The use of electronic nicotine delivery systems (ENDS) is increasing among young adults. However, there are few studies regarding predictors of ENDS initiation in tobacco-naive young adults. Identifying the risk and protective factors of ENDS initiation that are specific to tobacco-naive young adults will enable the creation of targeted policies and prevention programs. This study used machine learning (ML) to create predictive models, identify risk and protective factors for ENDS initiation for tobacco-naive young adults, and the relationship between these predictors and the prediction of ENDS initiation. We used nationally representative data of tobacco-naive young adults in the U.S drawn from the Population Assessment of Tobacco and Health (PATH) longitudinal cohort survey. Respondents were young adults (18-24 years) who had never used any tobacco products in Wave 4 and who completed Waves 4 and 5 interviews. ML techniques were used to create models and determine predictors at 1-year follow-up from Wave 4 data. Among the 2,746 tobacco-naive young adults at baseline, 309 initiated ENDS use at 1-year follow-up. The top five prospective predictors of ENDS initiation were susceptibility to ENDS, increased days of physical exercise specifically designed to strengthen muscles, frequency of social media use, marijuana use and susceptibility to cigarettes. This study identified previously unreported and emerging predictors of ENDS initiation that warrant further investigation and provided comprehensive information on the predictors of ENDS initiation. Furthermore, this study showed that ML is a promising technique that can aid ENDS monitoring and prevention programs.

Young adult never cigarette smokers with disabilities may be at particular risk for adopting e-cigarettes, but little attention has been paid to these people. This study examines the associations between different types of disability and e-cigarette use in this population. Young adult never-smokers from the 2016–2017 Behavioral Risk Factor Surveillance System (BRFSS) survey who were either never or current e-cigarette users (n = 79,177) were selected for the analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select confounders for multivariable logistic regression models. Multivariable logistic regression models were used to determine the associations between current e-cigarette use and different types of disability after incorporating BRFSS survey design and adjusting for confounders. Young adult never-smokers who reported any disability had increased odds (OR 1.44, 95% CI 1.18–1.76) of e-cigarette use compared to those who reported no disability. Young adult never-smokers who reported self-care, cognitive, vision, and independent living disabilities had higher odds of e-cigarette use compared to those who reported no disability. There was no statistically significant difference in the odds of e-cigarette use for those reporting hearing and mobility disabilities compared to those who reported no disability. This study highlights the need for increased public education and cessation programs for this population.

Background: Electronic cigarettes (e-cigarettes) generally have a more favorable toxicant profile than conventional cigarettes; however, limited information exists for women of reproductive age (WRA). Our aim was to compare biomarkers of toxicant exposure, inflammation, and oxidative stress among WRA who self-report exclusive e-cigarette use, exclusive cigarette smoking, or never tobacco use (controls). Methods: Multivariable linear regression models were used to compare the geometric means of urinary biomarkers of toxicant exposure and their metabolites, serum markers of inflammation [highly sensitive C-reactive protein, soluble intercellular adhesion molecule (sICAM), interleukin 6, fibrinogen], and a measurement of oxidative stress [prostaglandin F2a-8-isoprostane (F2PG2a)] among WRA from the Population Assessment of Tobacco and Health survey. Results: E-cigarette users had higher levels of lead, tobacco-specific nitrosamines, nicotine metabolites, and some volatile organic compounds (VOCs) than controls. Except for cadmium and lead, e-cigarette users had lower levels of the analyzed urinary toxicant biomarkers compared with cigarette smokers. Cigarette smokers had higher levels of all the biomarkers of toxicant exposure than controls. There were no significant differences in the levels of markers of inflammation and oxidative stress between e-cigarette users and controls. E-cigarette users and controls had lower levels of sICAM and F2PG2a than cigarette smokers. Conclusion: WRA who use e-cigarettes had lower levels of some of the evaluated urinary biomarkers of toxicant exposure and serum biomarkers of inflammation and oxidative stress than those who smoke cigarettes, but higher lead, nicotine metabolites, and some VOCs than controls, which can increase health risks.

Purpose: Electronic cigarette (e-cigarette) use has increased exponentially among the youth in the United States and may increase the incidence of substance use. Methods: Youth participants (12-17 years) were surveyed through the Population Assessment of Tobacco and Health study over a three-year time period. Youth with any baseline substance use or diagnosis of an attention deficit disorder were excluded from the analysis. Multivariable logistic regressions were used to assess the association between e-cigarette use at Wave 1 and incident substance use (marijuana, painkillers, sedatives, or tranquilizers and Ritalin/Adderall) and poly substance use at Wave 2 or 3, and marijuana use in the electronic nicotine device at Wave 3. Results: Baseline ever e-cigarette users who had no history of marijuana, nonprescribed drugs and illicit substance use in Wave 1 had increased odds of reporting incident use of marijuana (odds ratio 2.59, 95% confidence interval: 1.90-3.52), nonprescribed Ritalin/Adderall use (1.89, 1.09 -3.28), or polysubstance use (2.09, 1.43-3.05) in Wave 2 or 3 compared to never e-cigarette users. They were also more likely to report use of marijuana in the electronic nicotine product (2.26, 1.56 -3.27) in Wave 3 compared to never e-cigarette users. There was no statistically significant association between baseline e-cigarette use and incident use of painkillers, sedatives, or tranquilizers in Wave 2 or 3 (1.21, .79-1.87). Conclusions: E-cigarette use is associated with incident use of marijuana, marijuana in electronic nicotine devices, Ritalin/Adderall, and polysubstance use but not painkillers, sedatives, or tranquilizers. Results indicate that e-cigarettes are associated with subsequent additional risky health behaviors in youth. (C) 2020 Society for Adolescent Health and Medicine. All rights reserved.

E-cigarette use is increasing among young adult never smokers of conventional cigarettes, but the awareness of the factors associated with e-cigarette use in this population is limited. The goal of this work was to use machine learning (ML) algorithms to determine the factors associated with current e-cigarette use among US young adult never cigarette smokers. Young adult (18–34 years) never cigarette smokers from the 2016 and 2017 Behavioral Risk Factor Surveillance System (BRFSS) who reported current or never e-cigarette use were used for the analysis (n = 79,539). Variables associated with current e-cigarette use were selected by two ML algorithms (Boruta and Least absolute shrinkage and selection operator (LASSO)). Odds ratios were calculated to determine the association between e-cigarette use and the variables selected by the ML algorithms, after adjusting for age, gender and race/ethnicity and incorporating the BRFSS complex design. The prevalence of e-cigarette use varied across states. Factors previously reported in the literature, such as age, race/ethnicity, alcohol use, depression, as well as novel factors associated with e-cigarette use, such as disabilities, obesity, history of diabetes and history of arthritis were identified. These results can be used to generate further hypotheses for research, increase public awareness and help provide targeted e-cigarette education.

Abstract Rationale: E-cigarettes are popular among youth and young adults and have been shown to be associated with pulmonary conditions such as asthma and COPD. YKL-40 may serve as a biomarker of pulmonary diseases and may predict the loss of pulmonary function among smokers. We hypothesized that similar to cigarette smokers, e-cigarette users will have higher levels of YKL-40 compared to non-tobacco users. Methods We conducted a cross-sectional study of adults between 18 and 55 years old. Inclusion criteria were: exclusive e-cigarette use or cigarette smoking for ≥ 1 year or no history of tobacco use. Participants with a history of pulmonary illness, atopy, medications (except birth control pills), marijuana, and illegal substance use were excluded. Custom Multiplex ELISA was used to measure YKL-40 and other biomarker levels in the serum and induced sputum of the participants. Multivariable linear regression was used to compare the levels of YLK-40 in healthy participants, e-cigarette, and cigarette users after adjusting for age, sex, and BMI. Results We recruited 20 healthy controls, 23 cigarette smokers, and 22 exclusive e-cigarette users. Serum YKL-40 (ng/ml) was significantly higher in e-cigarette users (Median 21.2 [IQR 12.1-24.0] ng/ml) when compared to controls (12.2 [IQR 8.7-18.1] ng/ml, p = 0.016) but comparable to cigarette smokers (21.6 [IQR 11.62-51.7] ng/ml, p = 0.31). No significant differences were found in the serum or sputum of the other biomarkers tested. Conclusion The inflammatory biomarker, YKL-40 is elevated in the serum but not the sputum of e-cigarette users with no reported pulmonary disease. Further research is necessary to characterize this association.

Introduction: There is little information on the incidence of atrial fibrillation (“a-fib”) in patients hospitalized with pneumonia. Our aim was to assess the incidence of a-fib after hospitalization for pneumonia and the impact of a-fib on 30-day mortality. Methods: We conducted a retrospective cohort study using United States Department of Veterans Affairs (VA) national data including patients >65 years hospitalized with pneumonia in fiscal years 2002-2007 that did not have a prior diagnosis of a-fib. We included only the first pneumonia-related hospitalization. We identified patients who had a new diagnosis of a-fib within 30-days of admission. The primary outcome was all-cause 30-day mortality. Our primary analysis was a multilevel regression model, adjusting for >40 potential confounders including sociodemographics, health care utilization, comorbidities, medications, and severity of illness. Results: We identified 38,679 patients who met the inclusion criteria. Of these, 2,690 (7%) had a new diagnosis of a-fib within 30-days of admission. In the univariate analysis, a-fib was associated with increased 30-day mortality (20.1% vs. 13.2%, P<0.0001). In the multivariable regression model, incident a-fib was significantly associated with increased 30-day mortality (odds ratio 1.32, 95% confidence interval 1.18-1.47). Conclusion: A clinically significant number of patients hospitalized for pneumonia have new onset a-fib and it is associated with increased 30-day mortality. Additional research is needed to identify the potential causes of a-fib as well as to determine the ideal way to manage these patients.

Introduction Atypical antipsychotics are commonly used in patients with psychiatric conditions and dementia. They are also frequently used in patients being admitted with pneumonia; however, there are few safety data. The purpose of this study was to examine whether atypical antipsychotic use prior to admission is associated with increased mortality in patients with pneumonia. Methods We conducted a retrospective cohort study of hospitalised patients with pneumonia over a 10-year period. We included patients 65 years or older and hospitalised with pneumonia. For our primary analysis, we used propensity score matching to balance confounders between atypical antipsychotic users and nonusers. Results There were 102 897 patients and 5977 were taking atypical antipsychotics. After matching there were 5513 users and 5513 nonusers. Atypical antipsychotic use was associated with increased odds of 30-day (OR 1.20, 95% CI 1.11–1.31) and 90-day mortality (1.19, 1.09–1.30). Conclusion In patients 65 years or older that are hospitalised with pneumonia, we found an association between atypical antipsychotic use and increased odds of mortality. This was particularly pronounced for patients with pre-existing psychiatric or cardiac conditions. We suggest closely monitoring patients who use these medications and minimising their use in older adult patients.

Electronic nicotine product use is increasing in the U.S., but few studies have addressed its effects on oral health. The goal of this work was to determine the association between electronic nicotine product use and periodontal disease. Population Assessment of Tobacco and Health adult survey data from 2013–2016 (waves 1, 2 and 3) was used for the analysis. Longitudinal electronic nicotine product users used electronic nicotine products regularly every day or somedays in all three waves. Participants with new cases of gum disease reported no history of gum disease in wave 1 but reported being diagnosed with gum disease in waves 2 or 3. Odds ratios (OR) were calculated to determine the association between electronic nicotine product use and new cases of gum disease after controlling for potential confounders. Compared to never users, longitudinal electronic nicotine product users had increased odds of being diagnosed with gum disease (OR 1.76, 95% Confidence Interval (CI) 1.12–2.76) and bone loss around teeth (OR 1.67, 95% CI 1.06–2.63). These odds were higher for participants with a history of marijuana and a history of illicit or non-prescribed drug use. Our findings show that e-cigarettes may be harmful to oral health.

BACKGROUND:Prior research has demonstrated high mortality rates in patients with cirrhosis who contract bacterial infections. The purpose of our study was to explore clinical outcomes such as 90-day mortality, rehospitalization, and intensive care unit (ICU) admission in older veterans with pneumonia and cirrhosis.METHODS:We conducted a retrospective cohort study of hospitalized patients with community-acquired pneumonia at any Departments of Veterans Affairs (VA) hospital over a 10-year period. We included patients 65 years or older who consistently received VA care and who were diagnosed with community-acquired pneumonia. There were 103,997 patients who met the inclusion criteria, and 1,246 patients with cirrhosis. We used multilevel regression models to examine the association between cirrhosis and the outcomes of interest after controlling for potential confounders.RESULTS:Cirrhosis was associated with significantly increased odds of 90-day mortality (odds ratio 1.79, 95% confidence interval, 1.57-2.04). There were also significantly increased odds of rehospitalization within 90-days (1.30, 1.16-1.47). No significant association was found with ICU admission (1.00, 0.83-1.19).CONCLUSIONS:We found an association between cirrhosis and 90-day mortality and rehospitalization in older patients with pneumonia. We suggest that physicians should carefully monitor patients with cirrhosis who develop pneumonia.

BACKGROUND:The use of e-cigarettes is increasing in the US but there is still a paucity of research on the metabolic effects of e-cigarette use. The goal of this work was to determine the association between e-cigarette use and self-reported prediabetes in adult never cigarette smokers.METHOD:The 2017 cross sectional Behavioral Risk Factor Surveillance System (BRFSS) survey data was used for the analysis. Current e-cigarette users reported daily or someday use of e-cigarettes and former e-cigarette users reported no current use of e-cigarettes. Participants who reported a history of diabetes, gestational prediabetes/ diabetes were excluded. Odds ratios were calculated to determine the association between e-cigarette use and self-reported prediabetes in never cigarette smokers after adjusting for potential confounders.RESULTS:There were a total of 154,404 participants that met the inclusion criteria. Of those participants, there were 143,952 never, 1339 current and 7625 former e-cigarette users. Current e-cigarette users had an increased odds of reporting a diagnosis of prediabetes 1.97 (95% CI 1.25-3.10) compared to never e-cigarette users. After stratifying by gender, men and women had an increased odds ratio of reporting a diagnosis of prediabetes 2.36 (95% CI 1.26-4.40) and 1.88 (95% CI 1.00-3.53) respectively when compared to never e-cigarette users. There was no association between former e-cigarette use and a self-reported diagnosis of prediabetes.CONCLUSION:Our findings show that e-cigarette use may be associated with self-reported prediabetes. Further evaluation is needed in prospective studies.

The use of electronic cigarettes (e-cigarettes) has increased in the US, but little is known about the effects of these products on lung health. The main purpose of this study was to examine the association between e-cigarette use and a participant’s report of being diagnosed with chronic obstructive pulmonary disease (COPD) in a nationally representative sample of adults. Methods: The first wave of the Population Assessment of Tobacco and Health (PATH) survey adult data was used (N = 32,320). Potential confounders between e-cigarette users and non-users were balanced using propensity score matching. Odds ratios (OR) were calculated to examine the association between e-cigarette use and COPD in the propensity-matched sample, the entire sample, different age groups, and in nonsmokers. Replicate weights and balanced repeated replication methods were utilized to account for the complex survey design. Results: Of the 3642 participants who met the criteria for e-cigarette use, 2727 were propensity matched with 2727 non e-cigarette users. In the propensity-matched sample, e-cigarette users were more likely to report being diagnosed with COPD (OR 1.43, 95% confidence interval [CI] 1.12–1.85) than non-e-cigarette users after adjusting for confounders. The result was similar in the entire sample and in the different age subgroups. Among nonsmokers, the odds of reporting a COPD diagnosis were even greater among e-cigarette users (OR 2.94, 95% CI 1.73–4.99) compared to non-e-cigarette users. Conclusion: Our findings demonstrate that e-cigarette use was associated with a reported diagnosis of COPD among adults in the US. Further research is necessary to characterize the nature of this association and on the long-term effects of using e-cigarettes.

PURPOSE The authors propose a method whereby serially acquired DCE-MRI, DW-MRI, and FDG-PET breast data sets can be spatially and temporally coregistered to enable the comparison of changes in parameter maps at the voxel level. METHODS First, the authors aligned the PET and MR images at each time point rigidly and nonrigidly. To register the MR images longitudinally, the authors extended a nonrigid registration algorithm by including a tumor volume-preserving constraint in the cost function. After the PET images were aligned to the MR images at each time point, the authors then used the transformation obtained from the longitudinal registration of the MRI volumes to register the PET images longitudinally. The authors tested this approach on ten breast cancer patients by calculating a modified Dice similarity of tumor size between the PET and MR images as well as the bending energy and changes in the tumor volume after the application of the registration algorithm. RESULTS The median of the modified Dice in the registered PET and DCE-MRI data was 0.92. For the longitudinal registration, the median tumor volume change was -0.03% for the constrained algorithm, compared to -32.16% for the unconstrained registration algorithms (p = 8 × 10(-6)). The medians of the bending energy were 0.0092 and 0.0001 for the unconstrained and constrained algorithms, respectively (p = 2.84 × 10(-7)). CONCLUSIONS The results indicate that the proposed method can accurately spatially align DCE-MRI, DW-MRI, and FDG-PET breast images acquired at different time points during therapy while preventing the tumor from being substantially distorted or compressed.

PurposeThe purpose of this pilot study is to determine (1) if early changes in both semiquantitative and quantitative DCE-MRI parameters, observed after the first cycle of neoadjuvant chemotherapy in breast cancer patients, show significant difference between responders and nonresponders and (2) if these parameters can be used as a prognostic indicator of the eventual response.MethodsTwenty-eight patients were examined using DCE-MRI pre-, post-one cycle, and just prior to surgery. The semiquantitative parameters included longest dimension, tumor volume, initial area under the curve, and signal enhancement ratio related parameters, while quantitative parameters included K-trans, v(e), k(ep), v(p), and (i) estimated using the standard Tofts-Kety, extended Tofts-Kety, and fast exchange regime models.ResultsOur preliminary results indicated that the signal enhancement ratio washout volume and k(ep) were significantly different between pathologic complete responders from nonresponders (P<0.05) after a single cycle of chemotherapy. Receiver operator characteristic analysis showed that the AUC of the signal enhancement ratio washout volume was 0.75, and the AUCs of k(ep) estimated by three models were 0.78, 0.76, and 0.73, respectively.ConclusionIn summary, the signal enhancement ratio washout volume and k(ep) appear to predict breast cancer response after one cycle of neoadjuvant chemotherapy. This observation should be confirmed with additional prospective studies. Magn Reson Med 71:1592-1602, 2014. (c) 2013 Wiley Periodicals, Inc.

a Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA b Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, USA c Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, USA d Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA e Department of Cancer Biology, Vanderbilt University, Nashville, TN 37232, USA f Translational and Molecular Imaging Institute, Mt. Sinai Medical Center, New York, NY 10029, USA g Department of Radiology, Mt. Sinai Medical Center, New York, NY 10029, USA h Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA i Department of Neurosurgery, Vanderbilt University, Nashville, TN 37232, USA j Department of Cardiology, Mt. Sinai Medical Center, New York, NY 10029, USA k Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA

Current mathematical models of tumor growth are limited in their clinical application because they require input data that are nearly impossible to obtain with sufficient spatial resolution in patients even at a single time point--for example, extent of vascularization, immune infiltrate, ratio of tumor-to-normal cells, or extracellular matrix status. Here we propose the use of emerging, quantitative tumor imaging methods to initialize a new generation of predictive models. In the near future, these models could be able to forecast clinical outputs, such as overall response to treatment and time to progression, which will provide opportunities for guided intervention and improved patient care.