Depression treatment in individuals with cancer: a comparative analysis with cardio-metabolic conditions

A clear picture of the current state of nationwide depression treatment practices in individuals with cancer and depression does not exist in the United States (US). Therefore, the primary objective of this study was to examine rates of any depression treatment among individuals with cancer and depression in the US. To better understand the relationship between any treatment for depression and presence of cancer, we used a comparison group of individuals with cardio-metabolic conditions owing to the similar challenges faced in management of depression in individuals with these conditions. We used a retrospective cross-sectional design and data from multiple years of the Medical Expenditure Panel Survey, a nationally representative household-survey on healthcare utilization and expenditures. Study sample consisted of adults aged 21 or older with self-reported depression and cancer (n=528) or self-reported depression and diabetes, heart disease or hypertension (n=1643). Depression treatment comprised of any use of antidepres- sants and/or any use of mental health counseling services. Treatment rates for depression were 78.0% and 81.7% among individuals with cancer and cardio-metabolic conditions respectively. After controlling for socio-demographic, access-to-care, number of physician-visits, health-status, and lifestyle risk-factors related variables; individuals with cancer were less likely to report any treatment for depression (Adjusted Odds Ratio=0.67; 95% Confidence Interval=0.49, 0.92) compared to individuals with cardio-metabolic conditions (P≤0.01). Our findings highlight the possibility that competing demands may crowd out treatment for depression and that cancer diagnosis may be a barrier to depression treatment

Geographic variations in lipid-lowering therapy utilization, LDL-C levels, and proportion retrospectively meeting the ACC/AHA very high-risk criteria in a real-world population of patients with major atherosclerotic cardiovascular disease events in the United States

Objective: We assessed national- and state-level geographic variations among patients with a history of ≥1 major atherosclerotic cardiovascular disease (ASCVD) event in: (1) the proportion of patients with retrospectively identified 2018 American College of Cardiology/American Heart Association guideline very high-risk (VHR) ASCVD criteria; (2) utilization of guideline-directed lipid-lowering therapy (LLT); and (3) the proportion of patients with persistent low-density lipoprotein cholesterol (LDL-C) elevations despite statin and/or ezetimibe use. Methods: A retrospective cohort study using the Prognos LDL-C database linked to IQVIA longitudinal medical and prescription claims databases. The study period was from January 01, 2011, to November 30, 2019 and the index period was from January 01, 2016, to November 30, 2019; the index date was defined as the most recent LDL-C test during the index period. The study included patients aged ≥18 years at index who had a measured LDL-C level during the index period and had ≥1 inpatient/outpatient claim for ASCVD during the 5-year pre-index period. Results: Of patients with any ASCVD (N=4652,468), 1537,514 (33.1%) patients had ≥1 major ASCVD event. Among patients with ≥1 major ASCVD event, the VHR ASCVD criteria were retrospectively identified in 1139,018 (74.1%) patients; Hawaii had the highest (81.7%) and Colorado the lowest (65.0%) proportion of these patients. Nationally, 48.8% and 50.2% of patients with ≥1 major ASCVD event and retrospectively identified VHR ASCVD criteria, respectively, had current LLT use; Massachusetts and Colorado had the highest and lowest proportions, respectively. After standardizing for age and sex, 57.3% and 58.8% of patients with ≥1 major ASCVD event and retrospectively identified VHR ASCVD criteria, respectively, had LDL-C ≥70 mg/dL (≥1.8 mmol/L) despite statin and/or ezetimibe use, with substantial state-level variations observed. Conclusions: The study highlights high rates of elevated LDL-C and pervasive underuse of LLT in health-insured patients with a history of major ASCVD events treated in the United States, with state-level geographic variations observed

Patient characteristics and acute cardiovascular event rates among patients with very high-risk and non-very high-risk atherosclerotic cardiovascular disease.

BackgroundThe risk for subsequent major cardiovascular (CV) events among patients with very high-risk (VHR) atherosclerotic CV disease (ASCVD) remains to be fully elucidated.HypothesisWe assessed the characteristics and major CV event rates of patients with VHR versus non-VHR ASCVD in a real-world setting in the United States (US), hypothesizing that patients with VHR ASCVD would have higher CV event rates.MethodsThis was a retrospective cohort study conducted from January 01, 2011, to June 30, 2018, in the US using the Prognos LDL-C database linked to the IQVIA PharMetrics Plus® database supplemented with the IQVIA prescription claims (Dx/LRx) databases. Patients were ≥18 years old and had  ≥2 non-ancillary medical claims in the linked databases at least 30 days apart. The study was conducted in 2 stages: (1) identification of patients with ASCVD who met the definition of VHR ASCVD and a matched cohort of non-VHR ASCVD patients using the incidence density sampling (IDS) approach; (2) estimation of the occurrence of major CV events.ResultsAmong patients with ≥1 major ASCVD event (N=147,679), most qualified as VHR ASCVD (79.5%). There were 115,460 patients each in IDS-matched VHR and non-VHR ASCVD cohorts. The composite myocardial infarction/ischemic stroke event rates in the VHR and non-VHR ASCVD cohorts were 8.04 (95% confidence interval [95% CI]: 7.87-8.22) and 0.82 (95% CI: 0.77-0.88) events per 100 patient-years, respectively, during the 1-year post-index period.ConclusionsMost patients with ≥1 previous major ASCVD event treated in real-world US clinical practice qualified as VHR ASCVD. Patients with VHR ASCVD had much higher rates of major CV events versus non-VHR ASCVD patients

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset