20-year depressive symptoms, dementia, and structural neuropathology in older women.

INTRODUCTION: The course of depressive symptoms and dementia risk is unclear, as are potential structural neuropathological common causes. METHODS: Utilizing joint latent class mixture models, we identified longitudinal trajectories of annually assessed depressive symptoms and dementia risk over 21 years in 957 older women (baseline age 72.7 years old) from the Women's Health Initiative Memory Study. In a subsample of 569 women who underwent structural magnetic resonance imaging, we examined whether estimates of cerebrovascular disease and Alzheimer's disease (AD)-related neurodegeneration were associated with identified trajectories. RESULTS: Five trajectories of depressive symptoms and dementia risk were identified. Compared to women with minimal symptoms, women who reported mild and stable and emerging depressive symptoms were at the highest risk of developing dementia and had more cerebrovascular disease and AD-related neurodegeneration. DISCUSSION: There are heterogeneous profiles of depressive symptoms and dementia risk. Common neuropathological factors may contribute to both depression and dementia. Highlights The progression of depressive symptoms and concurrent dementia risk is heterogeneous. Emerging depressive symptoms may be a prodromal symptom of dementia. Cerebrovascular disease and AD are potentially shared neuropathological factors.

Comparing data-driven and hypothesis-driven MRI-based predictors of cognitive impairment in individuals from the Atherosclerosis Risk in Communities (ARIC) study.

INTRODUCTION: A data-driven index of dementia risk based on magnetic resonance imaging (MRI), the Alzheimer's Disease Pattern Similarity (AD-PS) score, was estimated for participants in the Atherosclerosis Risk in Communities (ARIC) study. METHODS: AD-PS scores were generated for 839 cognitively non-impaired individuals with a mean follow-up of 4.86 years. The scores and a hypothesis-driven volumetric measure based on several brain regions susceptible to AD were compared as predictors of incident cognitive impairment in different settings. RESULTS: Logistic regression analyses suggest the data-driven AD-PS scores to be more predictive of incident cognitive impairment than its counterpart. Both biomarkers were more predictive of incident cognitive impairment in participants who were White, female, and apolipoprotein E gene (APOE) ε4 carriers. Random forest analyses including predictors from different domains ranked the AD-PS scores as the most relevant MRI predictor of cognitive impairment. CONCLUSIONS: Overall, the AD-PS scores were the stronger MRI-derived predictors of incident cognitive impairment in cognitively non-impaired individuals.

Particulate matter and episodic memory decline mediated by early neuroanatomic biomarkers of Alzheimer's disease.

Evidence suggests exposure to particulate matter with aerodynamic diameter <2.5 µm (PM2.5) may increase the risk for Alzheimer's disease and related dementias. Whether PM2.5 alters brain structure and accelerates the preclinical neuropsychological processes remains unknown. Early decline of episodic memory is detectable in preclinical Alzheimer's disease. Therefore, we conducted a longitudinal study to examine whether PM2.5 affects the episodic memory decline, and also explored the potential mediating role of increased neuroanatomic risk of Alzheimer's disease associated with exposure. Participants included older females (n = 998; aged 73-87) enrolled in both the Women's Health Initiative Study of Cognitive Aging and the Women's Health Initiative Memory Study of Magnetic Resonance Imaging, with annual (1999-2010) episodic memory assessment by the California Verbal Learning Test, including measures of immediate free recall/new learning (List A Trials 1-3; List B) and delayed free recall (short- and long-delay), and up to two brain scans (MRI-1: 2005-06; MRI-2: 2009-10). Subjects were assigned Alzheimer's disease pattern similarity scores (a brain-MRI measured neuroanatomical risk for Alzheimer's disease), developed by supervised machine learning and validated with data from the Alzheimer's Disease Neuroimaging Initiative. Based on residential histories and environmental data on air monitoring and simulated atmospheric chemistry, we used a spatiotemporal model to estimate 3-year average PM2.5 exposure preceding MRI-1. In multilevel structural equation models, PM2.5 was associated with greater declines in immediate recall and new learning, but no association was found with decline in delayed-recall or composite scores. For each interquartile increment (2.81 µg/m3) of PM2.5, the annual decline rate was significantly accelerated by 19.3% [95% confidence interval (CI) = 1.9% to 36.2%] for Trials 1-3 and 14.8% (4.4% to 24.9%) for List B performance, adjusting for multiple potential confounders. Long-term PM2.5 exposure was associated with increased Alzheimer's disease pattern similarity scores, which accounted for 22.6% (95% CI: 1% to 68.9%) and 10.7% (95% CI: 1.0% to 30.3%) of the total adverse PM2.5 effects on Trials 1-3 and List B, respectively. The observed associations remained after excluding incident cases of dementia and stroke during the follow-up, or further adjusting for small-vessel ischaemic disease volumes. Our findings illustrate the continuum of PM2.5 neurotoxicity that contributes to early decline of immediate free recall/new learning at the preclinical stage, which is mediated by progressive atrophy of grey matter indicative of increased Alzheimer's disease risk, independent of cerebrovascular damage.

Using high-dimensional machine learning methods to estimate an anatomical risk factor for Alzheimer's disease across imaging databases.

INTRODUCTION: The main goal of this work is to investigate the feasibility of estimating an anatomical index that can be used as an Alzheimer's disease (AD) risk factor in the Women's Health Initiative Magnetic Resonance Imaging Study (WHIMS-MRI) using MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a well-characterized imaging database of AD patients and cognitively normal subjects. We called this index AD Pattern Similarity (AD-PS) scores. To demonstrate the construct validity of the scores, we investigated their associations with several AD risk factors. The ADNI and WHIMS imaging databases were collected with different goals, populations and data acquisition protocols: it is important to demonstrate that the approach to estimating AD-PS scores can bridge these differences. METHODS: MRI data from both studies were processed using high-dimensional warping methods. High-dimensional classifiers were then estimated using the ADNI MRI data. Next, the classifiers were applied to baseline and follow-up WHIMS-MRI GM data to generate the GM AD-PS scores. To study the validity of the scores we investigated associations between GM AD-PS scores at baseline (Scan 1) and their longitudinal changes (Scan 2 -Scan 1) with: 1) age, cognitive scores, white matter small vessel ischemic disease (WM SVID) volume at baseline and 2) age, cognitive scores, WM SVID volume longitudinal changes respectively. In addition, we investigated their associations with time until classification of independently adjudicated status in WHIMS-MRI. RESULTS: Higher GM AD-PS scores from WHIMS-MRI baseline data were associated with older age, lower cognitive scores, and higher WM SVID volume. Longitudinal changes in GM AD-PS scores (Scan 2 - Scan 1) were also associated with age and changes in WM SVID volumes and cognitive test scores. Increases in the GM AD-PS scores predicted decreases in cognitive scores and increases in WM SVID volume. GM AD-PS scores and their longitudinal changes also were associated with time until classification of cognitive impairment. Finally, receiver operating characteristic curves showed that baseline GM AD-PS scores of cognitively normal participants carried information about future cognitive status determined during follow-up. DISCUSSION: We applied a high-dimensional machine learning approach to estimate a novel AD risk factor for WHIMS-MRI study participants using ADNI data. The GM AD-PS scores showed strong associations with incident cognitive impairment and cross-sectional and longitudinal associations with age, cognitive function, cognitive status and WM SVID volume lending support to the ongoing validation of the GM AD-PS score.

Cumulative Number of Treatment Interventions Predicts Health-Related Quality of Life in Patients with Critical Limb Ischemia.

BACKGROUND: Health-related quality of life (QOL) is usually assessed after a defined interval following a single intervention, but critical limb ischemia (CLI) is a chronic condition where multiple interventions are often required over a patient's lifetime. We hypothesized that the impact of CLI treatment interventions on QOL is diminished in the setting of multiple previous interventions. To test this hypothesis, we performed a cross-sectional study evaluating associations between cumulative number of previous peripheral artery disease (PAD) treatment interventions and QOL adjusting for both comorbidity and disease severity. METHODS: Participants with CLI (abnormal ankle brachial index [ABI] plus rest pain and/or tissue loss) were enrolled in a cross-sectional study and completed a disease-specific QOL assessment, (the Vascular Quality of Life Questionnaire-6 [VascuQol-6]). Minimum ABI was used to assess disease severity, and comorbidity was evaluated based on Charlson Comorbidity Index. Cumulative number of PAD treatment interventions was defined based on the lifelong total for both legs. QOL associations were evaluated using a multivariable linear regression model adjusted for age and gender. RESULTS: Thirty-two patients with CLI participated. Mean age was 63 ± 10 years, 72% were men, and 63% were white; mean ABI was 0.6 ± 0.2. Mean VQ-6 score was 11.6 ± 4.2, and QOL was lower in patients with more previous interventions. Multivariable models demonstrated that an increasing number of previous treatment interventions negatively impacted QOL (P = 0.047), whereas positive associations were identified for female gender (P = 0.006) and ABI (P = 0.006). No association between comorbidity and QOL was identified. CONCLUSIONS: Vascular-specific factors appear to be key determinants of QOL among patients with CLI, whereas comorbidity appears less important. Strategies focused on definitive and durable revascularization may reduce cumulative interventions and potentially maximize QOL for patients with CLI.

Development of a video-simulation instrument for assessing cognition in older adults.

BACKGROUND: Commonly used methods to assess cognition, such as direct observation, self-report, or neuropsychological testing, have significant limitations. Therefore, a novel tablet computer-based video simulation was created with the goal of being valid, reliable, and easy to administer. The design and implementation of the SIMBAC (Simulation-Based Assessment of Cognition) instrument is described in detail, as well as informatics "lessons learned" during development. RESULTS: The software emulates 5 common instrumental activities of daily living (IADLs) and scores participants' performance. The modules were chosen by a panel of geriatricians based on relevance to daily functioning and ability to be modeled electronically, and included facial recognition, pairing faces with the correct names, filling a pillbox, using an automated teller machine (ATM), and automatic renewal of a prescription using a telephone. Software development included three phases 1) a period of initial design and testing (alpha version), 2) pilot study with 10 cognitively normal and 10 cognitively impaired adults over the age of 60 (beta version), and 3) larger validation study with 162 older adults of mixed cognitive status (release version). Results of the pilot study are discussed in the context of refining the instrument; full results of the validation study are reported in a separate article. In both studies, SIMBAC reliably differentiated controls from persons with cognitive impairment, and performance was highly correlated with Mini Mental Status Examination (MMSE) score. Several informatics challenges emerged during software development, which are broadly relevant to the design and use of electronic assessment tools. Solutions to these issues, such as protection of subject privacy and safeguarding against data loss, are discussed in depth. Collection of fine-grained data (highly detailed information such as time spent reading directions and the number of taps on screen) is also considered. CONCLUSIONS: SIMBAC provides clinicians direct insight into whether subjects can successfully perform selected cognitively intensive activities essential for independent living and advances the field of cognitive assessment. Insight gained from the development process could inform other researchers who seek to develop software tools in health care.

Has the incidence of thoracolumbar spine injuries increased in the United States from 1998 to 2011?

BACKGROUND: While most motor vehicle crash (MVC)-related injuries have been decreasing, one study showed increases in MVC-related spinal fractures from 1994 to 2002 in Wisconsin. To our knowledge, no studies evaluating nationwide trends of MVC-related thoracolumbar spine injuries have been published. Such fractures can cause pain, loss of functionality or even death. If the incidence of such injuries is increasing, it may provide a motive for reassessment of current vehicle safety design. QUESTIONS/PURPOSES: We questioned whether the incidence of thoracolumbar spine injuries increased in the United States population with time (between 1998 and 2011), and if there was an increased incidence of thoracolumbar injuries, whether there were identifiable compensatory "trade-off injury" patterns, such as reductions in sacropelvic injuries. PATIENTS AND METHODS: Institutional review board approval was obtained for retrospective review of three national databases: the National Trauma Databank® (NTDB®), 2002-2006, National Automotive Sampling System (NASS), 2000-2011, and National Inpatient Sample (NIS), 1998-2007. In each database, the total number of MVC-related injuries and the number of MVC-related thoracolumbar injuries per year were identified using appropriate Abbreviated Injury Scale (AIS) or ICD-9 codes. Sacropelvic injuries also were identified to evaluate their potential as trade-off injuries. Poisson regression models adjusting for age were used to analyze trends in the data with time. RESULTS: All databases showed increases in MVC-related thoracolumbar spine injuries when adjusting for age with time. These age-adjusted relative annual percent increases ranged from 8.22% (95% CI, 5.77%-10.72%; p<0.001) using AIS of 2 or more (AIS2 +) injury codes in the NTDB®, 8.59% (95% CI, 5.88%-11.37%; p<0.001) using ICD-9 codes in the NTDB®, 8.12% (95% CI, 7.20%-9.06%; p<0.001) using ICD-9 codes in the NIS, and 8.10 % (95% CI 5.00%-11.28%; p<0.001) using AIS2+ injury codes in the NASS. As these thoracolumbar injuries have increased, there has been no consistent trend toward a compensatory reduction in terms of sacropelvic injuries. CONCLUSIONS: While other studies have shown that rates of many MVC-related injuries are declining with time, our data show increases in the incidence of thoracolumbar injury. Although more sensitive screening tools likely have resulted in earlier and increased recognition of these injuries, it cannot be stated for certain that this is the only driver of the increased incidence observed in this study. As seatbelt use has continued to increase, this trend may be the result of thoracolumbar injuries as trade-offs for other injuries, although in our study we did not see a compensatory decrease in sacropelvic injuries. Investigation evaluating the root of this pattern is warranted.

Association of CT-Derived Skeletal Muscle and Adipose Tissue Metrics with Frailty in Older Adults.

RATIONALE AND OBJECTIVES: Tools are needed for frailty screening of older adults. Opportunistic analysis of body composition could play a role. We aim to determine whether computed tomography (CT)-derived measurements of muscle and adipose tissue are associated with frailty. MATERIALS AND METHODS: Outpatients aged ≥ 55 years consecutively imaged with contrast-enhanced abdominopelvic CT over a 3-month interval were included. Frailty was determined from the electronic health record using a previously validated electronic frailty index (eFI). CT images at the level of the L3 vertebra were automatically segmented to derive muscle metrics (skeletal muscle area [SMA], skeletal muscle density [SMD], intermuscular adipose tissue [IMAT]) and adipose tissue metrics (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT]). Distributions of demographic and CT-derived variables were compared between sexes. Sex-specific associations of muscle and adipose tissue metrics with eFI were characterized by linear regressions adjusted for age, race, ethnicity, duration between imaging and eFI measurements, and imaging parameters. RESULTS: The cohort comprised 886 patients (449 women, 437 men, mean age 67.9 years), of whom 382 (43%) met the criteria for pre-frailty (ie, 0.10 < eFI ≤ 0.21) and 138 (16%) for frailty (eFI > 0.21). In men, 1 standard deviation changes in SMD (ß = -0.01, 95% confidence interval [CI], -0.02 to -0.001, P = .02) and VAT area (ß = 0.008, 95% CI, 0.0005-0.02, P = .04), but not SMA, IMAT, or SAT, were associated with higher frailty. In women, none of the CT-derived muscle or adipose tissue metrics were associated with frailty. CONCLUSION: We observed a positive association between frailty and CT-derived biomarkers of myosteatosis and visceral adiposity in a sex-dependent manner.

Associations of plasma proteomics and age-related outcomes with brain age in a diverse cohort.

Machine learning models are increasingly being used to estimate "brain age" from neuroimaging data. The gap between chronological age and the estimated brain age gap (BAG) is potentially a measure of accelerated and resilient brain aging. Brain age calculated in this fashion has been shown to be associated with mortality, measures of physical function, health, and disease. Here, we estimate the BAG using a voxel-based elastic net regression approach, and then, we investigate its associations with mortality, cognitive status, and measures of health and disease in participants from Atherosclerosis Risk in Communities (ARIC) study who had a brain MRI at visit 5 of the study. Finally, we used the SOMAscan assay containing 4877 proteins to examine the proteomic associations with the MRI-defined BAG. Among N = 1849 participants (age, 76.4 (SD 5.6)), we found that increased values of BAG were strongly associated with increased mortality and increased severity of the cognitive status. Strong associations with mortality persisted when the analyses were performed in cognitively normal participants. In addition, it was strongly associated with BMI, diabetes, measures of physical function, hypertension, prevalent heart disease, and stroke. Finally, we found 33 proteins associated with BAG after a correction for multiple comparisons. The top proteins with positive associations to brain age were growth/differentiation factor 15 (GDF-15), Sushi, von Willebrand factor type A, EGF, and pentraxin domain-containing protein 1 (SEVP 1), matrilysin (MMP7), ADAMTS-like protein 2 (ADAMTS), and heat shock 70 kDa protein 1B (HSPA1B) while EGF-receptor (EGFR), mast/stem-cell-growth-factor-receptor (KIT), coagulation-factor-VII, and cGMP-dependent-protein-kinase-1 (PRKG1) were negatively associated to brain age. Several of these proteins were previously associated with dementia in ARIC. These results suggest that circulating proteins implicated in biological aging, cellular senescence, angiogenesis, and coagulation are associated with a neuroimaging measure of brain aging.

Psychometric properties of a computerized adaptive test for assessing mobility in older adults using novel video-animation technology.

PURPOSE: This paper reports on the psychometric properties of a computerized adaptive test (CAT) version of the Mobility Assessment Tool (MAT) for older adults (MAT-CAT). METHODS: An item pool of 78 video-animation-based items for mobility was developed, and response data were collected from a sample of 234 participants aged 65-90 years. The video-animation-based instrument was designed to minimize ambiguity in the presentation of task demands. In addition to evaluating traditional psychometric properties including dimensionality, differential item functioning (DIF), and local dependence, we extensively tested the performance of several MAT-CAT measures and compared their performances with a fixed format. RESULTS: Operationally, the MAT-CAT was sufficiently unidimensional and had acceptable levels of local independence. One DIF item was removed. Most importantly, the CAT measures showed that even starting with a single fixed item at the mean ability, the adaptive version delivered better performance than the fixed format in terms of several criteria including the standard error of estimate. CONCLUSION: The MAT-CAT demonstrated satisfactory psychometric properties and superior performance to a fixed format. The video-animation-based adaptive instrument can be used for assessing mobility with specificity and precision.

Design and implementation of the mobility assessment tool: software description.

BACKGROUND: In previous work, we described the development of an 81-item video-animated tool for assessing mobility. In response to criticism levied during a pilot study of this tool, we sought to develop a new version built upon a flexible framework for designing and administering the instrument. RESULTS: Rather than constructing a self-contained software application with a hard-coded instrument, we designed an XML schema capable of describing a variety of psychometric instruments. The new version of our video-animated assessment tool was then defined fully within the context of a compliant XML document. Two software applications--one built in Java, the other in Objective-C for the Apple iPad--were then built that could present the instrument described in the XML document and collect participants' responses. Separating the instrument's definition from the software application implementing it allowed for rapid iteration and easy, reliable definition of variations. CONCLUSIONS: Defining instruments in a software-independent XML document simplifies the process of defining instruments and variations and allows a single instrument to be deployed on as many platforms as there are software applications capable of interpreting the instrument, thereby broadening the potential target audience for the instrument. Continued work will be done to further specify and refine this type of instrument specification with a focus on spurring adoption by researchers in gerontology and geriatric medicine.

Is an MRI-derived anatomical measure of dementia risk also a measure of brain aging?

Machine learning methods have been applied to estimate measures of brain aging from neuroimages. However, only rarely have these measures been examined in the context of biologic age. Here, we investigated associations of an MRI-based measure of dementia risk, the Alzheimer's disease pattern similarity (AD-PS) scores, with measures used to calculate biological age. Participants were those from visit 5 of the Atherosclerosis Risk in Communities Study with cognitive status adjudication, proteomic data, and AD-PS scores available. The AD-PS score estimation is based on previously reported machine learning methods. We evaluated associations of the AD-PS score with all-cause mortality. Sensitivity analyses using only cognitively normal (CN) individuals were performed treating CNS-related causes of death as competing risk. AD-PS score was examined in association with 32 proteins measured, using a Somalogic platform, previously reported to be associated with age. Finally, associations with a deficit accumulation index (DAI) based on a count of 38 health conditions were investigated. All analyses were adjusted for age, race, sex, education, smoking, hypertension, and diabetes. The AD-PS score was significantly associated with all-cause mortality and with levels of 9 of the 32 proteins. Growth/differentiation factor 15 (GDF-15) and pleiotrophin remained significant after accounting for multiple-testing and when restricting the analysis to CN participants. A linear regression model showed a significant association between DAI and AD-PS scores overall. While the AD-PS scores were created as a measure of dementia risk, our analyses suggest that they could also be capturing brain aging.

Age-based differences in the disability of spine injuries in pediatric and adult motor vehicle crash occupants.

OBJECTIVE: The objective was to develop a disability-based metric for quantifying disability rates as a result of motor vehicle crash (MVC) spine injuries and compare functional outcomes between pediatric and adult subgroups. METHODS: Disability rate was quantified using Functional Independence Measure (FIM) scores within the National Trauma Data Bank-Research Data System for the top 95% most frequent Abbreviated Injury Scale (AIS) 3 spine injuries (14 unique injuries). Pediatric (7-18 years), young adult (19-45 years), middle-aged adult (46-65 years), and older adult (66+ years) MVC occupants with FIM scores available and at least one of the 14 spine injuries were included. FIM scores of 1 or 2 at time of discharge were used to define disability and correspond to full functional or modified dependence in self-feeding, locomotion, and/or verbal expression. Disability rate was evaluated on a per injury basis for each AIS 3 spine injury and calculated as the proportion of cases associated with disability (i.e. FIM of 1 or 2) out of the total cases of that particular injury. Disability rates were calculated with and without the exclusion of cases with severe co-injuries (AIS 4+) to minimize bias from additional non-spinal injuries that could have contributed to disability. Associations between adjusted disability rates and existing mortality rates were investigated. RESULTS: Locomotion impairment alone was the most frequent disability type for the top 14 AIS 3 spine injuries (7 cervical, 4 thoracic, and 3 lumbar) across all age groups and spine regions. Adjusted and unadjusted disability rates ranged from 0-69%. Adjusted disability rates increased with age: 14.8 ± 10% (mean ± SD) in pediatrics to 16.2 ± 6.6% (young adults), 29.2 ± 10.9% (middle-aged adults), and 45.0 ± 12.2% (older adults). Among all adult populations, adjusted mortality and disability rates were positively correlated (R2>0.24), with disability rates consistently greater than corresponding mortality rates. CONCLUSIONS: Older adults had significantly greater disability rates associated with MVC spine injuries across all spinal regions. MVC disability rates for pediatrics were considerably lower. Overall, rates of mortality were significantly lower than rates of disability. The adjusted disability rates developed can supplement existing injury metrics by accounting for age- and location-specific functional implications of MVC spine injuries.

Advanced Automatic Crash Notification Algorithm for Children.

BACKGROUND: Advanced automatic crash notification (AACN) can improve triage decision-making by using vehicle telemetry to alert first responders of a motor vehicle crash and estimate an occupant's likelihood of injury. The objective was to develop an AACN algorithm to predict the risk that a pediatric occupant is seriously injured and requires treatment at a Level I or II trauma center. METHODS: Based on 3 injury facets (severity; time sensitivity; predictability), a list of Target Injuries associated with a child's need for Level I/II trauma center treatment was determined. Multivariable logistic regression of motor vehicle crash occupants was performed creating the pediatric-specific AACN algorithm to predict risk of sustaining a Target Injury. Algorithm inputs included: delta-v, rollover quarter-turns, belt status, multiple impacts, airbag deployment, and age. The algorithm was optimized to achieve under-triage ≤5% and over-triage ≤50%. Societal benefits were assessed by comparing correctly triaged motor vehicle crash occupants using the AACN algorithm against real-world decisions. RESULTS: The pediatric AACN algorithm achieved 25% to 49% over-triage across crash modes, and under-triage rates of 2% for far-side, 3% for frontal and near-side, 8% for rear, and 14% for rollover crashes. Applied to real-world motor vehicle crashes, improvements of 59% in under-triage and 45% in over-triage are estimated: more appropriate triage of 32,320 pediatric occupants annually. CONCLUSIONS: This AACN algorithm accounts for pediatric developmental stage and will aid emergency personnel in correctly triaging pediatric occupants after a motor vehicle crash. Once incorporated into the trauma triage network, it will increase triage efficiency and improve patient outcomes.

Automated Muscle Measurement on Chest CT Predicts All-Cause Mortality in Older Adults From the National Lung Screening Trial.

BACKGROUND: Muscle metrics derived from computed tomography (CT) are associated with adverse health events in older persons, but obtaining these metrics using current methods is not practical for large datasets. We developed a fully automated method for muscle measurement on CT images. This study aimed to determine the relationship between muscle measurements on CT with survival in a large multicenter trial of older adults. METHOD: The relationship between baseline paraspinous skeletal muscle area (SMA) and skeletal muscle density (SMD) and survival over 6 years was determined in 6,803 men and 4,558 women (baseline age: 60-69 years) in the National Lung Screening Trial (NLST). The automated machine learning pipeline selected appropriate CT series, chose a single image at T12, and segmented left paraspinous muscle, recording cross-sectional area and density. Associations between SMA and SMD with all-cause mortality were determined using sex-stratified Cox proportional hazards models, adjusted for age, race, height, weight, pack-years of smoking, and presence of diabetes, chronic lung disease, cardiovascular disease, and cancer at enrollment. RESULTS: After a mean 6.44 ± 1.06 years of follow-up, 635 (9.33%) men and 265 (5.81%) women died. In men, higher SMA and SMD were associated with a lower risk of all-cause mortality, in fully adjusted models. A one-unit standard deviation increase was associated with a hazard ratio (HR) = 0.85 (95% confidence interval [CI] = 0.79, 0.91; p < .001) for SMA and HR = 0.91 (95% CI = 0.84, 0.98; p = .012) for SMD. In women, the associations did not reach significance. CONCLUSION: Higher paraspinous SMA and SMD, automatically derived from CT exams, were associated with better survival in a large multicenter cohort of community-dwelling older men.

Comprehensive and Computable Molecular Diagnostic Panel (C2Dx) From Small Volume Specimens for Precision Oncology: Molecular Subtyping of Non-Small Cell Lung Cancer From Fine Needle Aspirates.

The Comprehensive, Computable NanoString Diagnostic gene panel (C2Dx) is a promising solution to address the need for a molecular pathological research and diagnostic tool for precision oncology utilizing small volume tumor specimens. We translate subtyping-related gene expression patterns of Non-Small Cell Lung Cancer (NSCLC) derived from public transcriptomic data which establish a highly robust and accurate subtyping system. The C2Dx demonstrates supreme performance on the NanoString platform using microgram-level FNA samples and has excellent portability to frozen tissues and RNA-Seq transcriptomic data. This workflow shows great potential for research and the clinical practice of cancer molecular diagnosis.

Injury risk curves in far-side lateral motor vehicle crashes by AIS level, body region and injury code.

OBJECTIVE: The objective of this study was to develop injury risk curves as a function of change in vehicle velocity for occupants in far-side lateral motor vehicle crashes (MVCs) by AIS level, body region, and specific AIS codes that commonly occur in this crash mode. METHODS: The National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) years 2000-2015 database was queried, resulting in 4,495 non-weighted far-side crashes. For each case, occupant age, sex, and the following metadata were collected: vehicle model year, vehicle body type, lateral delta-v, normalized PDOF, multiple impacts, belt use, seat position, object contacted, striking vehicle body type, maximum crush extent and side airbag deployment. Multivariable logistic regression was used to develop risk curves for AIS 2+ through 5+ injuries, AIS 2+ injuries by body region (head, thorax, lower extremity), and for each of the 10 most frequent far-side AIS 2+ injuries. Significant covariates were determined by backwards elimination (p < 0.05). The full dataset and a subsampled dataset of only cases with side airbag deployment were used to develop risk curves. RESULTS: For AIS 2+ through 5+ injury, greater delta-V was associated with greater injury risk (OR's: 2.48-3.66 per 11.9 kph increase) and belt use was associated with lower risk (OR's: 0.04-0.36 compared to unbelted). Multiple impacts were significant predictors of increased AIS 3+, 4+ and 5+ injury risk (OR's: 2.56, 2.27 and 2.83 compared to single impact). For AIS 2+ body region injuries, lateral delta-V and maximum CDC extent were positively associated with increased head, thorax and lower extremity injury risk while belt use was associated with lower risk. Increased lateral delta-v, unbelted status, and greater maximum CDC extent frequently increased injury risk for the most common far-side injuries. Side airbag deployment was not a significant covariate for the injury risk models. CONCLUSIONS: The resulting risk models expand upon previous literature gaps to provide a more comprehensive view of contributors to injury risk for occupants in far-side MVCs. This study yields risk curves based on the latest available NASS-CDS data.

PM2.5 associated with gray matter atrophy reflecting increased Alzheimers risk in older women.

OBJECTIVE: To examine whether late-life exposure to PM2.5 (particulate matter with aerodynamic diameters <2.5-µm) contributes to progressive brain atrophy predictive of Alzheimer's disease (AD) using a community-dwelling cohort of women (aged 70-89) with up to two brain MRI scans (MRI-1: 2005-6; MRI-2: 2010-11). METHODS: AD pattern similarity (AD-PS) scores, developed by supervised machine learning and validated with MRI data from the AD Neuroimaging Initiative, was used to capture high-dimensional gray matter atrophy in brain areas vulnerable to AD (e.g., amygdala, hippocampus, parahippocampal gyrus, thalamus, inferior temporal lobe areas and midbrain). Based on participants' addresses and air monitoring data, we implemented a spatiotemporal model to estimate 3-year average exposure to PM2.5 preceding MRI-1. General linear models were used to examine the association between PM2.5 and AD-PS scores (baseline and 5-year standardized change), accounting for potential confounders and white matter lesion volumes. RESULTS: For 1365 women aged 77.9±3.7 years in 2005-6, there was no association between PM2.5 and baseline AD-PS score in cross-sectional analyses (ß=-0.004; 95% CI: -0.019, 0.011). Longitudinally, each interquartile range increase of PM2.5 (2.82-µg/m3) was associated with increased AD-PS scores during the follow-up, equivalent to a 24% (hazard ratio=1.24; 95% CI: 1.14, 1.34) increase in AD risk over 5-years (n=712; aged 77.4±3.5 years). This association remained after adjustment for socio-demographics, intracranial volume, lifestyle, clinical characteristics, and white matter lesions, and was present with levels below US regulatory standards (<12-µg/m3). CONCLUSIONS: Late-life exposure to PM2.5 is associated with increased neuroanatomical risk of AD, which may not be explained by available indicators of cerebrovascular damage.

Age-based differences in the disability of extremity injuries in pediatric and adult occupants.

Objective: The objective was to develop a disability-based metric for motor vehicle crash (MVC) upper and lower extremity injuries and compare functional outcomes between children and adults.Methods: Disability risk (DR) was quantified using Functional Independence Measure (FIM) scores within the National Trauma Data Bank-Research Data System for the top 95% most frequently occurring Abbreviated Injury Scale (AIS) 3 extremity injuries (22 unique injuries). Pediatric (7-18 years), young adult (19-45 years), middle-aged (46-65 years), and older adult (66+ years) MVC occupants with an FIM score and at least one of the 22 extremity injuries were included. DR was calculated for each injury as the proportion of occupants who were disabled of those sustaining the injury. A maximum AIS-adjusted disability risk (DRMAIS) was also calculated for each injury, excluding occupants with AIS 4+ co-injuries.Results: Locomotion impairment was the most frequent disability type across all ages. DR and DRMAIS of the extremity injuries ranged from 0.06 to 1.00 (6%-100% disability risk). Disability risk increased with age, with DRMAIS increasing from 25.9% ± 8.6% (mean ± SD) in pediatric subjects to 30.4% ± 6.3% in young adults, 39.5% ± 6.6% in middle-aged adults, and 60.5 ± 13.3% in older adults. DRMAIS for upper extremity fractures differed significantly between age groups, with higher disability in older adults, followed by middle-aged adults. DRMAIS for pelvis, hip, shaft, knee, and other lower extremity fractures differed significantly between age groups, with older adult DRMAIS being significantly higher for each fracture type. DRMAIS for hip and lower extremity shaft fractures was also significantly higher in middle-aged occupants compared to pediatric and young adult occupants. The maximum AIS-adjusted mortality risk (MRMAIS, proportion of fatalities among occupants sustaining an MAIS 3 injury) was not correlated with DRMAIS for extremity injuries in pediatric, young adult, middle-aged, and older adult occupants (all R2 < 0.01). Disability associated with each extremity injury was higher than mortality risk.Conclusions: Older adults had significantly greater disability for MVC extremity injuries. Lower disability rates in children may stem from their increased physiological capacity for bone healing and relative lack of bone disease. The disability metrics developed can supplement AIS and other severity-based metrics by accounting for the age-specific functional implications of MVC extremity injuries.

Machine Learning for Automatic Paraspinous Muscle Area and Attenuation Measures on Low-Dose Chest CT Scans.

RATIONALE AND OBJECTIVES: To develop and evaluate an automated machine learning (ML) algorithm for segmenting the paraspinous muscles on chest computed tomography (CT) scans to evaluate for presence of sarcopenia. MATERIALS AND METHODS: A convolutional neural network based on the U-Net architecture was trained to perform muscle segmentation on a dataset of 1875 single slice CT images and was tested on 209 CT images of participants in the National Lung Screening Trial. Low-dose, noncontrast CT examinations were obtained at 33 clinical sites, using scanners from four manufacturers. The study participants had a mean age of 71.6 years (range, 70-74 years). Ground truth was obtained by manually segmenting the left paraspinous muscle at the level of the T12 vertebra. Muscle cross-sectional area (CSA) and muscle attenuation (MA) were recorded. Comparison between the ML algorithm and ground truth measures of muscle CSA and MA were obtained using Dice similarity coefficients and Pearson correlations. RESULTS: Compared to ground truth segmentation, the ML algorithm achieved median (standard deviation) Dice scores of 0.94 (0.04) in the test set. Mean (SD) muscle CSA was 14.3 (3.6) cm2 for ground truth and 13.7 (3.5) cm2 for ML segmentation. Mean (SD) MA was 41.6 (7.6) Hounsfield units (HU) for ground truth and 43.5 (7.9) HU for ML segmentation. There was high correlation between ML algorithm and ground truth for muscle CSA (r2 = 0.86; p < 0.0001) and MA (r2 = 0.95; p < 0.0001). CONCLUSION: The ML algorithm for measurement of paraspinous muscles compared favorably to manual ground truth measurements in the NLST. The algorithm generalized well to a heterogeneous set of low-dose CT images and may be capable of automated quantification of muscle metrics to screen for sarcopenia on routine chest CT examinations.