Use of Drugs in Clinical Practice and the Associated Cost of Cancer Treatment in Adult Patients with Solid Tumors: A 10-Year Retrospective Cohort Study
Notice bibliographique
Résumé
BACKGROUND: Cancer is one of the leading causes of morbidity and mortality in the world. Its growing incidence and prevalence, as well as the advances in diagnostic and treatment tools, motivate an open debate about the economic burden it may place on health systems and have raised concerns about access to this technological innovation. There is a lack of information on the detailed costs of pharmacological treatment of cancer in our health setting. In this context, it is necessary to know the use of drugs in cancer treatment in conditions of real clinical practice. A real-word, evidence-based retrospective cohort study was conducted at Vall d'Hebron University Hospital (VHUH), the largest hospital complex in Catalonia, Spain, in order to determine the use of drugs and the associated cost in real clinical practice for the treatment of solid tumors in adult patients attended at this institution over 10 years (2010-2019). METHODS: This was a single-center retrospective cohort study of adult cancer patients attended in clinical practice at the Medical Oncology Department of VHUH between 1 January 2010 and 31 December 2019. Data of prescription, preparation, and cost of antineoplastic treatments were analyzed by pharmacological class (cytotoxic drugs, immunotherapy, targeted therapy, radiopharmaceuticals, and others), by antineoplastic agent, and by type of tumor. The number of patients and the pharmaceutical expenditure corresponding to all these subgroups were recorded. The cost per patient in each tumor location was also calculated. RESULTS: The study population included 13,209 patients with an overall pharmaceutical antineoplastic expenditure of EUR 120,396,097, increasing from 7.67% in relation to the total HUVH pharmaceutical expenditure in 2010 to 12.82% in 2019. By pharmacological class, the specific weight of the cost of targeted therapy is relevant (75.22% of pharmaceutical antineoplastic expenditure, 21.3% of patients) compared to the group of conventional cytotoxics (17.25% of pharmaceutical antineoplastic expenditure, 76.37% of patients), while immunotherapy has represented the largest relative increase, from 5% in 2014 to 12% in 2019. Eight targeted therapy drugs represented 50% of the costs of the targeted therapy drug class (palbociclib, trastuzumab, pertuzumab, bevacizumab, nivolumab, cetuximab, pembrolizumab, and trastuzumab emtansine). Eleven tumor sites accounted for 90% of the expenditure in 71% of all patients. Breast cancer had the highest expenditure during the study period (EUR 34,332,210) and at each individual year. Melanoma showed the highest increase, with 9.7% of total pharmaceutical antineoplastic expenditure in 2019 (2% of patients), representing a paradigm of the rising costs of cancer treatment due to the incorporation of new high-cost therapies. The average annual cost per patient was highly variable depending on the pathology. There was a growing increase in costs per patient in most tumor locations, particularly in patients with melanoma (from EUR 1922 in 2010 to EUR 37,020 in 2019), prostate cancer (from EUR 2992 in 2010 to EUR 14,118 in 2019), and non-small cell lung cancer (from EUR 3545 in 2010 to EUR 8371 in 2019). The relevance of the difference in monthly cost per patient that has been identified for the different intrinsic subtypes in breast cancer patients during 2019 (HER2+ EUR 2661/month, Luminal EUR 881/month, Triple negative EUR 386/month) makes us consider suggesting differentiated reimbursement rates for certain clinical conditions. Finally, support treatment with antiemetic drugs, erythropoietin stimulating agents, granulocyte-colony stimulating factor (G-CSF), and bone resorption inhibitors has involved a cost of EUR 5,751,910, which represents 4.6% of the overall pharmacological cost of cancer treatment. CONCLUSION: This study provides detailed insights on the oncological pharmaceutical expenditure for the treatment for solid tumors in the VHUH, based on real cost information from our hospital practice and for all antineoplastic therapies and types of solid tumors. This type of information on all the different types of cancer can be useful to better understand the economic burden of the disease and can be decisive for allocating public resources and funds for research, especially in those areas where information is scarce and therefore where further studies are needed. The contribution to knowledge of the cost of oncology therapy is of great value due to its realism and scope.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».