Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation.

Importance: Early detection of atrial fibrillation (AF) may help prevent adverse cardiovascular events such as stroke. Deep learning applied to electrocardiograms (ECGs) has been successfully used for early identification of several cardiovascular diseases. Objective: To determine whether deep learning models applied to outpatient ECGs in sinus rhythm can predict AF in a large and diverse patient population. Design, Setting, and Participants: This prognostic study was performed on ECGs acquired from January 1, 1987, to December 31, 2022, at 6 US Veterans Affairs (VA) hospital networks and 1 large non-VA academic medical center. Participants included all outpatients with 12-lead ECGs in sinus rhythm. Main Outcomes and Measures: A convolutional neural network using 12-lead ECGs from 2 US VA hospital networks was trained to predict the presence of AF within 31 days of sinus rhythm ECGs. The model was tested on ECGs held out from training at the 2 VA networks as well as 4 additional VA networks and 1 large non-VA academic medical center. Results: A total of 907 858 ECGs from patients across 6 VA sites were included in the analysis. These patients had a mean (SD) age of 62.4 (13.5) years, 6.4% were female, and 93.6% were male, with a mean (SD) CHA2DS2-VASc (congestive heart failure, hypertension, age, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age, sex category) score of 1.9 (1.6). A total of 0.2% were American Indian or Alaska Native, 2.7% were Asian, 10.7% were Black, 4.6% were Latinx, 0.7% were Native Hawaiian or Other Pacific Islander, 62.4% were White, 0.4% were of other race or ethnicity (which is not broken down into subcategories in the VA data set), and 18.4% were of unknown race or ethnicity. At the non-VA academic medical center (72 483 ECGs), the mean (SD) age was 59.5 (15.4) years and 52.5% were female, with a mean (SD) CHA2DS2-VASc score of 1.6 (1.4). A total of 0.1% were American Indian or Alaska Native, 7.9% were Asian, 9.4% were Black, 2.9% were Latinx, 0.03% were Native Hawaiian or Other Pacific Islander, 74.8% were White, 0.1% were of other race or ethnicity, and 4.7% were of unknown race or ethnicity. A deep learning model predicted the presence of AF within 31 days of a sinus rhythm ECG on held-out test ECGs at VA sites with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI, 0.85-0.86), accuracy of 0.78 (95% CI, 0.77-0.78), and F1 score of 0.30 (95% CI, 0.30-0.31). At the non-VA site, AUROC was 0.93 (95% CI, 0.93-0.94); accuracy, 0.87 (95% CI, 0.86-0.88); and F1 score, 0.46 (95% CI, 0.44-0.48). The model was well calibrated, with a Brier score of 0.02 across all sites. Among individuals deemed high risk by deep learning, the number needed to screen to detect a positive case of AF was 2.47 individuals for a testing sensitivity of 25% and 11.48 for 75%. Model performance was similar in patients who were Black, female, or younger than 65 years or who had CHA2DS2-VASc scores of 2 or greater. Conclusions and Relevance: Deep learning of outpatient sinus rhythm ECGs predicted AF within 31 days in populations with diverse demographics and comorbidities. Similar models could be used in future AF screening efforts to reduce adverse complications associated with this disease.

The Heart and Cannabis (THC) Cohort: Differences in Baseline Health and Behaviors by Cannabis Use.

BACKGROUND: Evidence on the cardiovascular health effects of cannabis use is limited. We designed a prospective cohort study of older Veterans (66 to 68 years) with coronary artery disease (CAD) to understand the cardiovascular consequences of cannabis use. We describe the cohort construction, baseline characteristics, and health behaviors that were associated with smoking cannabis. OBJECTIVE: To understand the cardiovascular consequences of cannabis use. DESIGN: We designed a prospective cohort study of older Veterans (66 to 68 years) with CAD. PARTICIPANTS: A total of 1,015 current cannabis smokers and 3,270 non-cannabis smokers with CAD. MAIN MEASURES: Using logistic regression, we examined the association of baseline variables with smoking cannabis in the past 30 days. RESULTS: The current cannabis smokers and non-current smokers were predominantly male (97.2% vs 97.1%, p=0.96). Characteristics associated with recent cannabis use in multivariable analyses included lack of a high school education (odds ratio [OR] 2.15, 95% confidence interval [CI]: 1.10 to 4.19), financial difficulty (OR 1.47, 95% CI: 1.02 to 2.11), tobacco use (OR 3.02, 95% CI: 1.66 to 5.48), current drug use (OR 2.82, 95% CI: 1.06 to 7.46), and prior drug use (OR 2.84, 95% CI: 2.11 to 3.82). In contrast, compared to individuals with 0 to 1 comorbid conditions, those with 5 chronic conditions or more (OR 0.43, 95% CI: 0.27 to 0.70) were less likely to smoke cannabis. CONCLUSIONS: In this older high-risk cohort, smoking cannabis was associated with higher social and behavioral risk, but with fewer chronic health conditions.

Examining the association of social risk with heart failure readmission in the Veterans Health Administration.

BACKGROUND: Previous research has found that social risk factors are associated with an increased risk of 30-day readmission. We aimed to assess the association of 5 social risk factors (living alone, lack of social support, marginal housing, substance abuse, and low income) with 30-day Heart Failure (HF) hospital readmissions within the Veterans Health Affairs (VA) and the impact of their inclusion on hospital readmission model performance. METHODS: We performed a retrospective cohort study using chart review and VA and Centers for Medicare and Medicaid Services (CMS) administrative data from a random sample of 1,500 elderly (≥ 65 years) Veterans hospitalized for HF in 2012. Using logistic regression, we examined whether any of the social risk factors were associated with 30-day readmission after adjusting for age alone and clinical variables used by CMS in its 30-day risk stratified readmission model. The impact of these five social risk factors on readmission model performance was assessed by comparing c-statistics, likelihood ratio tests, and the Hosmer-Lemeshow goodness-of-fit statistic. RESULTS: The prevalence varied among the 5 risk factors; low income (47 % vs. 47 %), lives alone (18 % vs. 19 %), substance abuse (14 % vs. 16 %), lacks social support (2 % vs. <1 %), and marginal housing (< 1 % vs. 3 %) among readmitted and non-readmitted patients, respectively. Controlling for clinical factors contained in CMS readmission models, a lack of social support was found to be associated with an increased risk of 30-day readmission (OR 4.8, 95 %CI 1.35-17.88), while marginal housing was noted to decrease readmission risk (OR 0.21, 95 %CI 0.03-0.87). Living alone (OR: 0.9, 95 %CI 0.64-1.26), substance abuse (OR 0.91, 95 %CI 0.67-1.22), and having low income (OR 1.01, 95 %CI 0.77-1.31) had no association with HF readmissions. Adding the five social risk factors to a CMS-based model (age and comorbid conditions; c-statistic 0.62) did not improve model performance (c-statistic: 0.62). CONCLUSIONS: While a lack of social support was associated with 30-day readmission in the VA, its prevalence was low. Moreover, the inclusion of some social risk factors did not improve readmission model performance. In an integrated healthcare system like the VA, social risk factors may have a limited effect on 30-day readmission outcomes.

Examining the Interfacility Variation of Social Determinants of Health in the Veterans Health Administration.

INTRODUCTION: Recently, numerous studies have linked social determinants of health (SDoH) with clinical outcomes. While this association is well known, the interfacility variability of these risk favors within the Veterans Health Administration (VHA) is not known. Such information could be useful to the VHA for resource and funding allocation. The aim of this study is to explore the interfacility variability of 5 SDoH within the VHA. METHODS: In a cohort of patients (aged ≥ 65 years) hospitalized at VHA acute care facilities with either acute myocardial infarction (AMI), heart failure (HF), or pneumonia in 2012, we assessed (1) the proportion of patients with any of the following five documented SDoH: lives alone, marginal housing, alcohol use disorder, substance use disorder, and use of substance use services, using administrative diagnosis codes and clinic stop codes; and (2) the documented facility-level variability of these SDoH. To examine whether variability was due to regional coding differences, we assessed the variation of living alone using a validated natural language processing (NLP) algorithm. RESULTS: The proportion of veterans admitted for AMI, HF, and pneumonia with SDoH was low. Across all 3 conditions, lives alone was the most common SDoH (2.2% [interquartile range (IQR), 0.7-4.7]), followed by substance use disorder (1.3% [IQR, 0.5-2.1]), and use of substance use services (1.2% [IQR, 0.6-1.8]). Using NLP, the proportion of hospitalized veterans with lives alone was higher for HF (14.4% vs 2.0%, P < .01), pneumonia (11% vs 1.9%, P < .01), and AMI (10.2% vs 1.4%, P < .01) compared with International Classification of Diseases, Ninth Edition codes. Interfacility variability was noted with both administrative and NLP extraction methods. CONCLUSIONS: The presence of SDoH in administrative data among patients hospitalized for common medical issues is low and variable across VHA facilities. Significant facility-level variation of 5 SDoH was present regardless of extraction method.

Development of a Cannabis Assessment Tool (CAT-1) to measure current and lifetime marijuana use among older Veterans.

OBJECTIVE: To develop a tool to assess current (past 30 days) and lifetime marijuana use in older Veterans. SETTING: US Veteran's Affairs Healthcare System. PARTICIPANTS: 704 older Veterans were screened, 339 completed the initial survey, 100 completed the follow-up. PRIMARY OUTCOME MEASURE: Pearson's correlation coefficient to assess strength of association between initial and follow-up survey on measures of current and lifetime marijuana use. RESULTS: Both a 'gram-month' measure of marijuana smoked in the past 30 days (r=0.83) and a frequency-based measure assessing total number of times smoked in the past 30 days were reliable (r=0.89). Both a simple categorical measure of lifetime use (agreement=85%) and a continuous measure of lifetime use (r=0.82) were reliable. CONCLUSIONS: The Cannabis Assessment Tool offers a reliable assessment of past 30 days and lifetime assessments of smoking cannabis in older adults.

Sources of Information and Beliefs About the Health Effects of Marijuana.

BACKGROUND: Marijuana is currently legal for recreational use in 10 states and Washington DC while a total of 34 states have implemented varying degrees of medical marijuana. The commercialization of marijuana has been accompanied by a proliferation of false claims regarding the therapeutic potential of marijuana, which are popularized by several different information sources. To date, no study has examined where US adults get their information regarding marijuana. OBJECTIVE: To determine the sources of information associated with believing unsupported claims about marijuana. DESIGN: Probability-based online survey PARTICIPANTS: 16,820 adults, with a response rate of about 55% (N = 9003) MAIN MEASURES: Most influential sources of information about marijuana and belief of statements consistent with misinformation, for example, smoking marijuana has preventative health benefits, secondhand marijuana smoke or use during pregnancy is completely or somewhat safe, and marijuana is not at all addictive. KEY RESULTS: There were 9003 respondents (response rate 55%). Forty-three percent believed unsupported claims about marijuana. The most influential sources of information were health professionals, traditional media, friends/relatives, and social media/internet. Individuals reporting social media or the Internet (1.46 CI [1.30, 1.64]), the marijuana industry (e.g., advertisements, dispensaries) (2.88 CI [2.15, 3.88]), and friends or relatives (1.41 CI[1.26, 1.58]) as the most influential source of information about marijuana were more likely to believe any statement consistent with misinformation about marijuana in comparison with those who reported other sources as most influential. CONCLUSIONS: Individuals reporting the most significant source of information regarding marijuana was from social media or the Internet, the marijuana industry, or friends or relatives were more likely to believe unsupported claims about marijuana. Public health campaigns to counter the misinformation about marijuana to the public are needed.

Differences in Opinions About Marijuana Use and Prevalence of Use by State Legalization Status.

OBJECTIVE: Beliefs about marijuana use and prevalence of use may be associated with the legalization status of the state of residence. We examined differences in views and rates of use of marijuana among residents in recreationally legal, medically legal, and nonlegal states. METHODS: We surveyed a nationally representative online panel of US adults (N = 16,280) and stratified results by marijuana legalization status of states. We compared views of residents of recreational states on benefits and risks of marijuana use to residents in other states. RESULTS: The response rate was 56.3% (n = 9003). Residents in recreationally legal states were more likely to believe marijuana could be beneficial for pain management (73% in recreationally legal states, 67% in medically legal states, 63% in nonlegal states; P value: <0.0001), provide relief from stress, anxiety or depression (52% in recreationally legal states, 47% in medically legal states, 46% in nonlegal states; P value: 0.01), and improve appetite (39% in recreationally legal states, 36% in medically legal states, 33% in nonlegal states; P value: <0.009). In addition, residents in recreational states were significantly more likely to believe that smoking 1 marijuana joint a day is somewhat or much safer than smoking 1 cigarette a day (40.8% in recreationally legal states, 39.1% in medically legal states, and 36.1% in nonlegal states; P value: <0.0001). Residents of recreationally and medically legal states were more likely to believe second-hand marijuana smoke was somewhat or much safer than second-hand tobacco smoke (38.3% in recreationally legal states, 38.3% in medically legal states, and 35.7% in nonlegal states; P value: 0.003). Past-year marijuana use in any form (20% in recreational, 14.1% in medical, 12% in nonlegal) and past-year marijuana use of multiple forms (11.1% in recreational, 6.1% in medical, 4.9% in nonlegal) were highest among residents of recreationally legal states. Overall, prevalence of past-year use of any form of marijuana use was more common among residents of recreationally legal states compared with other states (20.3%, confidence interval [CI] 19.5, 21.1 in recreationally legal states; 15.4%, CI 14.7, 16.2 in medically legal states; 11.9%, CI 11.2, 12.6 in nonlegal states). CONCLUSIONS: Residents in recreationally legal states were most likely to believe marijuana has benefits, marijuana smoke is safer than tobacco smoke, and have the highest rate of marijuana use. This is cause for concern, given the tide of commercialization, growing number of high-potency cannabis products, and favorable media coverage promoting use for health problems.

Substitution of marijuana for opioids in a national survey of US adults.

Opioid prescriptions for chronic pain and subsequent opioid-related complications have risen dramatically in the US. Recent data suggest that medical marijuana laws have been associated with lower state-level opioid overdose mortality. In a national survey, we examined the prevalence of substitution of marijuana for opioids among US adults taking opioids for pain.Using GfK's KnowledgePanel, we conducted an Internet-based survey of a nationally representative sample of 16,280 adults in 2017 about individual perceptions and use of marijuana. We developed questions designed to assess the extent and reasons for substitution of marijuana for opioids. We examined opioid substitution among respondents with a history of ever using marijuana who used opioids in the past 12 months. There were 9,003 respondents, corresponding to a 55.3% response rate. The mean age was 48 years. Among the 5% (n = 486) who reported ever using marijuana and using opioids in the past year, 43% used opioids daily, and 23% reported current (past 30 day) marijuana use. Forty-one percent reported a decrease or cessation of opioid use due to marijuana use; 46% reported no change in opioid use; and 8% reported an increase in opioid use. We found that a substantial number of US adults reported that they substituted marijuana for opioids.

Moonstone: a novel natural language processing system for inferring social risk from clinical narratives.

BACKGROUND: Social risk factors are important dimensions of health and are linked to access to care, quality of life, health outcomes and life expectancy. However, in the Electronic Health Record, data related to many social risk factors are primarily recorded in free-text clinical notes, rather than as more readily computable structured data, and hence cannot currently be easily incorporated into automated assessments of health. In this paper, we present Moonstone, a new, highly configurable rule-based clinical natural language processing system designed to automatically extract information that requires inferencing from clinical notes. Our initial use case for the tool is focused on the automatic extraction of social risk factor information - in this case, housing situation, living alone, and social support - from clinical notes. Nursing notes, social work notes, emergency room physician notes, primary care notes, hospital admission notes, and discharge summaries, all derived from the Veterans Health Administration, were used for algorithm development and evaluation. RESULTS: An evaluation of Moonstone demonstrated that the system is highly accurate in extracting and classifying the three variables of interest (housing situation, living alone, and social support). The system achieved positive predictive value (i.e. precision) scores ranging from 0.66 (homeless/marginally housed) to 0.98 (lives at home/not homeless), accuracy scores ranging from 0.63 (lives in facility) to 0.95 (lives alone), and sensitivity (i.e. recall) scores ranging from 0.75 (lives in facility) to 0.97 (lives alone). CONCLUSIONS: The Moonstone system is - to the best of our knowledge - the first freely available, open source natural language processing system designed to extract social risk factors from clinical text with good (lives in facility) to excellent (lives alone) performance. Although developed with the social risk factor identification task in mind, Moonstone provides a powerful tool to address a range of clinical natural language processing tasks, especially those tasks that require nuanced linguistic processing in conjunction with inference capabilities.

Examining the Utility of 30-day Readmission Rates and Hospital Profiling in the Veterans Health Administration.

BACKGROUND: The Veterans Health Administration (VA) reports hospital-specific 30-day risk-standardized readmission rates (RSRRs) using CMS-derived models. OBJECTIVE: The aim of this study was to examine and describe the interfacility variability of 30-day RSRRs for acute myocardial infarction (AMI), heart failure (HF), and pneumonia as a means to assess its utility for VA quality improvement and hospital comparison. RESEARCH DESIGN: A retrospective analysis of VA and Medicare claims data using one-year (2012) and three-year (2010-2012) data given their use for quality improvement or for hospital comparison, respectively. SUBJECTS: This study included 3,571 patients hospitalized for AMI at 56 hospitals, 10,609 patients hospitalized for HF at 102 hospitals, and 10,191 patients hospitalized for pneumonia at 106 hospitals. MEASURES: Hospital-specific 30-day RSRRs for AMI, HF, and pneumonia hospitalizations were calculated using hierarchical generalized linear models. RESULTS: Of 164 qualifying VA hospitals, 56 (34%), 102 (62%), and 106 (64%) qualified for analysis based on CMS criteria for AMI, HF, and pneumonia cohorts, respectively. Using 2012 data, we found that two hospitals (2%) had CHF RSRRs worse than the national average (+95% CI), whereas no hospital demonstrated worse-than-average risk-stratified readmission Rate (RSRR; +95% CI) for AMI or pneumonia. After increasing the number of facility admissions by combining three years of data, we found that four (range: 3.5%-5.3%) hospitals had RSRRs worse than the national average (+95% CI) for all three conditions. CONCLUSIONS: The Centers for Medicare and Medicaid Services-derived 30-day readmission measure may not be a useful measure to distinguish VA interfacility performance or drive quality improvement given the low facility-level volume of such readmissions.

Marijuana Use, Respiratory Symptoms, and Pulmonary Function: A Systematic Review and Meta-analysis.

Background: The health effects of smoking marijuana are not well-understood. Purpose: To examine the association between marijuana use and respiratory symptoms, pulmonary function, and obstructive lung disease among adolescents and adults. Data Sources: PubMed, Embase, PsycINFO, MEDLINE, and the Cochrane Library from 1 January 1973 to 30 April 2018. Study Selection: Observational and interventional studies published in English that reported pulmonary outcomes of adolescents and adults who used marijuana. Data Extraction: Four reviewers independently extracted study characteristics and assessed risk of bias. Three reviewers assessed strength of evidence. Studies of similar design with low or moderate risk of bias and sufficient data were pooled. Data Synthesis: Twenty-two studies were included. A pooled analysis of 2 prospective studies showed that marijuana use was associated with an increased risk for cough (risk ratio [RR], 2.04 [95% CI, 1.02 to 4.06]) and sputum production (RR, 3.84 [CI, 1.62 to 9.07]). Pooled analysis of cross-sectional studies (1 low and 3 moderate risk of bias) showed that marijuana use was associated with cough (RR, 4.37 [CI, 1.71 to 11.19]), sputum production (RR, 3.40 [CI, 1.99 to 5.79]), wheezing (RR, 2.83 [CI, 1.89 to 4.23]), and dyspnea (RR, 1.56 [CI, 1.33 to 1.83]). Data on pulmonary function and obstructive lung disease were insufficient. Limitation: Few studies were at low risk of bias, marijuana exposure was limited in the population studied, cohorts were young overall, assessment of marijuana exposure was not uniform, and study designs varied. Conclusion: Low-strength evidence suggests that smoking marijuana is associated with cough, sputum production, and wheezing. Evidence on the association between marijuana use and obstructive lung disease and pulmonary function is insufficient. Primary Funding Source: None. (PROSPERO: CRD42017059224).

Risks and Benefits of Marijuana Use: A National Survey of U.S. Adults.

Background: Despite insufficient evidence regarding its risks and benefits, marijuana is increasingly available and is aggressively marketed to the public. Objective: To understand the public's views on the risks and benefits of marijuana use. Design: Probability-based online survey. Setting: United States, 2017. Participants: 16 280 U.S. adults. Measurements: Proportion of U.S. adults who agreed with a statement. Results: The response rate was 55.3% (n = 9003). Approximately 14.6% of U.S. adults reported using marijuana in the past year. About 81% of U.S. adults believe marijuana has at least 1 benefit, whereas 17% believe it has no benefit. The most common benefit cited was pain management (66%), followed by treatment of diseases, such as epilepsy and multiple sclerosis (48%), and relief from anxiety, stress, and depression (47%). About 91% of U.S. adults believe marijuana has at least 1 risk, whereas 9% believe it has no risks. The most common risk identified by the public was legal problems (51.8%), followed by addiction (50%) and impaired memory (42%). Among U.S. adults, 29.2% agree that smoking marijuana prevents health problems. About 18% believe exposure to secondhand marijuana smoke is somewhat or completely safe for adults, whereas 7.6% indicated that it is somewhat or completely safe for children. Of the respondents, 7.3% agree that marijuana use is somewhat or completely safe during pregnancy. About 22.4% of U.S. adults believe that marijuana is not at all addictive. Limitation: Wording of the questions may have affected interpretation. Conclusion: Americans' view of marijuana use is more favorable than existing evidence supports. Primary Funding Source: National Heart, Lung, and Blood Institute.

A search algorithm for identifying likely users and non-users of marijuana from the free text of the electronic medical record.

BACKGROUND: The harmful effects of marijuana on health and in particular cardiovascular health are understudied. To develop such knowledge, an efficient method of developing an informative cohort of marijuana users and non-users is needed. METHODS: We identified patients with a diagnosis of coronary artery disease using ICD-9 codes who were seen in the San Francisco VA in 2015. We imported these patients' medical record notes into an informatics platform that facilitated text searches. We categorized patients into those with evidence of marijuana use in the past 12 months and patients with no such evidence, using the following text strings: "marijuana", "mjx", and "cannabis". We randomly selected 51 users and 51 non-users based on this preliminary classification, and sent a recruitment letter to 97 of these patients who had contact information available. Patients were interviewed on marijuana use and domains related to cardiovascular health. Data on marijuana use collected from the medical record was compared to data collected as part of the interview. RESULTS: The interview completion rate was 71%. Among the 35 patients identified by text strings as having used marijuana in the previous year, 15 had used marijuana in the past 30 days (positive predictive value = 42.9%). The probability of use in the past month increased from 8.8% to 42.9% in people who have these keywords in their medical record compared to those who did not have these terms in their medical record. CONCLUSION: Methods that combine text search strategies for participant recruitment with health interviews provide an efficient approach to developing prospective cohorts that can be used to study the health effects of marijuana.

Guideline concordance of new statin prescriptions: who got a statin?

OBJECTIVES: Statins are recommended to reduce serum cholesterol in patients at risk for atherosclerotic cardiovascular disease. Despite the prevalence of statin use, little is known about the indications for new prescriptions. We assessed the concordance of new statin prescriptions in the Veterans Health Administration (VHA) compared with the Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III, or ATP III) guidelines (the guidelines in force in 2012) and the American College of Cardiology (ACC)-American Heart Association (AHA) 2013 guidelines. STUDY DESIGN: Cross-sectional study. METHODS: We identified every patient who received a new prescription (no statin use in the prior year) in the VHA in 2012. Patients were excluded if they had incomplete data, triglycerides greater than 400 mg/dL, or fewer than 2 primary care visits to ensure adequate baseline data to calculate Framingham and ACC-AHA 2013 risk scores. RESULTS: We identified 250,243 new statin prescriptions in 2012 in the VHA, with 121,081 meeting inclusion criteria. Among new prescriptions, 68% were prescribed for primary prevention and 32% were prescribed for secondary prevention. Among patients receiving new statins for primary prevention, 48% did not have an indication supported by the ATP III guideline and 20% did not have an indication supported by the ACC/AHA guideline. Overall, approximately 19% of patients may have received a statin for an indication not supported by either guideline. CONCLUSIONS: Veterans are commonly prescribed statins for indications not supported by professional society guidelines. The finding of common use of statins outside established guidelines represents an opportunity to improve the quality and value of the healthcare delivery.

Molecular subgroups of medulloblastoma identification using noninvasive magnetic resonance spectroscopy.

BACKGROUND: Medulloblastomas in children can be categorized into 4 molecular subgroups with differing clinical characteristics, such that subgroup determination aids in prognostication and risk-adaptive treatment strategies. Magnetic resonance spectroscopy (MRS) is a widely available, noninvasive tool that is used to determine the metabolic characteristics of tumors and provide diagnostic information without the need for tumor tissue. In this study, we investigated the hypothesis that metabolite concentrations measured by MRS would differ between molecular subgroups of medulloblastoma and allow accurate subgroup determination. METHODS: MRS was used to measure metabolites in medulloblastomas across molecular subgroups (SHH = 12, Groups 3/4 = 17, WNT = 1). Levels of 14 metabolites were analyzed to determine those that were the most discriminant for medulloblastoma subgroups in order to construct a multivariable classifier for distinguishing between combined Group 3/4 and SHH tumors. RESULTS: Medulloblastomas across molecular subgroups revealed distinct spectral features. Group 3 and Group 4 tumors demonstrated metabolic profiles with readily detectable taurine, lower levels of lipids, and high levels of creatine. SHH tumors showed prominent choline and lipid with low levels of creatine and little or no evidence of taurine. A 5-metabolite subgroup classifier inclusive of creatine, myo-inositol, taurine, aspartate, and lipid 13a was developed that could discriminate between Group 3/4 and SHH medulloblastomas with excellent accuracy (cross-validated area under the curve [AUC] = 0.88). CONCLUSIONS: The data show that medulloblastomas of Group 3/4 differ metabolically as measured using MRS when compared with SHH molecular subgroups. MRS is a useful and accurate tool to determine medulloblastoma molecular subgroups.

Tumor-associated macrophages in SHH subgroup of medulloblastomas.

PURPOSE: Medulloblastoma in children can be categorized into at least four molecular subgroups, offering the potential for targeted therapeutic approaches to reduce treatment-related morbidities. Little is known about the role of tumor microenvironment in medulloblastoma or its contribution to these molecular subgroups. Tumor microenvironment has been shown to be an important source for therapeutic targets in both adult and pediatric neoplasms. In this study, we investigated the hypothesis that expression of genes related to tumor-associated macrophages (TAM) correlates with the medulloblastoma molecular subgroups and contributes to a diagnostic signature. METHODS: Gene-expression profiling using human exon array (n = 168) was analyzed to identify medulloblastoma molecular subgroups and expression of inflammation-related genes. Expression of 45 tumor-related and inflammation-related genes was analyzed in 83 medulloblastoma samples to build a gene signature predictive of molecular subgroups. TAMs in medulloblastomas (n = 54) comprising the four molecular subgroups were assessed by immunohistochemistry (IHC). RESULTS: A 31-gene medulloblastoma subgroup classification score inclusive of TAM-related genes (CD163 and CSF1R) was developed with a misclassification rate of 2%. Tumors in the Sonic Hedgehog (SHH) subgroup had increased expression of inflammation-related genes and significantly higher infiltration of TAMs than tumors in the Group 3 or Group 4 subgroups (P < 0.0001 and P < 0.0001, respectively). IHC data revealed a strong association between location of TAMs and proliferating tumor cells. CONCLUSIONS: These data show that SHH tumors have a unique tumor microenvironment among medulloblastoma subgroups. The interactions of TAMs and SHH medulloblastoma cells may contribute to tumor growth revealing TAMs as a potential therapeutic target.