Determining clinically important differences in health-related quality of life in older patients with cancer undergoing chemotherapy or surgery

PURPOSE: Using the EORTC Global Health Status (GHS) scale, we aimed to determine minimal clinically important differences (MCID) in health-related quality of life (HRQOL) changes for older cancer patients with a geriatric risk profile, as defined by the geriatric 8 (G8) health screening tool, undergoing treatment. Simultaneously, we assessed baseline patient characteristics prognostic for HRQOL changes. METHODS: Our analysis included 1424 (G8 ≤ 14) older patients with cancer scheduled to receive chemotherapy (n = 683) or surgery (n = 741). Anchor-based methods, linking the GHS score to clinical indicators, were used to determine MCID between baseline and follow-up at 3 months. A threshold of 0.2 standard deviation (SD) was used to exclude MCID estimates too small for interpretation. Logistic regressions analysed baseline patient characteristics prognostic for HRQOL changes. RESULTS: The 15-item Geriatric Depression Scale (GDS15), Visual Analogue Scale (VAS) for Fatigue and ECOG Performance Status (PS) were selected as clinical anchors. In the surgery group, MCID estimates for improvement and deterioration were ECOG PS (5*, 11*), GDS15 (5*, 2) and VAS Fatigue (3, 9*). In the chemotherapy group, MCID estimates for improvement and deterioration were ECOG PS (8*, 7*), GDS15 (5, 4) and VAS Fatigue (5, 5*). Estimates with * were > 0.2 SD threshold. Patients experiencing pain or malnutrition (surgery group) or fatigue (chemotherapy group) at baseline showed a significantly stable or improved HRQOL (p < 0.05) after their treatment. CONCLUSION: The reported MCID for improvement and deterioration depended on the anchor used and treatment received. The estimates can be used to evaluate significant changes in HRQOL and to determine sample sizes in clinical trials

Molecular Basis of Cannabis-Induced Schizophrenia-Relevant Behaviours: Insights from Animal Models

Introduction: Cannabis use is a well-established component risk factor for schizophrenia; however, the mechanisms by which cannabis use increases schizophrenia risk are unclear. Animal models can elucidate mechanisms by which chronic cannabinoid treatment can induce schizophrenia-relevant neural changes, in a standardised manner often not possible using patient-based data. Methods: We review recent literature (within the past 10 years) using animal models of chronic and subchronic treatment with cannabinoids which target the cannabinoid 1 receptor [i.e. ∆9-tetrahydrocannabinol, CP55,940 and WIN55,212-2]. Schizophrenia-relevant behavioural consequences of chronic cannabinoid treatment are first briefly summarised, followed by a detailed account of changes to several receptor systems [e.g. cannabinoid, dopaminergic, glutamatergic, γ-aminobutyric acid (GABAe)rgic, serotonergic, noradrenergic], dendritic spine morphology and inflammatory markers following chronic cannabinoids. We distinguish between adolescent and adult cannabinoid treatments, to determine if adolescence is a period of susceptibility to schizophrenia-relevant molecular changes. Results: Chronic cannabinoid treatment induces behaviours relevant to positive, negative and cognitive symptoms of schizophrenia. Chronic cannabinoids also cause region- and subtype-specific changes to receptor systems (e.g. cannabinoid, dopaminergic, glutamatergic, GABAergic), as well as changes in dendritic spine morphology and upregulation of inflammatory markers. These changes often align with molecular changes observed in post-mortem tissue from schizophrenia patients and correspond with schizophrenia-relevant behavioural change in rodents. There is some indication that adolescence is a period of susceptibility to cannabinoid-induced schizophrenia-relevant neural change, but more research in this field is required to confirm this hypothesis. Conclusions: Animal models indicate several molecular mechanisms by which chronic cannabinoids contribute to schizophrenia-relevant neural and behavioural change. It is likely that a number of these mechanisms are simultaneously impacted by chronic cannabinoids, thereby increasing schizophrenia risk in individuals who use cannabis. Understanding how cannabinoids can affect several molecular targets provides critical insight into the complex relationship between cannabis use and schizophrenia risk