The association between muscle mass and the degree of myosteatosis of the psoas muscle and mortality in older patients with cancer.

OBJECTIVE: Comprehensive geriatric assessment (CGA) is used for oncological management in older patients. The evaluation of muscle characteristics is currently not included in the CGA. This study investigates whether muscle mass and the degree of myosteatosis is associated with mortality in older patients with cancer. METHODS: CGA was performed in a cohort of older patients with cancer. Cross sectional area (CSA) and mean pixel density (Hounsfield units, HU), as measure for respectively muscle mass and myosteatosis, were obtained from CT images of the psoas muscle at the level of mid L3. Mortality was recorded. Correlation was determined between CSA and HU. Paired sample t-test was used to follow changes in muscle mass and density. Logistic regression was performed to define relevant prognostic factors for mortality. RESULTS: In total, 183 patients were included (86 male and 97 female), 120 patients (66%) died. Mean age was 80 years (range 70-94 years). Mean days of survival was 606 (range 1-2023). There was a significant correlation between CSA and HU (PCC = 0.196) at time of diagnosis and at follow-up (PCC = 0.257). There was a significant decrease in CSA (p = .008) and HU (p = .004) in men at follow-up. No significant changes were observed in women. In multivariate analysis, a higher gender-corrected CSA was linked to a lower mortality rate with an odds ratio of 0.657 (CI = 0.457-0.944, p = .023). No association was found between HU and mortality. CONCLUSIONS: Muscle mass correlated with the degree of myosteatosis. CSA and HU tended to decrease during follow-up. Having a greater CSA was prognostic for a lower mortality rate.

Health related quality of life in older patients with solid tumors and prognostic factors for decline.

OBJECTIVES: This study aims to investigate health-related quality of life (HRQOL) at baseline and at follow-up in older patients with cancer and to determine prognostic factors for HRQOL decline. METHODS: A prospective Belgian multicentre (n = 22) study was performed. Patients ≥70 years with a malignant tumor and abnormal G8 (≤14/17) screening tool were included. Patients underwent geriatric assessment (GA) and HRQOL evaluation with follow up at three months. Uni- and multivariate regression models were performed to determine factors associated (p < .05) with baseline HRQOL and HRQOL decline at follow-up. RESULTS: Results reflect data collected from 3673 patients. A multivariate analysis showed that younger patients, and those with poor Eastern Cooperative Oncology Group - Performance Status (ECOG-PS), specific tumor types (gastrointestinal, gynaecological and thorax) and higher stage had lower baseline HRQOL. In addition worse functional status and presence of pain, fatigue, depression and malnutrition were associated with lower baseline HRQOL. During treatment (n = 2972), improvement in HRQOL was observed in 1037 patients (35%) and a decline in 838 patients (28.2%). In multivariate analysis, stage and presence of baseline comorbidities, pain, fatigue or malnutrition were associated with HRQOL evolution. CONCLUSION: Baseline HRQOL in older patients with cancer and an abnormal G8 depends on tumor and age related parameters. During follow-up, HRQOL improved in one third of patients, indicating that they may benefit from cancer treatment while one quarter demonstrated a HRQOL decline for which prognostic factors were identified.

The prognostic value of 3 commonly measured blood parameters and geriatric assessment to predict overall survival in addition to clinical information in older patients with cancer.

BACKGROUND: The current study was performed to evaluate the prognostic value of laboratory parameters and geriatric assessment (GA) in addition to a baseline model with clinical information regarding overall survival (OS) in patients with cancer. METHODS: GA was systematically performed in patients aged ≥70 years. The baseline model consisted of age, tumor type, and stage of disease. The incremental prognostic values of the GA as a whole (10-item GA) and laboratory parameters were assessed separately and combined. The parameters included hemoglobin (Hb), albumin, C-reactive protein (CRP), and the Glasgow Prognostic Score (GPS). Analyses were conducted with continuous and dichotomized variables. Cox models were compared based on Akaike information criterion (ΔAIC) and their discriminatory ability was assessed using the concordance probability estimate (CPE). RESULTS: A total of 328 patients were considered for this analysis. The baseline model had a CPE of 0.725. The addition of CRP, albumin, and Hb combined resulted in the best performing model (ΔAIC: 40.12 and CPE: 0.757) among the laboratory parameters. However, the 10-item GA improved the baseline model even more (ΔAIC: 46.03 and CPE: 0.769). Similar results were observed in the analysis with dichotomous variables. The addition of the 3 laboratory parameters (CRP, albumin, and Hb) improved the CPE by 1.4% compared with the baseline model already extended with the 10-item GA. The CPE increase (1.7%) was the highest with the GPS in the analysis with dichotomous variables. CONCLUSIONS: GA appears to add slightly more prognostic information than laboratory parameters in addition to clinical information. The laboratory parameters have an additional prognostic value beyond clinical and geriatric information.

The added value of geriatric screening and assessment for predicting overall survival in older patients with cancer.

BACKGROUND: The aim of this study was to determine and compare the added prognostic value of screening tools, geriatric assessment (GA) components, and GA summaries to clinical information for overall survival (OS) in older patients with cancer. METHODS: A screening and a 10-item GA were systematically performed in patients ≥70 years old with cancer. Cox regression analyses were conducted to evaluate the added prognostic value for OS of screening tools, GA, and GA summaries to clinical information (age, stage, and tumor type) in 2 cohorts (A and B). Cox models were compared on the basis of the Akaike information criterion and the concordance probability estimate. The 2 cohorts for the analyses were similar but independent. RESULTS: A complete case analysis was available for 763 patients (median age, 76 years) in cohort A and for 402 patients (median age, 77 years) in cohort B. In both cohorts, most individual GA components were independent prognostic factors for OS. Nutritional status (assessed with the Mini Nutritional Assessment Short Form) and functional status (assessed with the Instrumental Activities of Daily Living) consistently displayed a strong capacity to predict OS. Less consistent results were found for screening tools. GA summaries performed the best in comparison with the screening tools and the individual GA components. CONCLUSIONS: Most individual GA components, especially nutritional status and functional status, are prognostic factors for OS in older patients with cancer. GA summaries provide more prognostic information than individual GA components but only moderately improve the prognostic baseline model with clinical information.

The influence of coping strategies on subsequent well-being in older patients with cancer: A comparison with 2 control groups.

OBJECTIVE: To evaluate dispositional coping strategies as predictors for changes in well-being after 1 year in older patients with cancer (OCP) and 2 control groups. METHODS: OCP were compared with 2 control groups: middle-aged patients with cancer (MCP) (aging effect) and older patients without cancer (ONC) (cancer effect). Patients were interviewed shortly after a cancer diagnosis and 1 year later. Dispositional coping was measured with the Short Utrecht Coping List. For well-being, we considered psychological well-being (depression, loneliness, distress) and physical health (fatigue, ADL, IADL). Logistic regression analyses were performed to study baseline coping as predictor for subsequent well-being while controlling for important baseline covariates. RESULTS: A total of 1245 patients were included in the analysis at baseline: 263 OCP, 590 ONC, and 392 MCP. Overall, active tackling was employed most often. With the exception of palliative reacting, OCP utilized each coping strategy less frequently than MCP. At 1-year follow-up, 833 patients (66.9%) were interviewed. Active coping strategies (active tackling and seeking social support) predicted subsequent well-being only in MCP. Avoidance coping strategies did not predict well-being in any of the patient groups. Palliative reacting predicted distress in OCP; depression and dependency for ADL in MCP. CONCLUSIONS: Coping strategies influence subsequent well-being in patients with cancer, but the impact is different in the age groups. Palliative reacting was the only coping strategy that predicted well-being (ie, distress) in OCP and is therefore, especially in this population, a target for coping skill interventions.

The utilization of formal and informal home care by older patients with cancer: a Belgian cohort study with two control groups.

BACKGROUND: The purpose of this paper is to analyse the utilization of formal and informal home care among older patients with cancer (OCP) and to compare this with middle-aged patients with cancer (MCP) and older patients without cancer (ONC). Additionally, we examined predictors of transitions towards formal care one year after a cancer diagnosis. METHODS: OCP and MCP had to be recruited within three months after a cancer diagnosis and have an estimated life expectancy over six months. ONC consisted of patients without known cancer, seen by the general practitioner. Formal and informal care were compared between the patient groups at baseline, i.e. shortly after a cancer diagnosis and changes in care were studied after one year. RESULTS: A total of 844 patients were evaluable for formal care at baseline and 469 patients (56%) at follow-up. At baseline, about half of older adults and 18% of MCP used formal care, while about 85% of cancer patients and 57% ONC used informal care. Formal care increased for all groups after one year though not significantly in OCP. The amount of informal care only changed in MCP which decreased after one year. Cancer-related factors and changes in need factors predict a transition towards formal care after a cancer diagnosis. CONCLUSIONS: A cancer diagnosis has a different impact on the use of formal and informal care than ageing as such. The first year after a cancer diagnosis is an important time to follow-up on the patients' needs for home care.

Corrigendum to "Implementation of geriatric assessment-based recommendations in older patients with cancer: A multicenter prospective study" [J. Geriatr. Oncol. 6 (2015) 401-410].

The authors regret: A calculation error was corrected in Table 3. As mentioned under the table, the percentage of patients under 'Baseline' was calculated from the total no. of patients with geriatric recommendations data (n = 932 pts). This was mistakenly calculated from the number of patients with available GA data (n = 979). Percentages have been recalculated. The corrected table is reproduced here (Table 3). We emphasize that these percentages are not mentioned in the text of the paper nor do they change any of the conclusions. The authors would like to apologize for any inconvenience caused.

Implementation of geriatric assessment-based recommendations in older patients with cancer: A multicentre prospective study.

PURPOSE: The main objective of this study was to describe geriatric recommendations based on a geriatric assessment (GA) and to evaluate the implementation of these recommendations. PATIENTS AND METHODS: A two-step approach of screening followed by a GA was implemented in nine hospitals in Belgium. Patients ≥ 70 years were included at diagnosis or at disease progression/relapse. Concrete geriatric recommendations were systematically documented and reported to the treating physicians and consisted of referrals to professional health care workers. Patient charts were reviewed after one month to verify which geriatric recommendations have been performed. RESULTS: From August 2011 to July 2012, 1550 patients were included for analysis. The median age was 77 (range: 70-97) and 57.0% were female. A solid tumour was diagnosed in 91.4% and a haematological malignancy in 8.6%. Geriatric screening with the G8 identified 63.6% of the patients for GA (n=986). A median of two geriatric recommendations (range: 1-6) were given for 76.2% (95%CI: 73.4-78.8) of the evaluable patients (n=710). A median of one geriatric recommendation (range: 1-5) was performed in 52.1% (95%CI: 48.4-55.8) of the evaluable patients (n=689). In general, 460 or 35.3% (95%CI: 32.8-38.0) of all the geriatric recommendations were performed. Geriatric recommendations most frequently consisted of referrals to the dietician (60.4%), social worker (40.3%), and psychologist (28.9%). CONCLUSION: This implementation study provides insight into GA-based recommendations/interventions in daily oncology practice. Geriatric recommendations were given in about three-fourths of patients. About one-third of all geriatric recommendations were performed in approximately half of these patients.

Geriatric screening results and the association with severe treatment toxicity after the first cycle of (radio)chemotherapy.

BACKGROUND: Screening tools are used in geriatric oncology to determine who should receive a Comprehensive Geriatric Assessment (CGA). However, in this prospective study, we evaluated the association between geriatric screening results, measured with the G8 and Groningen Frailty Indicator (GFI), and severe treatment toxicity. METHODS: Patients over 65 years with various types and stages of cancer were screened with the G8 and the GFI prior to the start of treatment. The association between geriatric screening results and Serious Adverse Events (SAE) after the first cycle of (radio)chemotherapy were studied with bivariate analysis (normal versus abnormal screening test) and logistic regression analysis. RESULTS: From 170 screened patients, 85 patients were eligible for this study. The median age was 76 years (range: 66-88 years). The treatment intent was curative in 46% and palliative in 54%. A SAE occurred in 15 patients (18%) of which three resulted in death. There was no significant association between the G8, as a dichotomous predictor (p = 0.376) or as a continuous predictor (p = 0.298), and risk of a SAE. We also found no significant association for the GFI analysed as a dichotomous predictor (cut-off ≥4: p = 0.384; cut-off ≥3: p = 0.773), nor as a continuous predictor (p = 0.734). All associations remained insignificant when adjusted for treatment type and comorbidity. CONCLUSION: The G8 and the GFI can be used to select patients for CGA, but they do not seem to be predictive for short-term severe treatment toxicity.

Evaluation of the Groningen Frailty Indicator and the G8 questionnaire as screening tools for frailty in older patients with cancer.

OBJECTIVE: In this study, we evaluated the Groningen Frailty Indicator (GFI) and the G8 questionnaire as screening tools for a Comprehensive Geriatric Assessment (CGA) in older patients with cancer. PATIENTS AND METHODS: Eligible patients with various types and stages of cancer were evaluated for frailty before treatment. Patients were categorized as patients with a normal CGA and abnormal CGA (≥2 impaired tests). The diagnostic performance of the screening tools was evaluated against the CGA with Receiver Operating Characteristic analysis. RESULTS: In total, 170 patients (79 women) with median age 77years old (range 66-97years) were included. Sixty-four percent of patients had an abnormal CGA while according to the GFI (GFI≥4) and G8 questionnaire (G8≤14) 47% and 76% of patients had an abnormal screening test, respectively. Overall, there was no significant difference (p=0.97) in diagnostic performance between the two screening tools. The Area Under the Curve was 0.87 for both tools. For the GFI and G8 questionnaire the sensitivity was respectively 66% (95% CI: 56-75%), 92% (95% CI: 85-96%); the negative predictive value (NPV): 59% (95 CI%: 49-69%), 78% (95% CI: 63-88%); and the specificity: 87% (95% CI: 76-94%), 52% (95% CI: 39-65%). CONCLUSION: In this study, we showed that overall both the GFI and the G8 questionnaire were able to separate older patients with cancer with a normal and abnormal CGA. For the G8 questionnaire, an adequate sensitivity and NPV were demonstrated, however at the expense of the specificity. For the GFI, we suggest to lower the threshold with one point to GFI ≥3 to screen patients for a CGA.

In vivo type 1 cannabinoid receptor mapping in the 6-hydroxydopamine lesion rat model of Parkinson's disease.

Type 1 cannabinoid (CB1) receptors are expressed in high concentrations in the central nervous system, including the basal ganglia, and could have direct or indirect effects on motor behavior through modulation of dopaminergic, glutamatergic and GABA-ergic neurotransmission. Using the CB1 receptor radioligand [(18)F]MK-9470 and small-animal PET, we investigated for the first time in vivo cerebral changes in [(18)F]MK-9470 binding in the 6-hydroxydopamine (6-OHDA) rat model of Parkinson's disease (PD), parallel to dopamine transporter (DAT) imaging, tyrosine hydroxylase (TH) staining, and behavioral measurements. In the 6-OHDA model, relative [(18)F]MK-9470 PET binding decreased in the contralateral cerebellum (-9%, p<0.0004) and caudate-putamen bilaterally (ipsilateral -8%, contralateral -7%; p=0.001 and p<0.0003, respectively). The number of TH(+) neurons in the substantia nigra was inversely correlated to CB1 receptor binding in the ipsilateral cerebellum (p=1.10(-6)). The behavioral outcome was positively related to regional CB1 receptor binding in the contralateral somatosensory cortex (p=4.10(-6)). In vivo [(18)F]MK-9470 PET imaging points to changes in endocannabinoid transmission, specifically for CB1 receptors in the 6-OHDA model of PD, with mainly involvement of the caudate-putamen, but also distant regions of the motor circuitry, including the cerebellum and somatosensory cortex.