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In Finland, the frequency of isolated cleft palate (CP) is higher than that of isolated cleft lip with or without cleft palate (CL/P). This trend contrasts to that in other European countries but its genetic underpinnings are unknown. We conducted a genome-wide association study in the Finnish population and identified rs570516915, a single nucleotide polymorphism highly enriched in Finns, as strongly associated with CP (P = 5.25 × 10-34, OR = 8.65, 95% CI 6.11-12.25), but not with CL/P (P = 7.2 × 10-5), with genome-wide significance. The risk allele frequency of rs570516915 parallels the regional variation of CP prevalence in Finland, and the association was replicated in independent cohorts of CP cases from Finland (P = 8.82 × 10-28) and Estonia (P = 1.25 × 10-5). The risk allele of rs570516915 alters a conserved binding site for the transcription factor IRF6 within an enhancer (MCS-9.7) upstream of the IRF6 gene and diminishes the enhancer activity. Oral epithelial cells derived from CRISPR-Cas9 edited induced pluripotent stem cells demonstrate that the CP-associated allele of rs570516915 concomitantly decreases the binding of IRF6 and the expression level of IRF6, suggesting impaired IRF6 autoregulation as a molecular mechanism underlying the risk for CP.

IntroductionWe examined whether sex modifies the association between APOE ε2 and cognitive decline in two independent samples.MethodsWe used observational data from cognitively unimpaired non-Hispanic White (NHW) and non-Hispanic Black (NHB) adults. Linear mixed models examined interactive associations of APOE genotype (ε2 or ε4 carrier vs. ε3/ε3) and sex on cognitive decline in NHW and NHB participants separately.ResultsIn both Sample 1 (N = 9766) and Sample 2 (N = 915), sex modified the association between APOE ε2 and cognitive decline in NHW participants. Specifically, relative to APOE ε3/ε3, APOE ε2 protected against cognitive decline in men but not women. Among APOE ε2 carriers, men had slower decline than women. Among APOE ε3/ε3 carriers, cognitive trajectories did not differ between sexes. There were no sex-specific associations of APOE ε2 with cognition in NHB participants (N = 2010).DiscussionIn NHW adults, APOE ε2 may protect men but not women against cognitive decline.HighlightsWe studied sex-specific apolipoprotein E (APOE) ε2 effects on cognitive decline. In non-Hispanic White (NHW) adults, APOE ε2 selectively protects men against decline. Among men, APOE ε2 was more protective than APOE ε3/ε3. In women, APOE ε2 was no more protective than APOE ε3/ε3. Among APOE ε2 carriers, men had slower decline than women. There were no sex-specific APOE ε2 effects in non-Hispanic Black (NHB) adults.

IntroductionThe apolipoprotein E (APOE) genotype is a driver of cognitive decline and dementia. We used causal mediation methods to characterize pathways linking the APOE genotype to late-life cognition through Alzheimer's disease (AD) and non-AD neuropathologies.MethodsWe analyzed autopsy data from 1671 individuals from the Religious Orders Study, Memory and Aging Project, and Minority Aging Research Study (ROS/MAP/MARS) studies with cognitive assessment within 5 years of death and autopsy measures of AD (amyloid beta (Aβ), neurofibrillary tangles), vascular (athero/arteriolo-sclerosis, micro-infarcts/macro-infarcts), and non-AD neurodegenerative neuropathologies (TAR DNA protein 43 [TDP-43], Lewy bodies, amyloid angiopathy, hippocampal sclerosis).ResultsThe detrimental effect of APOE ε4 on cognition was mediated by summary measures of AD and non-AD neurodegenerative neuropathologies but not vascular neuropathologies; effects were strongest in individuals with dementia. The protective effect of APOE ε2 was partly mediated by AD neuropathology and stronger in women than in men.DiscussionThe APOE genotype influences cognition and dementia through multiple neuropathological pathways, with implications for different therapeutic strategies targeting people at increased risk for dementia.HighlightsBoth apolipoprotein E (APOE) ε2 and APOE ε4 effects on late-life cognition are mediated by AD neuropathology. The estimated mediated effects of most measures of AD neuropathology were similar. Non-Alzheimer's disease (AD) neurodegenerative pathologies mediate the effect of ε4 independently from AD. Non-AD vascular pathologies did not mediate the effect of the APOE genotype on cognition. The protective effect of APOE ε2 on cognition was stronger in women.

BackgroundClinical data collection related to prostate cancer (PCa) care is often unstructured or heterogeneous among providers, resulting in a high risk for ambiguity in its meaning when sharing or analyzing data. Ontologies, which are shareable formal (i.e., computable) representations of knowledge, can address these challenges by enabling machine-readable semantic interoperability. The purpose of this study was to identify PCa-specific key data elements (KDEs) for standardization in clinic and research.MethodsA modified Delphi method using iterative online surveys was performed to report a consensus agreement on KDEs by a multidisciplinary panel of 39 PCa specialists. Data elements were divided into three themes in PCa and included (1) treatment-related toxicities (TRT), (2) patient-reported outcome measures (PROM), and (3) disease control metrics (DCM).ResultsThe panel reached consensus on a thirty-item, two-tiered list of KDEs focusing mainly on urinary and rectal symptoms. The Expanded Prostate Cancer Index Composite (EPIC-26) questionnaire was considered most robust for PROM multi-domain monitoring, and granular KDEs were defined for DCM.ConclusionsThis expert consensus on PCa-specific KDEs has served as a foundation for a professional society-endorsed, publicly available operational ontology developed by the American Association of Physicists in Medicine (AAPM) Big Data Sub Committee (BDSC).

PurposeThe ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), Radiation Oncology Information Systems (ROIS), treatment planning systems (TPSs), and other cancer care and outcomes databases. The effort sought to create a standardized ontology for clinical data, social determinants of health (SDOH), and other radiation oncology concepts and interrelationships.Methods and materialsThe American Association of Physicists in Medicine's (AAPM's) Big Data Science Committee (BDSC) was initiated July of 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra- institutional databases from EHRs. The BDSC adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community.ResultsWe developed the Operational Ontology for Oncology (O3) which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, or the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to four constituencies: device manufacturers, centers of clinical care, researchers, and professional societies.ConclusionsO3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used creating large, representative, findable, accessible, interoperable and reusable (FAIR) datasets supporting the scientific objectives of grant programs. The construction of comprehensive "real world" datasets and application of advanced analytic techniques, including artificial intelligence (AI), holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative datasets.

ObjectivesClinical artificial intelligence and machine learning (ML) face barriers related to implementation and trust. There have been few prospective opportunities to evaluate these concerns. System for High Intensity EvaLuation During Radiotherapy (NCT03775265) was a randomised controlled study demonstrating that ML accurately directed clinical evaluations to reduce acute care during cancer radiotherapy. We characterised subsequent perceptions and barriers to implementation.MethodsAn anonymous 7-question Likert-type scale survey with optional free text was administered to multidisciplinary staff focused on workflow, agreement with ML and patient experience.Results59/71 (83%) responded. 81% disagreed/strongly disagreed their workflow was disrupted. 67% agreed/strongly agreed patients undergoing intervention were high risk. 75% agreed/strongly agreed they would implement the ML approach routinely if the study was positive. Free-text feedback focused on patient education and ML predictions.ConclusionsRandomised data and firsthand experience support positive reception of clinical ML. Providers highlighted future priorities, including patient counselling and workflow optimisation.

Females show a disproportionate burden of Alzheimer's disease pathology and higher Alzheimer's disease dementia prevalences compared to males, yet the mechanisms driving these vulnerabilities are unknown. There is sexual dimorphism in immunological functioning, and neuroimmune processes are implicated in Alzheimer's disease genesis. Using neuropathology indicators from human brain tissue, we examined the mediational role of microglial activation on the relationship between amyloid and tau and how it differs by sex. 187 decedents (64% female; 89 mean age at death; 62% non-demented) from the Rush Memory and Aging Project completed neuropathological evaluations with brain tissue quantified for microglial activation, amyloid-β and tau. Proportion of morphologically activated microglia was determined via immunohistochemistry (HLA-DP-DQ-DR) and morphological staging (stage I, II or III). Amyloid-β and tau burden were quantified via immunohistochemistry (M00872 or AT8, respectively). Using causal counterfactual modelling, we estimated the mediational effect of microglial activation on the amyloid-β to tau relationship in the whole sample and stratified by sex (amyloid-β → microglial activation → tau). Alternative models tested the role of microglia activation as the precipitating event (microglial activation → amyloid-β → tau). Microglial activation significantly mediated 33% [95% confidence interval (CI) 10-67] of the relationship between amyloid-β and tau in the whole sample; stratified analyses suggested this effect was stronger and only statistically significant in females. 57% (95% CI 22-100) of the effect of amyloid-β on tau was mediated through microglial activation in females, compared to 19% (95% CI 0-64) in males. Regional analyses suggested that mediational effects were driven by greater cortical versus subcortical microglial activation. Relationships were independent of cerebrovascular disease indices. Alternative models suggested that in females, microglial activation was a significant exposure both preceding the amyloid-β to tau relationship (mediational effect: 50%, 95% CI 23-90) and directly related to tau burden (microglia direct effect: 50%, 95% CI 10-77). By contrast, in males, only the direct effect of microglial activation to tau reached significance (74%, 95% CI 32-100) (mediational effect: 26%, 95% CI 0-68). Our models suggest a reciprocal, bidirectional relationship between amyloid-β and microglial activation that significantly accounts for tau burden in females. By contrast, in males, direct independent (non-mediational) relationships between microglial activation or amyloid-β with tau were observed. Microglial activation may be disproportionately important for Alzheimer's disease pathogenesis in females. Determining sex-specific vulnerabilities to Alzheimer's disease development both inform fundamental pathophysiology and support precision health approaches for this heterogeneous disease.

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)-a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

Accumulating data suggest that cerebrovascular disease contributes to Alzheimer's disease pathophysiology and progression toward dementia. Cerebral amyloid angiopathy is a form of cerebrovascular pathology that results from the build-up of β-amyloid in the vessel walls. Cerebral amyloid angiopathy commonly co-occurs with Alzheimer's disease pathology in the ageing brain and increases the risk of Alzheimer's disease dementia. In the present study, we examined whether cerebral amyloid angiopathy influences tau deposition and cognitive decline independently or synergistically with parenchymal β-amyloid burden. Secondly, we examined whether tau burden mediates the association between cerebral amyloid angiopathy and cognitive decline. We included data from autopsied subjects recruited from one of three longitudinal clinical-pathological cohort studies: the Rush Memory and Aging Project, the Religious Orders Study and the Minority Aging Research Study. Participants completed annual clinical and cognitive evaluations and underwent brain autopsy. Cerebral amyloid angiopathy pathology was rated as none, mild, moderate or severe. Bielschowsky silver stain was used to visualize neuritic β-amyloid plaques and neurofibrillary tangles. We used linear regression and linear mixed models to test independent versus interactive associations of cerebral amyloid angiopathy and neuritic plaque burden with tau burden and longitudinal cognitive decline, respectively. We used causal mediation models to examine whether tau mediates the association between cerebral amyloid angiopathy and cognitive decline. The study sample included 1722 autopsied subjects (age at baseline = 80.2 ± 7.1 years; age at death = 89.5 ± 6.7 years; 68% females). Cerebral amyloid angiopathy interacted with neuritic plaques to accelerate tau burden and cognitive decline. Specifically, those with more severe cerebral amyloid angiopathy pathology and higher levels of neuritic plaque burden had greater tau burden and faster cognitive decline. We also found that tau mediated the association between cerebral amyloid angiopathy and cognitive decline among participants with higher neuritic plaque burden. In summary, more severe levels of cerebral amyloid angiopathy and higher parenchymal β-amyloid burden interacted to promote cognitive decline indirectly via tau deposition. These results highlight the dynamic interplay between cerebral amyloid angiopathy and Alzheimer's disease pathology in accelerating progression toward dementia. These findings have implications for Alzheimer's disease clinical trials and therapeutic development.

The Collaborative Cohort of Cohorts for COVID-19 Research (C4R) is a national prospective study of adults comprising 14 established US prospective cohort studies. Starting as early as 1971, investigators in the C4R cohort studies have collected data on clinical and subclinical diseases and their risk factors, including behavior, cognition, biomarkers, and social determinants of health. C4R links this pre-coronavirus disease 2019 (COVID-19) phenotyping to information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and acute and postacute COVID-related illness. C4R is largely population-based, has an age range of 18-108 years, and reflects the racial, ethnic, socioeconomic, and geographic diversity of the United States. C4R ascertains SARS-CoV-2 infection and COVID-19 illness using standardized questionnaires, ascertainment of COVID-related hospitalizations and deaths, and a SARS-CoV-2 serosurvey conducted via dried blood spots. Master protocols leverage existing robust retention rates for telephone and in-person examinations and high-quality event surveillance. Extensive prepandemic data minimize referral, survival, and recall bias. Data are harmonized with research-quality phenotyping unmatched by clinical and survey-based studies; these data will be pooled and shared widely to expedite collaboration and scientific findings. This resource will allow evaluation of risk and resilience factors for COVID-19 severity and outcomes, including postacute sequelae, and assessment of the social and behavioral impact of the pandemic on long-term health trajectories.

BackgroundArtificial intelligence (AI) and machine learning (ML) have resulted in significant enthusiasm for their promise in healthcare. Despite this, prospective randomized controlled trials and successful clinical implementation remain limited. One clinical application of ML is mitigation of the increased risk for acute care during outpatient cancer therapy. We previously reported the results of the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) study (NCT04277650), which was a prospective, randomized quality improvement study demonstrating that ML based on electronic health record (EHR) data can direct supplemental clinical evaluations and reduce the rate of acute care during cancer radiotherapy with and without chemotherapy. The objective of this study is to report the workflow and operational challenges encountered during ML implementation on the SHIELD-RT study.ResultsData extraction and manual review steps in the workflow represented significant time commitments for implementation of clinical ML on a prospective, randomized study. Barriers include limited data availability through the standard clinical workflow and commercial products, the need to aggregate data from multiple sources, and logistical challenges from altering the standard clinical workflow to deliver adaptive care.ConclusionsThe SHIELD-RT study was an early randomized controlled study which enabled assessment of barriers to clinical ML implementation, specifically those which leverage the EHR. These challenges build on a growing body of literature and may provide lessons for future healthcare ML adoption.Trial registrationNCT04277650. Registered 20 February 2020. Retrospectively registered quality improvement study.

ObjectivesExpert abstraction of acute toxicities is critical in oncology research but is labor-intensive and variable. We assessed the accuracy of a natural language processing (NLP) pipeline to extract symptoms from clinical notes compared to physicians.Materials and methodsTwo independent reviewers identified present and negated National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 symptoms from 100 randomly selected notes for on-treatment visits during radiation therapy with adjudication by a third reviewer. A NLP pipeline based on Apache clinical Text Analysis Knowledge Extraction System was developed and used to extract CTCAE terms. Accuracy was assessed by precision, recall, and F1.ResultsThe NLP pipeline demonstrated high accuracy for common physician-abstracted symptoms, such as radiation dermatitis (F1 0.88), fatigue (0.85), and nausea (0.88). NLP had poor sensitivity for negated symptoms.ConclusionNLP accurately detects a subset of documented present CTCAE symptoms, though is limited for negated symptoms. It may facilitate strategies to more consistently identify toxicities during cancer therapy.

Background To determine if accelerometer measured sedentary behavior (SED), light-intensity physical activity (LPA), and moderate-to-vigorous-intensity physical activity (MVPA) in midlife is prospectively associated with cognitive function. Methods and Results Participants were 1970 adults enrolled in the CARDIA (Coronary Artery Risk Development in Young Adults) study who wore an accelerometer in 2005 to 2006 (ages 38-50 years) and had cognitive function assessments completed 5 and/or 10 years later. SED, LPA, and MVPA were measured by an ActiGraph 7164 accelerometer. Cognitive function tests included the Digit Symbol Substitution Test, Rey Auditory Verbal Learning Test, and Stroop Test. Compositional isotemporal substitution analysis examined associations of SED, LPA, and MVPA with repeated measures of the cognitive function standardized scores. In men, statistical reallocation of 30 minutes of LPA with 30 minutes of MVPA resulted in an estimated difference of SD 0.07 (95% CI, 0.01-0.14), SD 0.09 (95% CI, 0.02-0.17), and SD -0.11 (95% CI, -0.19 to -0.04) in the Digit Symbol Substitution Test, Rey Auditory Verbal Learning Test, and Stroop scores, respectively, indicating better performance. Associations were similar when reallocating time in SED with MVPA, but results were less robust. Reallocation of time in SED with LPA resulted in an estimated difference of SD -0.05 (95% CI, -0.06 to -0.03), SD -0.03 (95% CI, -0.05 to -0.01), and SD 0.05 (95% CI, 0.03- 0.07) in the Digit Symbol Substitution Test, Rey Auditory Verbal Learning Test, and Stroop scores, respectively, indicating worse performance. Associations were largely nonsignificant among women. Conclusions Our findings support the idea that for men, higher-intensity activities (MVPA) may be necessary in midlife to observe beneficial associations with cognition.

Background To determine if accelerometer measured sedentary behavior (SED), light-intensity physical activity (LPA), and moderate-to-vigorous-intensity physical activity (MVPA) in midlife is prospectively associated with cognitive function. Methods and Results Participants were 1970 adults enrolled in the CARDIA (Coronary Artery Risk Development in Young Adults) study who wore an accelerometer in 2005 to 2006 (ages 38-50 years) and had cognitive function assessments completed 5 and/or 10 years later. SED, LPA, and MVPA were measured by an ActiGraph 7164 accelerometer. Cognitive function tests included the Digit Symbol Substitution Test, Rey Auditory Verbal Learning Test, and Stroop Test. Compositional isotemporal substitution analysis examined associations of SED, LPA, and MVPA with repeated measures of the cognitive function standardized scores. In men, statistical reallocation of 30 minutes of LPA with 30 minutes of MVPA resulted in an estimated difference of SD 0.07 (95% CI, 0.01-0.14), SD 0.09 (95% CI, 0.02-0.17), and SD -0.11 (95% CI, -0.19 to -0.04) in the Digit Symbol Substitution Test, Rey Auditory Verbal Learning Test, and Stroop scores, respectively, indicating better performance. Associations were similar when reallocating time in SED with MVPA, but results were less robust. Reallocation of time in SED with LPA resulted in an estimated difference of SD -0.05 (95% CI, -0.06 to -0.03), SD -0.03 (95% CI, -0.05 to -0.01), and SD 0.05 (95% CI, 0.03- 0.07) in the Digit Symbol Substitution Test, Rey Auditory Verbal Learning Test, and Stroop scores, respectively, indicating worse performance. Associations were largely nonsignificant among women. Conclusions Our findings support the idea that for men, higher-intensity activities (MVPA) may be necessary in midlife to observe beneficial associations with cognition.

ObjectivesExpert abstraction of acute toxicities is critical in oncology research but is labor-intensive and variable. We assessed the accuracy of a natural language processing (NLP) pipeline to extract symptoms from clinical notes compared to physicians.Materials and methodsTwo independent reviewers identified present and negated National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 symptoms from 100 randomly selected notes for on-treatment visits during radiation therapy with adjudication by a third reviewer. A NLP pipeline based on Apache clinical Text Analysis Knowledge Extraction System was developed and used to extract CTCAE terms. Accuracy was assessed by precision, recall, and F1.ResultsThe NLP pipeline demonstrated high accuracy for common physician-abstracted symptoms, such as radiation dermatitis (F1 0.88), fatigue (0.85), and nausea (0.88). NLP had poor sensitivity for negated symptoms.ConclusionNLP accurately detects a subset of documented present CTCAE symptoms, though is limited for negated symptoms. It may facilitate strategies to more consistently identify toxicities during cancer therapy.

PurposePatients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited prospective studies investigating the clinical impact of ML in health care. The objective of this study was to determine whether ML can identify high-risk patients and direct mandatory twice-weekly clinical evaluation to reduce acute care visits during treatment.Patients and methodsDuring this single-institution randomized quality improvement study (ClinicalTrials.gov identifier: NCT04277650), 963 outpatient adult courses of RT and CRT started from January 7 to June 30, 2019, were evaluated by an ML algorithm. Among these, 311 courses identified by ML as high risk (> 10% risk of acute care during treatment) were randomized to standard once-weekly clinical evaluation (n = 157) or mandatory twice-weekly evaluation (n = 154). Both arms allowed additional evaluations on the basis of clinician discretion. The primary end point was the rate of acute care visits during RT. Model performance was evaluated using receiver operating characteristic area under the curve (AUC) and decile calibration plots.ResultsTwice-weekly evaluation reduced rates of acute care during treatment from 22.3% to 12.3% (difference, -10.0%; 95% CI, -18.3 to -1.6; relative risk, 0.556; 95% CI, 0.332 to 0.924; P = .02). Low-risk patients had a 2.7% acute care rate. Model discrimination was good in high- and low-risk patients undergoing standard once-weekly evaluation (AUC, 0.851).ConclusionIn this prospective randomized study, ML accurately triaged patients undergoing RT and CRT, directing clinical management with reduced acute care rates versus standard of care. This prospective study demonstrates the potential benefit of ML in health care and offers opportunities to enhance care quality and reduce health care costs.

PurposePatients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited prospective studies investigating the clinical impact of ML in health care. The objective of this study was to determine whether ML can identify high-risk patients and direct mandatory twice-weekly clinical evaluation to reduce acute care visits during treatment.Patients and methodsDuring this single-institution randomized quality improvement study (ClinicalTrials.gov identifier: NCT04277650), 963 outpatient adult courses of RT and CRT started from January 7 to June 30, 2019, were evaluated by an ML algorithm. Among these, 311 courses identified by ML as high risk (> 10% risk of acute care during treatment) were randomized to standard once-weekly clinical evaluation (n = 157) or mandatory twice-weekly evaluation (n = 154). Both arms allowed additional evaluations on the basis of clinician discretion. The primary end point was the rate of acute care visits during RT. Model performance was evaluated using receiver operating characteristic area under the curve (AUC) and decile calibration plots.ResultsTwice-weekly evaluation reduced rates of acute care during treatment from 22.3% to 12.3% (difference, -10.0%; 95% CI, -18.3 to -1.6; relative risk, 0.556; 95% CI, 0.332 to 0.924; P = .02). Low-risk patients had a 2.7% acute care rate. Model discrimination was good in high- and low-risk patients undergoing standard once-weekly evaluation (AUC, 0.851).ConclusionIn this prospective randomized study, ML accurately triaged patients undergoing RT and CRT, directing clinical management with reduced acute care rates versus standard of care. This prospective study demonstrates the potential benefit of ML in health care and offers opportunities to enhance care quality and reduce health care costs.

We examined associations of individual- and neighborhood-level life-course (LC) socioeconomic status (SES) with incident dementia in the Atherosclerosis Risk in Communities cohort. Individual- and neighborhood-level SES were assessed at 3 life epochs (childhood, young adulthood, midlife) via questionnaire (2001-2002) and summarized into LC-SES scores. Dementia was ascertained through 2013 using cognitive exams, telephone interviews, and hospital and death certificate codes. Cox regression was used to estimate hazard ratios of dementia by LC-SES scores in race-specific models. The analyses included data from 12,599 participants (25% Black) in the United States, with a mean age of 54 years and median follow-up of 24 years. Each standard-deviation greater individual LC-SES score was associated with a 14% (hazard ratio (HR) = 0.86, 95% confidence interval (CI): 0.81, 0.92) lower risk of dementia in White and 21% (HR = 0.79, 95% CI: 0.71, 0.87) lower risk in Black participants. Education was removed from the individual LC-SES score and adjusted for separately to assess economic factors of LC-SES. A standard-deviation greater individual LC-SES score, without education, was associated with a 10% (HR = 0.90, 95% CI: 0.84, 0.97) lower dementia risk in White and 15% (HR = 0.85, 95% CI: 0.76, 0.96) lower risk in Black participants. Neighborhood LC-SES was not associated with dementia. We found that individual LC-SES is a risk factor for dementia, whereas neighborhood LC-SES was not associated.

PurposeThe National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 is the standard for oncology toxicity encoding and grading, despite limited validation. We assessed interrater reliability (IRR) in multireviewer toxicity identification.Methods and materialsTwo reviewers independently reviewed 100 randomly selected notes for weekly on-treatment visits during radiation therapy from the electronic health record. Discrepancies were adjudicated by a third reviewer for consensus. Term harmonization was performed to account for overlapping symptoms in CTCAE. IRR was assessed based on unweighted and weighted Cohen's kappa coefficients.ResultsBetween reviewers, the unweighted kappa was 0.68 (95% confidence interval, 0.65-0.71) and the weighted kappa was 0.59 (0.22-1.00). IRR was consistent between symptoms noted as present or absent with a kappa of 0.6 (0.66-0.71) and 0.6 (0.65-0.69), respectively.ConclusionsSignificant discordance suggests toxicity identification, particularly retrospectively, is a complex and error-prone task. Strategies to minimize IRR, including training and simplification of the CTCAE criteria, should be considered in trial design and future terminologies.

PurposeThe National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE) v5.0 is the standard for oncology toxicity encoding and grading, despite limited validation. We assessed interrater reliability (IRR) in multireviewer toxicity identification.Methods and materialsTwo reviewers independently reviewed 100 randomly selected notes for weekly on-treatment visits during radiation therapy from the electronic health record. Discrepancies were adjudicated by a third reviewer for consensus. Term harmonization was performed to account for overlapping symptoms in CTCAE. IRR was assessed based on unweighted and weighted Cohen's kappa coefficients.ResultsBetween reviewers, the unweighted kappa was 0.68 (95% confidence interval, 0.65-0.71) and the weighted kappa was 0.59 (0.22-1.00). IRR was consistent between symptoms noted as present or absent with a kappa of 0.6 (0.66-0.71) and 0.6 (0.65-0.69), respectively.ConclusionsSignificant discordance suggests toxicity identification, particularly retrospectively, is a complex and error-prone task. Strategies to minimize IRR, including training and simplification of the CTCAE criteria, should be considered in trial design and future terminologies.

Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P

BackgroundAbnormal thyroid hormone levels (high or low) and autoimmunity from autoimmune thyroid disease (AITD) may increase dementia risk.MethodsWe examined the associations of thyroid dysfunction or possible AITD in 1990 - 1992 with dementia through 2017 in the Atherosclerosis Risk in Communities (ARIC) Neurocognitive Study. Thyroid dysfunction (subclinical and overt hypo- or hyperthyroidism and euthyroidism) was categorized from serum thyroid-stimulating hormone (TSH) and free thyroxine (FT4) cut-points and AITD from anti-thyroid peroxidase (anti-TPO) antibody positivity. Dementia was identified primarily based on cognitive test performance, neuropsychological examinations and clinician review of suspected cases. Additional cases of dementia were ascertained through telephone interviews or relevant hospital and death certificate codes. Cox regression with multivariable adjustment was used for analysis.ResultsAfter exclusions for missing data, 12,481 participants were included in the analysis (mean index exam age 57 ± 5.7 (44% male, 25% black)), and 2,235 incident dementia cases were identified. AITD was not significantly associated with dementia. Subclinical hypothyroidism was associated with a lower risk of dementia (hazard ratio (HR) (95% confidence interval (CI)): 0.74 (0.60 - 0.92)), while overt hyperthyroidism was associated with a higher risk of dementia (HR (95% CI): 1.40 (1.02 - 1.92)) compared to euthyroid participants. Participants with serum FT4 concentrations above the 95th percentile were at an increased risk of dementia compared to those in the middle 90% of FT4 (HR (95% CI): 1.23 (1.02 - 1.48)).ConclusionsSubclinical hypothyroidism was associated with reduced risk of dementia, whereas overt hyperthyroidism, particularly very elevated FT4, was associated with increased risk of dementia. The association between subclinical hypothyroidism and reduced risk of dementia cannot be explained, but may have been an artifact due to change. By extrapolation, effective treatment of overt hyperthyroidism may modestly reduce dementia risk in older adults.

BackgroundAbnormal thyroid hormone levels (high or low) and autoimmunity from autoimmune thyroid disease (AITD) may increase dementia risk.MethodsWe examined the associations of thyroid dysfunction or possible AITD in 1990 - 1992 with dementia through 2017 in the Atherosclerosis Risk in Communities (ARIC) Neurocognitive Study. Thyroid dysfunction (subclinical and overt hypo- or hyperthyroidism and euthyroidism) was categorized from serum thyroid-stimulating hormone (TSH) and free thyroxine (FT4) cut-points and AITD from anti-thyroid peroxidase (anti-TPO) antibody positivity. Dementia was identified primarily based on cognitive test performance, neuropsychological examinations and clinician review of suspected cases. Additional cases of dementia were ascertained through telephone interviews or relevant hospital and death certificate codes. Cox regression with multivariable adjustment was used for analysis.ResultsAfter exclusions for missing data, 12,481 participants were included in the analysis (mean index exam age 57 ± 5.7 (44% male, 25% black)), and 2,235 incident dementia cases were identified. AITD was not significantly associated with dementia. Subclinical hypothyroidism was associated with a lower risk of dementia (hazard ratio (HR) (95% confidence interval (CI)): 0.74 (0.60 - 0.92)), while overt hyperthyroidism was associated with a higher risk of dementia (HR (95% CI): 1.40 (1.02 - 1.92)) compared to euthyroid participants. Participants with serum FT4 concentrations above the 95th percentile were at an increased risk of dementia compared to those in the middle 90% of FT4 (HR (95% CI): 1.23 (1.02 - 1.48)).ConclusionsSubclinical hypothyroidism was associated with reduced risk of dementia, whereas overt hyperthyroidism, particularly very elevated FT4, was associated with increased risk of dementia. The association between subclinical hypothyroidism and reduced risk of dementia cannot be explained, but may have been an artifact due to change. By extrapolation, effective treatment of overt hyperthyroidism may modestly reduce dementia risk in older adults.

Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P

Rationale: Premature birth affects 10% of live births in the United States and is associated with alveolar simplification and altered pulmonary microvascular development. However, little is known about the long-term impact prematurity has on the pulmonary vasculature.Objectives: Determine the long-term effects of prematurity on right ventricular and pulmonary vascular hemodynamics.Methods: Preterm subjects (n = 11) were recruited from the Newborn Lung Project, a prospectively followed cohort at the University of Wisconsin-Madison, born preterm with very low birth weight (≤1,500 g; average gestational age, 28 wk) between 1988 and 1991. Control subjects (n = 10) from the same birth years were recruited from the general population. All subjects had no known adult cardiopulmonary disease. Right heart catheterization was performed to assess right ventricular and pulmonary vascular hemodynamics at rest and during hypoxic and exercise stress.Measurements and Main Results: Preterm subjects had higher mean pulmonary arterial pressures (mPAPs), with 27% (3 of 11) meeting criteria for borderline pulmonary hypertension (mPAP, 19-24 mm Hg) and 18% (2 of 11) meeting criteria for overt pulmonary hypertension (mPAP ≥ 25 mm Hg). Pulmonary vascular resistance and elastance were higher at rest and during exercise, suggesting a stiffer vascular bed. Preterm subjects were significantly less able to augment cardiac index or right ventricular stroke work during exercise. Among neonatal characteristics, total ventilatory support days was the strongest predictor of adult pulmonary pressure.Conclusions: Young adults born preterm demonstrate early pulmonary vascular disease, characterized by elevated pulmonary pressures, a stiffer pulmonary vascular bed, and right ventricular dysfunction, consistent with an increased risk of developing pulmonary hypertension.

PurposePatients undergoing radiotherapy (RT) or chemoradiotherapy (CRT) may require emergency department evaluation or hospitalization. Early identification may direct preventative supportive care, improving outcomes and reducing health care costs. We developed and evaluated a machine learning (ML) approach to predict these events.MethodsA total of 8,134 outpatient courses of RT and CRT from a single institution from 2013 to 2016 were identified. Extensive pretreatment data were programmatically extracted and processed from the electronic health record (EHR). Training and internal validation cohorts were randomly generated (3:1 ratio). Gradient tree boosting (GTB), random forest, support vector machine, and least absolute shrinkage and selection operator logistic regression approaches were trained and internally validated based on area under receiver operating characteristic (AUROC) curve. The most predictive ML approach was also evaluated using only disease- and treatment-related factors to assess predictive gain of extensive EHR data.ResultsAll methods had high predictive accuracy, particularly GTB (validation AUROC, 0.798). Extensive EHR data beyond disease and treatment information improved accuracy (delta AUROC, 0.056). A Youden-based cutoff corresponded to validation sensitivity of 81.0% (175 of 216 courses with events) and specificity of 67.3% (1,218 of 1811 courses without events). Interpretability is an important advantage of GTB. Variable importance identified top predictive factors, including treatment (planned RT and systemic therapy), pretreatment encounters (emergency department visits and admissions in the year before treatment), vital signs (weight loss and pain score in the year before treatment), and laboratory values (albumin level at weeks before treatment).ConclusionML predicts emergency visits and hospitalization during cancer therapy. Incorporating predictions into clinical care algorithms may help direct personalized supportive care, improve quality of care, and reduce costs. A prospective trial investigating ML-assisted direction of increased clinical assessments during RT is planned.

PurposePatients undergoing radiotherapy (RT) or chemoradiotherapy (CRT) may require emergency department evaluation or hospitalization. Early identification may direct preventative supportive care, improving outcomes and reducing health care costs. We developed and evaluated a machine learning (ML) approach to predict these events.MethodsA total of 8,134 outpatient courses of RT and CRT from a single institution from 2013 to 2016 were identified. Extensive pretreatment data were programmatically extracted and processed from the electronic health record (EHR). Training and internal validation cohorts were randomly generated (3:1 ratio). Gradient tree boosting (GTB), random forest, support vector machine, and least absolute shrinkage and selection operator logistic regression approaches were trained and internally validated based on area under receiver operating characteristic (AUROC) curve. The most predictive ML approach was also evaluated using only disease- and treatment-related factors to assess predictive gain of extensive EHR data.ResultsAll methods had high predictive accuracy, particularly GTB (validation AUROC, 0.798). Extensive EHR data beyond disease and treatment information improved accuracy (delta AUROC, 0.056). A Youden-based cutoff corresponded to validation sensitivity of 81.0% (175 of 216 courses with events) and specificity of 67.3% (1,218 of 1811 courses without events). Interpretability is an important advantage of GTB. Variable importance identified top predictive factors, including treatment (planned RT and systemic therapy), pretreatment encounters (emergency department visits and admissions in the year before treatment), vital signs (weight loss and pain score in the year before treatment), and laboratory values (albumin level at weeks before treatment).ConclusionML predicts emergency visits and hospitalization during cancer therapy. Incorporating predictions into clinical care algorithms may help direct personalized supportive care, improve quality of care, and reduce costs. A prospective trial investigating ML-assisted direction of increased clinical assessments during RT is planned.

The term Big Data has come to encompass a number of concepts and uses within medicine. This paper lays out the relevance and application of large collections of data in the radiation oncology community. We describe the potential importance and uses in clinical practice. The important concepts are then described and how they have been or could be implemented are discussed. Impediments to progress in the collection and use of sufficient quantities of data are also described. Finally, recommendations for how the community can move forward to achieve the potential of big data in radiation oncology are provided.

It is an exciting time for big data efforts in radiation oncology. The use of big data to help aid both outcomes and decision-making research is becoming a reality. However, there are true challenges that exist in the space of gathering and utilizing performance and outcomes data. Here, we summarize the current state of big data in radiation oncology with respect to outcomes and discuss some of the efforts and challenges in radiation oncology big data.

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

A substantial barrier to the single- and multi-institutional aggregation of data to supporting clinical trials, practice quality improvement efforts, and development of big data analytics resource systems is the lack of standardized nomenclatures for expressing dosimetric data. To address this issue, the American Association of Physicists in Medicine (AAPM) Task Group 263 was charged with providing nomenclature guidelines and values in radiation oncology for use in clinical trials, data-pooling initiatives, population-based studies, and routine clinical care by standardizing: (1) structure names across image processing and treatment planning system platforms; (2) nomenclature for dosimetric data (eg, dose-volume histogram [DVH]-based metrics); (3) templates for clinical trial groups and users of an initial subset of software platforms to facilitate adoption of the standards; (4) formalism for nomenclature schema, which can accommodate the addition of other structures defined in the future. A multisociety, multidisciplinary, multinational group of 57 members representing stake holders ranging from large academic centers to community clinics and vendors was assembled, including physicists, physicians, dosimetrists, and vendors. The stakeholder groups represented in the membership included the AAPM, American Society for Radiation Oncology (ASTRO), NRG Oncology, European Society for Radiation Oncology (ESTRO), Radiation Therapy Oncology Group (RTOG), Children's Oncology Group (COG), Integrating Healthcare Enterprise in Radiation Oncology (IHE-RO), and Digital Imaging and Communications in Medicine working group (DICOM WG); A nomenclature system for target and organ at risk volumes and DVH nomenclature was developed and piloted to demonstrate viability across a range of clinics and within the framework of clinical trials. The final report was approved by AAPM in October 2017. The approval process included review by 8 AAPM committees, with additional review by ASTRO, European Society for Radiation Oncology (ESTRO), and American Association of Medical Dosimetrists (AAMD). This Executive Summary of the report highlights the key recommendations for clinical practice, research, and trials.

A substantial barrier to the single- and multi-institutional aggregation of data to supporting clinical trials, practice quality improvement efforts, and development of big data analytics resource systems is the lack of standardized nomenclatures for expressing dosimetric data. To address this issue, the American Association of Physicists in Medicine (AAPM) Task Group 263 was charged with providing nomenclature guidelines and values in radiation oncology for use in clinical trials, data-pooling initiatives, population-based studies, and routine clinical care by standardizing: (1) structure names across image processing and treatment planning system platforms; (2) nomenclature for dosimetric data (eg, dose-volume histogram [DVH]-based metrics); (3) templates for clinical trial groups and users of an initial subset of software platforms to facilitate adoption of the standards; (4) formalism for nomenclature schema, which can accommodate the addition of other structures defined in the future. A multisociety, multidisciplinary, multinational group of 57 members representing stake holders ranging from large academic centers to community clinics and vendors was assembled, including physicists, physicians, dosimetrists, and vendors. The stakeholder groups represented in the membership included the AAPM, American Society for Radiation Oncology (ASTRO), NRG Oncology, European Society for Radiation Oncology (ESTRO), Radiation Therapy Oncology Group (RTOG), Children's Oncology Group (COG), Integrating Healthcare Enterprise in Radiation Oncology (IHE-RO), and Digital Imaging and Communications in Medicine working group (DICOM WG); A nomenclature system for target and organ at risk volumes and DVH nomenclature was developed and piloted to demonstrate viability across a range of clinics and within the framework of clinical trials. The final report was approved by AAPM in October 2017. The approval process included review by 8 AAPM committees, with additional review by ASTRO, European Society for Radiation Oncology (ESTRO), and American Association of Medical Dosimetrists (AAMD). This Executive Summary of the report highlights the key recommendations for clinical practice, research, and trials.

The NCCN Guidelines for Hepatobiliary Cancers provide treatment recommendations for cancers of the liver, gallbladder, and bile ducts. The NCCN Hepatobiliary Cancers Panel meets at least annually to review comments from reviewers within their institutions, examine relevant new data from publications and abstracts, and reevaluate and update their recommendations. These NCCN Guidelines Insights summarize the panel's discussion and most recent recommendations regarding locoregional therapy for treatment of patients with hepatocellular carcinoma.

ObjectiveMenopause is widely believed to be an established cause of sleep disorders, but evidence for this theory is inconclusive. Attributing any sleep problem to normal processes of menopause may lead to underdiagnosis of treatable sleep disorders in midlife women. This study uses detailed longitudinal data on sleep and menopausal health from participants in the Sleep in Midlife Women Study to investigate whether risk and severity of sleep-disordered breathing increase with progression through menopause, accounting for changes in age and body habitus.MethodsA total of 219 women aged 38 to 62 years were recruited from participants in the Wisconsin Sleep Cohort Study. Menopause status was determined from daily diaries in which participants reported menstrual flow, hot flashes, and use of hormonal medications. Each participant underwent in-home polysomnography studies every 6 months, to measure the apnea-hypopnea index (AHI) (N = 1,667 studies). Linear models with empirical standard errors were fit for logarithm of AHI on menopause status and years in menopause, adjusting for age, body mass index, waist girth, and neck girth.ResultsCompared with women in premenopause, AHI was 21% higher among participants in perimenopause (95% CI, -4 to 54), 31% higher among participants in postmenopause (95% CI, 2-68), and 41% higher among participants whose menopausal stage could not be distinguished between peri- and postmenopause (95% CI, 8-82). Among women who had begun perimenopause, each additional year in menopause was associated with 4% greater AHI (95% CI, 2-6).ConclusionsProgression through menopause is associated with greater sleep-disordered breathing severity. This association is independent of aging and changes in body habitus.

Genetic variants affecting hematopoiesis can influence commonly measured blood cell traits. To identify factors that affect hematopoiesis, we performed association studies for blood cell traits in the population-based Estonian Biobank using high-coverage whole-genome sequencing (WGS) in 2,284 samples and SNP genotyping in an additional 14,904 samples. Using up to 7,134 samples with available phenotype data, our analyses identified 17 associations across 14 blood cell traits. Integration of WGS-based fine-mapping and complementary epigenomic datasets provided evidence for causal mechanisms at several loci, including at a previously undiscovered basophil count-associated locus near the master hematopoietic transcription factor CEBPA The fine-mapped variant at this basophil count association near CEBPA overlapped an enhancer active in common myeloid progenitors and influenced its activity. In situ perturbation of this enhancer by CRISPR/Cas9 mutagenesis in hematopoietic stem and progenitor cells demonstrated that it is necessary for and specifically regulates CEBPA expression during basophil differentiation. We additionally identified basophil count-associated variation at another more pleiotropic myeloid enhancer near GATA2, highlighting regulatory mechanisms for ordered expression of master hematopoietic regulators during lineage specification. Our study illustrates how population-based genetic studies can provide key insights into poorly understood cell differentiation processes of considerable physiologic relevance.

The increasing complexity of modern radiation therapy planning and delivery challenges traditional prescriptive quality management (QM) methods, such as many of those included in guidelines published by organizations such as the AAPM, ASTRO, ACR, ESTRO, and IAEA. These prescriptive guidelines have traditionally focused on monitoring all aspects of the functional performance of radiotherapy (RT) equipment by comparing parameters against tolerances set at strict but achievable values. Many errors that occur in radiation oncology are not due to failures in devices and software; rather they are failures in workflow and process. A systematic understanding of the likelihood and clinical impact of possible failures throughout a course of radiotherapy is needed to direct limit QM resources efficiently to produce maximum safety and quality of patient care. Task Group 100 of the AAPM has taken a broad view of these issues and has developed a framework for designing QM activities, based on estimates of the probability of identified failures and their clinical outcome through the RT planning and delivery process. The Task Group has chosen a specific radiotherapy process required for "intensity modulated radiation therapy (IMRT)" as a case study. The goal of this work is to apply modern risk-based analysis techniques to this complex RT process in order to demonstrate to the RT community that such techniques may help identify more effective and efficient ways to enhance the safety and quality of our treatment processes. The task group generated by consensus an example quality management program strategy for the IMRT process performed at the institution of one of the authors. This report describes the methodology and nomenclature developed, presents the process maps, FMEAs, fault trees, and QM programs developed, and makes suggestions on how this information could be used in the clinic. The development and implementation of risk-assessment techniques will make radiation therapy safer and more efficient.

Pancreatic cancer is a formidable malignancy with poor outcomes. The majority of patients are unable to undergo resection, which remains the only potentially curative treatment option. The management of locally advanced (unresectable) pancreatic cancer is controversial; however, treatment with either chemotherapy or chemoradiation is associated with high rates of local tumor progression and metastases development, resulting in low survival rates. An emerging local modality is stereotactic body radiation therapy (SBRT), which uses image-guided, conformal, high-dose radiation. SBRT has demonstrated promising local control rates and resultant quality of life with acceptable rates of toxicity. Over the past decade, increasing clinical experience and data have supported SBRT as a local treatment modality. Nevertheless, additional research is required to further evaluate the role of SBRT and improve upon the persistently poor outcomes associated with pancreatic cancer. This review discusses the existing clinical experience and technical implementation of SBRT for pancreatic cancer and highlights the directions for ongoing and future studies.

Pancreatic cancer is a formidable malignancy with poor outcomes. The majority of patients are unable to undergo resection, which remains the only potentially curative treatment option. The management of locally advanced (unresectable) pancreatic cancer is controversial; however, treatment with either chemotherapy or chemoradiation is associated with high rates of local tumor progression and metastases development, resulting in low survival rates. An emerging local modality is stereotactic body radiation therapy (SBRT), which uses image-guided, conformal, high-dose radiation. SBRT has demonstrated promising local control rates and resultant quality of life with acceptable rates of toxicity. Over the past decade, increasing clinical experience and data have supported SBRT as a local treatment modality. Nevertheless, additional research is required to further evaluate the role of SBRT and improve upon the persistently poor outcomes associated with pancreatic cancer. This review discusses the existing clinical experience and technical implementation of SBRT for pancreatic cancer and highlights the directions for ongoing and future studies.

PurposeObservational studies suggest that menopausal hormone therapy protects against sleep-disordered breathing, but such findings may be biased by a "healthy user effect." When the Women's Health Initiative Study reported in 2002 that estrogen-progestin therapy increases heart disease risk, many women discontinued hormone therapy. We investigate healthy user bias in the association of hormone therapy with sleep-disordered breathing in the Sleep in Midlife Women Study.MethodsA total of 228 women aged 38 to 62 years were recruited from the Wisconsin Sleep Cohort Study. They underwent polysomnography to measure apnea-hypopnea index, at home semiannually from 1997 to 2006, and in the sleep laboratory every four years (n = 1828 studies). Hormone therapy was recorded monthly. Linear models with empirical standard errors regressed logarithm of apnea-hypopnea index on hormone use with a pre- or post-July 2002 interaction, adjusting for menopause and age.ResultsThe association of hormone therapy and sleep-disordered breathing was heterogeneous (P < .01); apnea-hypopnea index among users was 15% lower in the early period (95% confidence interval, -27% to -1%), but similar to nonusers in the late.ConclusionsHormone therapy was negatively associated with sleep-disordered breathing only until the Women's Health Initiative results were publicized. Hormone therapy may have been a marker for healthfulness in the early period, creating a spurious association with sleep-disordered breathing.

Genetic associations involving both rare and common alleles have been reported for schizophrenia but there have been no systematic scans for rare recessive genotypes using fully phased trio data. Here, we use exome sequencing in 604 schizophrenia proband-parent trios to investigate the role of recessive (homozygous or compound heterozygous) nonsynonymous genotypes in the disorder. The burden of recessive genotypes was not significantly increased in probands at either a genome-wide level or in any individual gene after adjustment for multiple testing. At a system level, probands had an excess of nonsynonymous compound heterozygous genotypes (minor allele frequency, MAF ⩽ 1%) in voltage-gated sodium channels (VGSCs; eight in probands and none in parents, P = 1.5 × 10(-)(4)). Previous findings of multiple de novo loss-of-function mutations in this gene family, particularly SCN2A, in autism and intellectual disability provide biological and genetic plausibility for this finding. Pointing further to the involvement of VGSCs in schizophrenia, we found that these genes were enriched for nonsynonymous mutations (MAF ⩽ 0.1%) in cases genotyped using an exome array, (5585 schizophrenia cases and 8103 controls), and that in the trios data, synaptic proteins interacting with VGSCs were also enriched for both compound heterozygosity (P = 0.018) and de novo mutations (P = 0.04). However, we were unable to replicate the specific association with compound heterozygosity at VGSCs in an independent sample of Taiwanese schizophrenia trios (N = 614). We conclude that recessive genotypes do not appear to make a substantial contribution to schizophrenia at a genome-wide level. Although multiple lines of evidence, including several from this study, suggest that rare mutations in VGSCs contribute to the disorder, in the absence of replication of the original findings regarding compound heterozygosity, this conclusion requires evaluation in a larger sample of trios.

ObjectiveMultimorbidity affects 26 million persons with diabetes, and care for comorbid chronic conditions may impact diabetes care quality. The aim of this study was to determine which chronic conditions were related to lack of achievement or achievement of diabetes care quality goals to determine potential targets for future interventions.Research design and methodsThis is an exploratory retrospective analysis of electronic health record data for 23 430 adults, aged 18-75, with diabetes who were seen at seven Midwestern US health systems. The main outcome measures were achievement of six diabetes quality metrics in the reporting year, 2011 (glycated haemoglobin (HbA1c) control and testing, low-density lipoprotein control and testing, blood pressure control, kidney testing). Explanatory variables were 62 chronic condition indicators. Analyses were adjusted for baseline patient sociodemographic and healthcare utilization factors.ResultsThe 62 chronic conditions varied in their relationships to diabetes care goal achievement for specific care goals. Congestive heart failure was related to lack of achievement of cholesterol management goals. Obesity was related to lack of HbA1c and BP control. Mental health conditions were related to both lack of achievement and achievement of different care goals. Three conditions were related to lack of cholesterol testing, including congestive heart failure and substance-use disorders. Of 17 conditions related to achieving control goals, 16 were related to achieving HbA1c control. One-half of the comorbid conditions did not predict diabetes care quality.ConclusionsFuture interventions could target patients at risk for not achieving diabetes care for specific care goals based on their individual comorbidities.

Genetic associations involving both rare and common alleles have been reported for schizophrenia but there have been no systematic scans for rare recessive genotypes using fully phased trio data. Here, we use exome sequencing in 604 schizophrenia proband-parent trios to investigate the role of recessive (homozygous or compound heterozygous) nonsynonymous genotypes in the disorder. The burden of recessive genotypes was not significantly increased in probands at either a genome-wide level or in any individual gene after adjustment for multiple testing. At a system level, probands had an excess of nonsynonymous compound heterozygous genotypes (minor allele frequency, MAF ⩽ 1%) in voltage-gated sodium channels (VGSCs; eight in probands and none in parents, P = 1.5 × 10(-)(4)). Previous findings of multiple de novo loss-of-function mutations in this gene family, particularly SCN2A, in autism and intellectual disability provide biological and genetic plausibility for this finding. Pointing further to the involvement of VGSCs in schizophrenia, we found that these genes were enriched for nonsynonymous mutations (MAF ⩽ 0.1%) in cases genotyped using an exome array, (5585 schizophrenia cases and 8103 controls), and that in the trios data, synaptic proteins interacting with VGSCs were also enriched for both compound heterozygosity (P = 0.018) and de novo mutations (P = 0.04). However, we were unable to replicate the specific association with compound heterozygosity at VGSCs in an independent sample of Taiwanese schizophrenia trios (N = 614). We conclude that recessive genotypes do not appear to make a substantial contribution to schizophrenia at a genome-wide level. Although multiple lines of evidence, including several from this study, suggest that rare mutations in VGSCs contribute to the disorder, in the absence of replication of the original findings regarding compound heterozygosity, this conclusion requires evaluation in a larger sample of trios.

AimsMost patients with diabetes have comorbid chronic conditions that could support (concordant) or compete with (discordant) diabetes care. We sought to determine the impact of the number of concordant and discordant chronic conditions on diabetes care quality.MethodsLogistic regression analysis of electronic health record data from 7 health systems on 24,430 patients with diabetes aged 18-75 years. Diabetes testing and control quality care goals were the outcome variables. The number of diabetes-concordant and the number of diabetes-discordant conditions were the main explanatory variables. Analysis was adjusted for health care utilization, health system and patient demographics.ResultsA higher number of concordant conditions were associated with higher odds of achieving testing and control goals for all outcomes except blood pressure control. There was no to minimal positive association between the number of discordant conditions and outcomes, except for cholesterol testing which was less likely with 4+ discordant conditions.ConclusionsHaving more concordant conditions makes diabetes care goal achievement more likely. The number of discordant conditions has a smaller, inconsistently significant impact on diabetes goal achievement. Interventions to improve diabetes care need to align with a patient's comorbidities, including the absence of comorbidities, especially concordant comorbidities.

ObjectiveMultimorbidity affects 26 million persons with diabetes, and care for comorbid chronic conditions may impact diabetes care quality. The aim of this study was to determine which chronic conditions were related to lack of achievement or achievement of diabetes care quality goals to determine potential targets for future interventions.Research design and methodsThis is an exploratory retrospective analysis of electronic health record data for 23 430 adults, aged 18-75, with diabetes who were seen at seven Midwestern US health systems. The main outcome measures were achievement of six diabetes quality metrics in the reporting year, 2011 (glycated haemoglobin (HbA1c) control and testing, low-density lipoprotein control and testing, blood pressure control, kidney testing). Explanatory variables were 62 chronic condition indicators. Analyses were adjusted for baseline patient sociodemographic and healthcare utilization factors.ResultsThe 62 chronic conditions varied in their relationships to diabetes care goal achievement for specific care goals. Congestive heart failure was related to lack of achievement of cholesterol management goals. Obesity was related to lack of HbA1c and BP control. Mental health conditions were related to both lack of achievement and achievement of different care goals. Three conditions were related to lack of cholesterol testing, including congestive heart failure and substance-use disorders. Of 17 conditions related to achieving control goals, 16 were related to achieving HbA1c control. One-half of the comorbid conditions did not predict diabetes care quality.ConclusionsFuture interventions could target patients at risk for not achieving diabetes care for specific care goals based on their individual comorbidities.

Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity, large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants, even in phenotypically relevant genes. Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation. To explore the potential of the Finnish founder population for studying low-frequency (0.5-5%) variants in complex diseases, we compared exome sequence data on 3,000 Finns to the same number of non-Finnish Europeans and discovered that, despite having fewer variable sites overall, the average Finn has more low-frequency loss-of-function variants and complete gene knockouts. We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36,262 Finns. Using a deep set of quantitative traits collected on these cohorts, we show 5 associations (p

Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity, large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants, even in phenotypically relevant genes. Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation. To explore the potential of the Finnish founder population for studying low-frequency (0.5-5%) variants in complex diseases, we compared exome sequence data on 3,000 Finns to the same number of non-Finnish Europeans and discovered that, despite having fewer variable sites overall, the average Finn has more low-frequency loss-of-function variants and complete gene knockouts. We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36,262 Finns. Using a deep set of quantitative traits collected on these cohorts, we show 5 associations (p

BackgroundArthritis is the leading cause of disability in the United States. We assess the generic health-related quality-of-life (HRQOL) among a nationally representative sample of U.S. adults with and without self-reported arthritis.MethodsThe NHMS, a cross-sectional survey of 3,844 adults (35-89 years) administered EuroQol-5D (EQ-5D), Health Utilities Index Mark 2 (HUI2) and 3 (HUI3), SF-36v2™, Quality of Well-being Scale self-administered form (QWB-SA), and the Health and Activities Limitations index (HALex) to each respondent via a telephone interview. Weighted multiple linear regression was used to generate age-gender-arthritis-stratified unadjusted HRQOL means and means adjusted for sociodemographic, socioeconomic covariates and comorbidities by arthritis-age category.ResultsThe estimated population prevalence of self-reported arthritis was 31%. People with arthritis were more likely to be woman, older, of lower socioeconomic status, and had more self-reported comorbidities than were those not reporting arthritis. Adults with arthritis had lower HRQOL on six different indexes compared with adults without arthritis, with overall differences ranging from 0.03 (QWB-SA, age-group 65-74) to 0.17 (HUI3, age-group 35-44; all P-value < .05).ConclusionArthritis in adults is associated with poorer HRQOL. We provide age-related reference values for six generic HRQOL measures in people with arthritis.

BackgroundArthritis is the leading cause of disability in the United States. We assess the generic health-related quality-of-life (HRQOL) among a nationally representative sample of U.S. adults with and without self-reported arthritis.MethodsThe NHMS, a cross-sectional survey of 3,844 adults (35-89 years) administered EuroQol-5D (EQ-5D), Health Utilities Index Mark 2 (HUI2) and 3 (HUI3), SF-36v2™, Quality of Well-being Scale self-administered form (QWB-SA), and the Health and Activities Limitations index (HALex) to each respondent via a telephone interview. Weighted multiple linear regression was used to generate age-gender-arthritis-stratified unadjusted HRQOL means and means adjusted for sociodemographic, socioeconomic covariates and comorbidities by arthritis-age category.ResultsThe estimated population prevalence of self-reported arthritis was 31%. People with arthritis were more likely to be woman, older, of lower socioeconomic status, and had more self-reported comorbidities than were those not reporting arthritis. Adults with arthritis had lower HRQOL on six different indexes compared with adults without arthritis, with overall differences ranging from 0.03 (QWB-SA, age-group 65-74) to 0.17 (HUI3, age-group 35-44; all P-value < .05).ConclusionArthritis in adults is associated with poorer HRQOL. We provide age-related reference values for six generic HRQOL measures in people with arthritis.