Symptom Clusters and Survival in Portuguese Patients with Advanced Cancer

This study aimed to identify clusters of symptoms, to determine the patient characteristics associated with identified, and determine their strength of association with survival in patients with advanced cancer (ACPs). Consecutively eligible ACPs not receiving cancer-specific treatment, and referred to a Tertiary Palliative Care Clinic, were enrolled in a prospective cohort study. At first consultation, patients rated 9 symptoms through the Edmonton Symptom Assessment System (0-10 scale) and 10 others using a Likert scale (1-5). Principal component analysis was used in an exploratory factor analysis to identify. Of 318 ACPs, 301 met eligibility criteria with a median (range) age of 69 (37-94) years. Three SCs were identified: neuro-psycho-metabolic (NPM) (tiredness, lack of appetite, lack of well-being, dyspnea, depression, and anxiety); gastrointestinal (nausea, vomiting, constipation, hiccups, and dry mouth) and sleep impairment (insomnia and sleep disturbance). Exploratory factor analysis accounted for 40% of variance of observed variables in all SCs. Shorter survival was observed for patients with the NPM cluster (58 vs. 23, P < 0.001), as well as for patients with two or more SCs (45 vs. 21, P = 0.005). In a multivariable model for survival at 30-days, age (HR: 0.98; 95% CI: 0.97-0.99; P = 0.008), hospitalization at inclusion (HR: 2.27; 95% CI: 1.47-3.51; P < 0.001), poorer performance status (HR: 1.90, 95% CI: 1.24-2.89; P = 0.003), and NPM (HR: 1.64; 95% CI: 1.17-2.31; P = 0.005), were associated with worse survival. Three clinically meaningful SC in patients with advanced cancer were identifiable. The NPM cluster and the presence of two or more SCs, had prognostic value in relation to survival.

Ocular Pulse Amplitude and Doppler Waveform Analysis in Glaucoma Patients

PURPOSE: To determine the correlation between ocular blood flow velocities and ocular pulse amplitude (OPA) in glaucoma patients using colour Doppler imaging (CDI) waveform analysis. METHOD: A prospective, observer-masked, case-control study was performed. OPA and blood flow variables from central retinal artery and vein (CRA, CRV), nasal and temporal short posterior ciliary arteries (NPCA, TPCA) and ophthalmic artery (OA) were obtained through dynamic contour tonometry and CDI, respectively. Univariate and multiple regression analyses were performed to explore the correlations between OPA and retrobulbar CDI waveform and systemic cardiovascular parameters (blood pressure, blood pressure amplitude, mean ocular perfusion pressure and peripheral pulse). RESULTS: One hundred and ninety-two patients were included [healthy controls: 55; primary open-angle glaucoma (POAG): 74; normal-tension glaucoma (NTG): 63]. OPA was statistically different between groups (Healthy: 3.17 ± 1.2 mmHg; NTG: 2.58 ± 1.2 mmHg; POAG: 2.60 ± 1.1 mmHg; p < 0.01), but not between the glaucoma groups (p = 0.60). Multiple regression models to explain OPA variance were made for each cohort (healthy: p < 0.001, r = 0.605; NTG: p = 0.003, r = 0.372; POAG: p < 0.001, r = 0.412). OPA was independently associated with retrobulbar CDI parameters in the healthy subjects and POAG patients (healthy CRV resistance index: β = 3.37, CI: 0.16-6.59; healthy NPCA mean systolic/diastolic velocity ratio: β = 1.34, CI: 0.52-2.15; POAG TPCA mean systolic velocity: β = 0.14, CI 0.05-0.23). OPA in the NTG group was associated with diastolic blood pressure and pulse rate (β = -0.04, CI: -0.06 to -0.01; β = -0.04, CI: -0.06 to -0.001, respectively). CONCLUSIONS: Vascular-related models provide a better explanation to OPA variance in healthy individuals than in glaucoma patients. The variables that influence OPA seem to be different in healthy, POAG and NTG patients.