An Evaluation of Co-Occurring Cancer-Related Cognitive Impairment and Anxiety in Patients Receiving Chemotherapy Using Latent Variable Modeling and Pathway Impact Analysis
Notice bibliographique
Résumé
Cognitive changes associated with cancer and its treatments, known as cancer-related cognitive impairment (CRCI), are reported by up to 75% of patients and 60% of those who have completed treatment. Because a number of cognitive domains are impacted, CRCI results in decrements in multiple domains of quality of life. In addition, because of gaps in knowledge regarding its underlying mechanism(s), progress is slow in the development of prevention or mitigation strategies. Equally important, anxiety is a common symptom that co-occurs with CRCI. Despite high prevalence rates for both symptoms, CRCI and anxiety are often evaluated as individual symptoms. However, because anxiety can impact cognitive function and vice versa, an assessment of the co-occurrence of both symptoms warrants evaluation.Therefore, the first three aims of this dissertation were to: 1) develop a comprehensive conceptual model of CRCI; 2) test this newly developed conceptual model; and 3) conduct a scoping review of the literature to describe the depth and breadth of available evidence on blood-based biomarkers of CRCI. In addition, using data from a sample of patients with heterogenous types of cancer with distinct joint CRCI AND anxiety profiles (n=1332), the fourth, fifth, and sixth aims of this dissertation were to: evaluate for differences in demographic and clinical characteristics among the three CRCI AND anxiety latent classes; evaluate for differences in levels of global stress, cancer-specific stress, cumulative life stress, and resilience among the three CRCI AND anxiety latent classes; and evaluate for perturbed pathways associated with membership in the No CRCI AND Low Anxiety class compared to the High CRCI AND High Anxiety class. \nIn terms of Aim 1, an original comprehensive conceptual model of CRCI, named the Multifactorial Model of CRCI (MMCRCI), was developed. The MMCRCI was designed based on a review of the literature that included over 100 review and state of the science papers published between 2017 and 2022. The specific concepts in the conceptual model include social determinants of health, patient-specific factors, co-occurring symptoms, treatment factors, and biologic mechanisms. The model can be used to design pre-clinical and clinical studies of CRCI. \nIn terms of Aim 2, structural regression methods were used to evaluate the MMCRCI using data from a large sample of outpatients receiving chemotherapy for a variety of cancers. The goals were to determine how well the concepts in the MMCRCI predicted CRCI and to determine the relative contribution of each of these concepts to deficits in perceived cognitive function. Of the four MMCRCI concepts evaluated, while co-occurring symptoms explained the largest amount of variance in CRCI, treatment factors explained the smallest amount of variance. These findings suggest that testing individual components of the MMCRCI may provide useful information on the relationships among various risk factors for CRCI, as well as refinements of the model. \nIn terms of Aim 3, a scoping review was done that synthesized the extant literature on associations between subjective and/or objective measures of CRCI and blood-based biomarkers in adults with non-central nervous system cancers. A total of 95 studies were included in this review. Of note, a wide variety of biomarkers were examined. The majority of studies evaluated patients with breast cancer. A variety of cognitive assessment measures were used. The most consistent significant associations were with various subjective and objective measures of CRCI and levels of interleukin-6 and tumor necrosis factor. This review concluded with directions for future research.\nIn terms of Aims 4 and 5, a latent profile analysis identified subgroups of patients with distinct joint CRCI AND anxiety profiles (i.e., latent classes). In addition, differences in demographic and clinical characteristics, as well as levels of global stress, cancer-specific stress, cumulative life stress, and resilience were reported. In terms of the symptom profiles, three latent classes were identified (i.e., No CRCI AND Low Anxiety (57.3%), Moderate CRCI AND Moderate Anxiety (34.5%), and High CRCI AND High Anxiety (8.2%)). All of the stress measures showed a dose response pattern. Higher levels of co-occurring CRCI AND anxiety were associated with several demographic (e.g., age, marital status) and clinical (e.g., functional status, comorbidity burden) characteristics, as well with as with higher levels of stress and lower levels of resilience. Increased knowledge of modifiable characteristics and sources of stress associated with the co-occurrence of these two symptoms will assist clinicians to identify high risk patients, and with the development and testing of interventions.\nIn terms of Aim 6, perturbations in neurodegenerative pathways associated with the CRCI AND High anxiety classes were identified. Five neurodegenerative pathways were significantly perturbed, namely: Amyotrophic lateral sclerosis, Huntington disease, Parkinson disease, Prion disease, and Pathways of neurodegeneration - multiple diseases. Four common biological processes across these perturbed neurodegenerative pathways were identified (i.e., apoptosis, mitochondrial function, endoplasmic stress, oxidative stress). While these findings warrant confirmation, they suggest that these two symptoms may share common mechanisms across patients with cancer and patients with neurodegenerative diseases. \n
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».