Prevalence and factors associated with general anxiety symptoms severity among older adults registered with the Primary Healthcare Corporation during the COVID-19 pandemic: A cross-sectional study.

BACKGROUND: Primary care-based studies examining the prevalence of anxiety symptoms severity and associated factors among older adults during the COVID-19 pandemic are scarce. The study aims to determine the prevalence of general anxiety symptoms severity and associated sociodemographic and physical health characteristics, including SARS-CoV-2 infection history, among older adults in primary care in Qatar during the COVID-19 pandemic. METHODS: A cross-sectional study was conducted using a random sample of older adults aged 60 years and above (n = 337) from all primary health care centers (n = 28) of Qatar's Primary Health Care Corporation. Participants were interviewed via telephone by family physicians between June and August 2020. General anxiety symptoms severity was assessed using the Generalized Anxiety Disorder 7-item Scale (GAD-7). Descriptive statistics and ordinal regression were used to analyse the data. RESULTS: The mean age of participants was 65 years (ranging from 60 to 89 years), standard deviation = 4.8. About 49.0% and 32.0% of participants were females and of Qatari nationality, respectively. The prevalence of minimal, mild, moderate, and severe general anxiety symptoms was 82.5%, 13.9%, 3.0%, and 0.6%, respectively. Around 33.5%, 63.5%, and 3.0% of participants had unknown, negative, or positive SARS-CoV-2 infection histories, respectively. Females had greater odds of higher levels of anxiety symptoms severity (odds ratio (OR) 2.34; 95% confidence interval (CI) 1.22, 4.50; p = 0.011). As compared to participants with unknown SARS-CoV-2 infection status, those with a negative and positive SARS-CoV-2 infection history had increased odds of higher levels of general anxiety symptoms severity by 2.48 (95% CI 1.17, 5.24; p = 0.017) and 7.21 (95% CI 1.67, 31.25; p = 0.008), respectively. Age, marital status, living arrangements, nationality, and the number of medical conditions had no statistically significant associations with general anxiety symptoms severity. CONCLUSIONS: Most older adults experience minimal to mild anxiety symptoms during the COVID-19 pandemic. Female gender and confirmed or suspected SARS-CoV-2 infection history are independent predictors of more severe anxiety symptoms among older adults.

Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation.

Background: Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested the use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision-making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care. Methods: We collected retrospective data from patients seen at Ohio State University, University of Mississippi, Barretos Cancer Hospital (Brazil), and FLENI (Argentina) who were managed for glioblastoma-amounting to 581 patient records documented using REDCap. Patients were evaluated using an unsupervised machine learning approach comprised of dimensionality reduction and eigenvector analysis to visualize the inter-relationship of collected clinical features. Results: We discovered that the serum white blood cell (WBC) count of a patient during baseline planning for treatment was predictive of overall survival with an over 6-month median survival difference between the upper and lower quartiles of WBC count. By utilizing an objective PD-L1 immunohistochemistry quantification algorithm, we were further able to identify an increase in PD-L1 expression in glioblastoma patients with high serum WBC counts. Conclusions: These findings suggest that in a subset of glioblastoma patients the incorporation of WBC count and PD-L1 expression in the brain tumor biopsy as simple biomarkers predicting glioblastoma patient survival. Moreover, machine learning models allow the distillation of complex clinical data sets to uncover novel and meaningful clinical relationships.

Unsupervised machine learning models reveal predictive markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation.

Purpose: Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care. Methods: We collected retrospective data from patients seen at Ohio State University, University of Mississippi, Barretos Cancer Hospital (Brazil), and FLENI (Argentina) who were managed for glioblastoma-amounting to nearly 600 patient records documented using REDCap. Patients were evaluated using an unsupervised machine learning approach comprised of dimensionality reduction and eigenvector analysis to visualize the inter-relationship of collected clinical features. Results: We discovered that white blood cell count of a patient during baseline planning for treatment was predictive of overall survival with an over 6-month median survival difference between the upper and lower quartiles of white blood cell count. By utilizing an objective PDL-1 immunohistochemistry quantification algorithm, we were further able to identify an increase in PDL-1 expression in glioblastoma patients with high white blood cell counts. Conclusion: These findings suggest that in a subset of glioblastoma patients the incorporation of white blood cell count and PDL-1 expression in the brain tumor biopsy as simple biomarkers predicting glioblastoma patient survival. Moreover, use of machine learning models allows us to visualize complex clinical datasets to uncover novel clinical relationships.