (An Interdisciplinary Approach for the Management of the Elderly Multimorbid Patient with Breast Cancer Therapy Induced Cardiac Toxicity-grant agreement 945175) mobile app and technologically advanced wearable devices (ie, the Garmin Venu SQ watch and Polar H10 sensor), the CARDIOCARE prospective study pursues a twofold aim: (1) testing the effectiveness of the CARDIOCARE mobile app to monitor and assess the intrinsic capacity and QoL of older patients with breast cancer and evaluating the CARDIOCARE eHealth interventions' effectiveness on these parameters and (2) developing a holistic, patient-centered risk prediction model specific for the detection of cardiotoxicity before it clinically emerges. METHODS: This prospective and multicentric study involves 6 clinical and 5 technical partners across Europe. In total, 750 older patients with breast cancer (aged ≥60 years) are assigned to either the standard practice or enhanced monitoring group, with only patients in the latter receiving access to eHealth psychological, behavioral, and functional interventions implemented on the CARDIOCARE eHealtHeart app. Patients will be recruited in 6 clinical centers and will undergo clinical procedures to collect multimodal data, including clinical data, cardiac imaging, biochemical and psychological biomarkers and omics, intrinsic capacity, and QoL indicators measured at baseline (T0) and every 3 months up to 12 months (T5). RESULTS: CARDIOCARE is a project funded by Horizon 2020, and enrollment started in May 2023. As of October 17, 2024, a total of 50% (375/750) of the target number of patients had been recruited. CONCLUSIONS: The CARDIOCARE prospective study will contribute to developing new best practice guidelines for managing older patients with breast cancer and multimorbidity while preserving their intrinsic capacity and improving their QoL. Furthermore, the CARDIOCARE mobile app and the wearable devices will allow clinicians to identify trajectories across the cardiotoxicity disease continuum and thus intervene in a preventative way among patients at higher risk. Such a health care approach will also benefit the health care system, which currently spends almost 40% of its resources on patients aged >60 years, with long-term care and hospital admissions being the primary cost drivers. TRIAL REGISTRATION: ClinicalTrials.gov NCT06334445; https://clinicaltrials.gov/study/NCT06334445. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63455.
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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 | 0.031 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".