Physical Comorbidities and Their Relationship with Cancer Treatment and Its Outcomes in Older Adult Populations: Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Cancer is one of the predominant causes of morbidity and mortality in older adult populations worldwide. Among a range of barriers, comorbidity particularly poses a clinical challenge in cancer diagnosis, prognosis, and treatment owing to its heterogeneous nature. While accurate comorbidity assessments and appropriate treatment administration can result in better patient outcomes, evidence related to older adult cancer populations is limited as these individuals are often excluded from regular clinical trials due to age and comorbid conditions. OBJECTIVE: To determine the prevalence of physical comorbidity and the impact of physical comorbidities and rurality on treatment and its outcomes in older adult cancer populations. METHODS: Scientific databases Embase and PubMed were searched for published scientific literature on physical comorbidity and older adult cancer patients. Google Scholar was searched for scholarly literature published in nonindexed journals. Snowballing was utilized to identify research papers missed in the above searches. Included studies : (1) reported on original research involving cancer patients; (2) included patients aged 65 years or older; (3) had patients receiving cancer-related treatment and (4) cancer survivors; (5) reported on physical comorbidity as a variable; (6) were published in English; and (7) conducted from any geographical location. RESULTS: In total, 29 studies were selected for data extraction, evidence synthesis, and quality assessment. In these, comorbidities ranged from 37.9%-74.3% in colorectal cancer, 74%-81% in head and neck cancer, and 12.6%-49% in breast cancer. Moderate comorbidities ranged from 13%-72.9%, and severe comorbidities from 2.5%-68.2%. Comorbidity increased with age, with comorbidity affecting both treatment choice and process. Physical comorbidities significantly affected treatment initiation, causing delay, toxicity, and discontinuation. Older adult cancer patients were given less vigorous and nonstandard treatments and were also less likely to be offered treatment. Where patients are given more vigorous treatment, several studies showed better survival outcomes. Appropriate treatment in older adult cancer patients increased both overall and disease-related survival rates. None of the studies noted rurality as a distinct variable. CONCLUSIONS: This systematic review concludes that there is evidence to substantiate the adverse effect of comorbidity on treatment and survival outcomes. However, the mechanism by which comorbidity impedes or impacts treatment is unknown in many cases. Some low-quality evidence is available for considering the functional status and biological age in treatment decisions. Future studies that substantiate the value of comprehensive older adult assessments before treatment initiation in cancer patients, including assessing the nature and severity of comorbidities, and additional consideration of rurality as a factor, could lessen the effect of comorbidities on the treatment process.
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 | 0,006 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,007 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».