Qualitative Data from a Trial of Home Blood Pressure Telemonitoring and Pharmacist Management (Hyperlink)

Background/Aims: Hyperlink was a cluster-randomized intervention trial in HealthPartners clinics from 2009 to 2013 with nonintervention follow-up through 2015 (60 months). Participants had uncontrolled hypertension. Telemonitoring intervention patients had improved blood pressure control at 6 months compared with usual care patients (72% vs. 45%, P \u3c 0.001). Intervention effects narrowed at 12 (72% vs. 53%, P = 0.005) and 18 months (72% vs. 57%, P = 0.003); 60-month blood pressure data will be complete in October 2015. We conducted a mixed-methods analysis combining our quantitative results with patient, clinical and other organizational stakeholder perspectives to learn how to optimize the intervention for the most patients and implement this intervention in our care setting. Methods: We collected three sources of qualitative data: seven patient focus groups stratified by 6–18-month blood pressure outcomes, four structured interviews with intervention pharmacists, and interviews (currently being collected) with key organizational stakeholders. Focus group and structured interview data were analyzed by a team of five using grounded theory. Initial themes were identified and coded in NVivo10. Results: Qualitative data revealed several initial themes. First, patients valued trust in the patient-provider relationship and good communication between providers. Second, patients have varying goals with medications and successfully initiating/adhering to treatment is better when provider understands and respects the patient’s perspective on medications. Finally, intervention patients benefited from seeing their own blood pressure data (reinforcement) and a trusted provider seeing their data (accountability). Pharmacist interviews agreed with these themes, revealing key insights about intervention design including: length of intervention, addressing relapse, and meeting individual patient’s needs with effective use of data and lifestyle counseling. Results of 60-month blood pressure outcomes will be analyzed in the context of these initial findings, and qualitative findings will be further refined. Stakeholder interview results about implementation are forthcoming. Conclusion: Findings suggest the need for several adaptations to the intervention before implementation in practice: provision of blood pressure monitors for ongoing use, a shorter duration with ability to re-engage if blood pressure becomes uncontrolled, more tailoring of the intervention to individual needs, and better communication and handoffs between pharmacists and physicians

SPARK: A US Cohort of 50,000 Families to Accelerate Autism Research

The Simons Foundation Autism Research Initiative (SFARI) has launched SPARKForAutism. org, a dynamic platform that is engaging thousands of individuals with autism spectrum disorder (ASD) and connecting them to researchers. By making all data accessible, SPARK seeks to increase our understanding of ASD and accelerate new supports and treatments for ASD

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