An Interdisciplinary Ecosystem for the Prevention of Cardiotoxicity in Older Patients With Breast Cancer: Protocol for a Prospective and Multicentric Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,031 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,037 | 0,008 |
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 source (Gemma direct ou Codex distillé), 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 ».