Beaumont Health

About: Beaumont Health is a nonprofit organization based out in Royal Oak, Michigan, United States. It is known for research contribution in the topics: Medicine & Population. The organization has 1483 authors who have published 1448 publications receiving 15407 citations. The organization is also known as: William Beaumont Health System & Beaumont Hospitals.

Improving Physical Health in Patients With Chronic Mental Disorders: Twelve-Month Results From a Randomized Controlled Collaborative Care Trial

Abstract: Objective Persons with chronic mental disorders are disproportionately burdened with physical health conditions. We determined whether Life Goals Collaborative Care compared to usual care improves physical health in patients with mental disorders within 12 months. Methods This single-blind randomized controlled effectiveness study of a collaborative care model was conducted at a midwestern Veterans Affairs urban outpatient mental health clinic. Patients (N = 293 out of 474 eligible approached) with an ICD-9-CM diagnosis of schizophrenia, bipolar disorder, or major depressive disorder and at least 1 cardiovascular disease risk factor provided informed consent and were randomized (February 24, 2010, to April 29, 2015) to Life Goals (n = 146) or usual care (n = 147). A total of 287 completed baseline assessments, and 245 completed 12-month follow-up assessments. Life Goals included 5 weekly sessions that provided semistructured guidance on managing physical and mental health symptoms through healthy behavior changes, augmented by ongoing care coordination. The primary outcome was change in physical health-related quality of life score (Veterans RAND 12-item Short Form Health Survey [VR-12] physical health component score). Secondary outcomes included control of cardiovascular risk factors from baseline to 12 months (blood pressure, lipids, weight), mental health-related quality of life, and mental health symptoms. Results Among patients completing baseline and 12-month outcomes assessments (N = 245), the mean age was 55.3 years (SD = 10.8; range, 25-78 years), and 15.4% were female. Intent-to-treat analysis revealed that compared to those in usual care, patients randomized to Life Goals had slightly increased VR-12 physical health scores (coefficient = 3.21; P = .01). Conclusions Patients with chronic mental disorders and cardiovascular disease risk who received Life Goals had improved physical health-related quality of life. Trial registration ClinicalTrials.gov identifiers: NCT01487668 and NCT01244854.

The numerical stability of transformation-based CT ventilation.

Abstract: Computed tomography (CT)-derived ventilation imaging utilizes deformable image registration (DIR) to recover respiratory-induced tissue volume changes from inhale/exhale 4DCT phases. While current strategies for validating CT ventilation rely on analyzing its correlation with existing functional imaging modalities, the numerical stability of the CT ventilation calculation has not been characterized. The purpose of this study is to examine how small changes in the DIR displacement field can affect the calculation of transformation-based CT ventilation. First, we derive a mathematical theorem, which states that the change in ventilation metric induced by a perturbation to single displacement vector is bounded by the perturbation magnitude. Second, we introduce a novel Jacobian constrained optimization method for computing user-defined CT ventilation images. Using the Jacobian constrained method, we demonstrate that for the same inhale/exhale CT pair, it is possible to compute two DIR transformations that have similar spatial accuracies, but generate ventilation images with significantly different physical characteristics. In particular, we compute a CT ventilation image that perfectly correlates with a single-photon emission CT perfusion scan. The analysis and experiments indicate that while transformation-based CT ventilation is a promising modality, small changes in the DIR displacement field can result in large relative changes in the ventilation image. As such, approaches for improving the reproducibility of CT ventilation are still needed.

Stereotactic Radiosurgery for Brainstem Arteriovenous Malformations: A Multicenter Study.

Abstract: Background The management of brainstem arteriovenous malformations (bAVMs) is a formidable challenge. bAVMs harbor higher morbidity and mortality compared to other locations. Objective To review the outcomes following stereotactic radiosurgery (SRS) of bAVMs in a multicenter study. Methods Six medical centers contributed data from 205 patients through the International Gamma Knife Research Foundation. Median age was 32 yr (6-81). Median nidus volume was 1.4 mL (0.1-69 mL). Favorable outcome (FO) was defined as AVM obliteration and no post-treatment hemorrhage or permanent symptomatic radiation-induced complications. Results Overall obliteration was reported in 65.4% (n = 134) at a mean follow-up of 69 mo. Obliteration was angiographically proven in 53.2% (n = 109) and on MRA in 12.2% (n = 25). Actuarial rate of obliteration at 2, 3, 5, 7, and 10 yr after SRS was 24.5%, 43.3%, 62.3%, 73%, and 81.8% respectively. Patients treated with a margin dose >20 Gy were more likely to achieve obliteration (P = .001). Obliteration occurred earlier in patients who received a higher prescribed margin dose (P = .05) and maximum dose (P = .041). Post-SRS hemorrhage occurred in 8.8% (n = 18). Annual postgamma knife latency period hemorrhage was 1.5%. Radiation-induced complications were radiologically evident in 35.6% (n = 73), symptomatic in 14.6% (n = 30), and permanent in 14.6% (n = 30, which included long-tract signs and new cranial nerve deficits). FO was achieved in 64.4% (n = 132). Predictors of an FO were a higher Virginia radiosurgery AVM scale score (P = .003), prior hemorrhage (P = .045), and a lower prescribed maximum dose (P = .006). Conclusion SRS for bAVMs results in obliteration and avoids permanent complications in the majority of patients.

Functional Data Analysis of Tree Data Objects

Abstract: Data analysis on non-Euclidean spaces, such as tree spaces, can be challenging The main contribution of this article is establishment of a connection between tree-data spaces and the well-developed area of functional data analysis (FDA), where the data objects are curves This connection comes through two tree representation approaches, the Dyck path representation and the branch length representation These representations of trees in the Euclidean spaces enable us to exploit the power of FDA to explore statistical properties of tree data objects A major challenge in the analysis is the sparsity of tree branches in a sample of trees We overcome this issue by using a tree-pruning technique that focuses the analysis on important underlying population structures This method parallels scale-space analysis in the sense that it reveals statistical properties of tree-structured data over a range of scales The effectiveness of these new approaches is demonstrated by some novel results obtained in the analys