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Background Anger and extreme stress can trigger potentially fatal cardiovascular events in susceptible people. Political elections, such as the 2016 US presidential election, are significant stressors. Whether they can trigger cardiac arrhythmias is unknown. Methods and Results In this retrospective case‐crossover study, we linked cardiac device data, electronic health records, and historic voter registration records from 2436 patients with implanted cardiac devices. The incidence of arrhythmias during the election was compared with a control period with Poisson regression. We also tested for effect modification by demographics, comorbidities, political affiliation, and whether an individual's political affiliation was concordant with county‐level election results. Overall, 2592 arrhythmic events occurred in 655 patients during the hazard period compared with 1533 events in 472 patients during the control period. There was a significant increase in the incidence of composite outcomes for any arrhythmia (incidence rate ratio [IRR], 1.77 [95% CI, 1.42–2.21]), supraventricular arrhythmia (IRR, 1.82 [95% CI, 1.36–2.43]), and ventricular arrhythmia (IRR, 1.60 [95% CI, 1.22–2.10]) during the election relative to the control period. There was also an increase in specific types of arrhythmia, including atrial fibrillation (IRR, 1.50 [95% CI, 1.06–2.11]), supraventricular tachycardia (IRR, 3.7 [95% CI, 2.2–6.2]), nonsustained ventricular tachycardia (IRR, 1.7 [95% CI, 1.3–2.2]), and daily atrial fibrillation burden ( P Conclusions There was a significant increase in cardiac arrhythmias during the 2016 US presidential election. These findings suggest that exposure to stressful sociopolitical events may trigger arrhythmogenesis in susceptible people.

In modern scientific research, data are often collected from multiple modalities. Since different modalities could provide complementary information, statistical prediction methods using multi-modality data could deliver better prediction performance than using single modality data. However, one special challenge for using multi-modality data is related to block-missing data. In practice, due to dropouts or the high cost of measures, the observations of a certain modality can be missing completely for some subjects. In this paper, we propose a new DIrect Sparse regression procedure using COvariance from Multi-modality data (DISCOM). Our proposed DISCOM method includes two steps to find the optimal linear prediction of a continuous response variable using block-missing multi-modality predictors. In the first step, rather than deleting or imputing missing data, we make use of all available information to estimate the covariance matrix of the predictors and the cross-covariance vector between the predictors and the response variable. The proposed new estimate of the covariance matrix is a linear combination of the identity matrix, the estimates of the intra-modality covariance matrix and the cross-modality covariance matrix. Flexible estimates for both the sub-Gaussian and heavy-tailed cases are considered. In the second step, based on the estimated covariance matrix and the estimated cross-covariance vector, an extended Lasso-type estimator is used to deliver a sparse estimate of the coefficients in the optimal linear prediction. The number of samples that are effectively used by DISCOM is the minimum number of samples with available observations from two modalities, which can be much larger than the number of samples with complete observations from all modalities. The effectiveness of the proposed method is demonstrated by theoretical studies, simulated examples, and a real application from the Alzheimer’s Disease Neuroimaging Initiative. The comparison between DISCOM and some existing methods also indicates the advantages of our proposed method.

Considerable lesion-specific response heterogeneity exists in metastatic colorectal cancer patients, largely due to organ-specific ecological environments and evolutionary pressures. Metastatic lesions with poor response to therapy often become tumor sanctuary sites, leading to systemic resistance and tumor relapse. To map the lesion-specific response and relapse patterns, we investigated the longitudinal dynamics of individual lesions in metastatic colorectal cancer patients. Tumor longitudinal data in 4,308 colorectal cancer patients with 40,612 individual lesions were collected from eight Phase III trials in Project Data Sphere. First, tumor response dynamics (regression after treatment and progression upon resistance) were characterized using an empirical mathematical model. Next, tumor response time (when the lesion size decreases ≥20% from baseline) and relapse time (when the lesion size increases ≥30% from tumor nadir) were estimated for each individual lesion in patients being treated with bevacizumab, panitumumab, and/or chemotherapy. Random effect cox proportional models were applied to predict lesion-specific response and relapse probabilities and temporal sequence. We then took machine learning algorithm k-means to cluster patients based on their lesion relapse sequence. We found the response probabilities across organs are: Liver > Distal Lymph Nodes (LN) > Abdomen > Spleen > Lung > Regional LN > Adrenal > Muscle/Soft Tissue > Bone > Brain/CNS. Lesion relapse temporal sequence are: Brain/CNS > Liver > Adrenal > Muscle/Soft tissue > Abdomen > Bone > Spleen > Lung > Distal LN > Regional LN. Of note, lesions in the bone, brain, adrenal, and muscle/soft tissues often had low responses and high relapse probabilities, implying the greatest potential as tumor sanctuary sites. Liver, the most common metastatic organ in colorectal cancer, showed highest response rate but high relapse probabilities. Interestingly, the organ-specific response rate and relapse probabilities are respectively in line with drug distribution profiles and organ-specific immune landscape. Organ-specific relapse sequence in each patient is significantly correlated with patient long-term survival (p Citation Format: Jiawei Zhou, Quefeng Li, Amber Cipriani, Yanguang Cao. Mapping lesion specific response and relapse patterns in metastatic colorectal cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3789.

Multiple types of data measured on a common set of subjects arise in many areas. Numerous empirical studies have found that integrative analysis of such data can result in better statistical performance in terms of prediction and feature selection. However, the advantages of integrative analysis have mostly been demonstrated empirically. In the context of two-class classification, we propose an integrative linear discriminant analysis method and establish a theoretical guarantee that it achieves a smaller classification error than running linear discriminant analysis on each data type individually. We address the issues of outliers and missing values, frequently encountered in integrative analysis, and illustrate our method through simulations and a neuroimaging study of Alzheimer's disease.

PURPOSE: The use of traditional, complementary, and alternative medicine (TCAM) for cancer may influence the delivery or effectiveness of conventional cancer treatment. In this systematic review, we aimed to 1.) summarize the available prevalence data on traditional medicine use by cancer patients in less developed countries (LDCs), and 2.) stratify the prevalence data by world region and country income level. METHODS: A literature search for cancer, TCAM, and low income (LI) and lower-middle income (LMI) countries was conducted across 5 databases. A total of 2,365 publications were reviewed for eligibility, of which 25 studies met inclusion criteria. RESULTS: The combined sample size was 6,878 cancer patients, with a median of 54.5% reporting the use of TCAM for cancer care. Of the studies providing data on the concomitant use of TCAM and conventional cancer treatment (n = 4,872 cancer patients), a median of 26.7% of participants reported combining the two systems of medicine. CONCLUSION: From the data available, it is apparent that TCAM use among cancer patients in less developed countries is common; however, additional studies are needed to support the safe and effective management of cancer for patients in LI and LMI countries.

Aim: Determine if incentive spirometry (IS) values correlate with postoperative pain control. Design: Prospective observational study. Setting & participants: A total of 100 patients undergoing major abdominal procedures at the University of North Carolina Medical Center. Interventions: Patients studied as a single cohort. All patients received thoracic epidural analgesia preoperatively. Outcome: Preoperative and daily postoperative numeric pain scores, subjective pain description and IS values were collected for all patients. Results: There was a strong correlation with IS values relative to baseline for both the numeric pain scores (p

HIV-infected individuals have a known increased risk of sudden cardiac death (SCD) compared to uninfected individuals. Implantable cardioverter defibrillators (ICDs) are standard therapy for preventing sudden arrhythmic death; however, there is limited data on the use and outcomes of ICDs in HIV-infected individuals. This is a retrospective cohort study of 35 consecutive HIV- Infected patients and 36 uninfected controls matched by age, race, and gender who were treated at University of North Carolina, Chapel Hill Medical Center in the outpatient or inpatient setting from 2014 to the present and had undergone primary or secondary prevention ICD implantation. For HIV-positive subjects, multivariate Poisson regression analysis was performed to evaluate the association between covariates and ICD therapies. Among HIV-infected subjects, mean CD4 count was 582.5 cells/mm3 and 69 % had an undetectable viral load. The median followup was 6.4 years. There was a trend towards longer corrected QT interval at the time of ICD implantation in HIV-infected subjects compared to uninfected controls (469.7 +/- 35.8 vs. 454.7 +/- 35.7 msec, p=0.089). HIV-infected subjects had both a higher number of appropriate ICD shocks or anti-tachycardia pacing (ATP) therapy per person-year as well as a higher number of inappropriate ICD shocks per person-year than uninfected controls (1.512 vs. 0.590 and 0.122 vs. 0.0166 respectively, p

Multimodal data, where different types of data are collected from the same subjects, are fast emerging in a large variety of scientific applications. Factor analysis is commonly used in integrative analysis of multimodal data, and is particularly useful to overcome the curse of high dimensionality and high correlations. However, there is little work on statistical inference for factor analysis based supervised modeling of multimodal data. In this article, we consider an integrative linear regression model that is built upon the latent factors extracted from multimodal data. We address three important questions: how to infer the significance of one data modality given the other modalities in the model; how to infer the significance of a combination of variables from one modality or across different modalities; and how to quantify the contribution, measured by the goodness-of-fit, of one data modality given the others. When answering each question, we explicitly characterize both the benefit and the extra cost of factor analysis. Those questions, to our knowledge, have not yet been addressed despite wide use of factor analysis in integrative multimodal analysis, and our proposal bridges an important gap. We study the empirical performance of our methods through simulations, and further illustrate with a multimodal neuroimaging analysis.

Introduction: Hospital length of stay with transcatheter aortic valve replacements (TAVRs) has decreased, though the rate of heart block requiring permanent pacemaker (PPM) implantation after TAVR has remained steady. It is unknown whether more patients are being readmitted for PPM after discharge from TAVR. Objective: To explore frequency, timing, and risk factors for PPM implant after TAVR in a nationally representative database. Methods: Patients who underwent TAVR from January 2012 through December 2017 were identified in the Nationwide Readmissions Database (NRD). Smoothing splines and logistic regression were used to analyze trends in length of stay and timing of PPM implantation after TAVR respectively. Multivariable logistic regression analysis was performed to identify risk factors for overall, early (during index hospitalization), and late (during subsequent hospitalization) PPM after TAVR. Results: Of the 62,083 included, 6,817 (11.0%) underwent PPM implantation [6,137 (9.9%) early and 680 (1.1%) late]. Rates of PPM remained stable between 8% and 12.5% with an increasing proportion occurring late (7% in 2012 increasing to 13% in 2017, p < 0.0001 for trend) (Figure 1). Late PPM was associated with atrial fibrillation (p < 0.01), diabetes mellitus (p < 0.001), chronic kidney disease (p < 0.05), second degree AVB (p < 0.001), left bundle branch block (p < 0.001), right bundle branch block (p < 0.001), and bifascicular block (p < 0.001). Conclusions: There has been a significant increase in the proportion of patients requiring readmission for PPM implantation after TAVR. As this high-risk population grows, algorithms for extended in-hospital observation or ambulatory cardiac monitoring post-TAVR are needed to reduce the risk for adverse events.

The sum of target lesions is routinely used to evaluate patient objective responses to treatment in the RECIST criteria, but it neglects the response heterogeneity across metastases. This study argues that the spatiotemporal response heterogeneity across metastases informs drug efficacy and patient survival. We analyzed the longitudinal data of 11,404 metastatic lesions in 2,802 colorectal cancer patients and examined their response heterogeneity. The response dynamics of metastatic lesions varied broadly across anatomical locations and therapies. High inter-lesion heterogeneity is associated with worse survival (p < 0.001), while targeted therapies (bevacizumab or panitumumab) reduced the inter-lesion heterogeneity (p < 0.05) and elicited more favorable effects on liver lesions (p < 0.001) than chemotherapy alone. The responses of liver lesions predicted patient survival more significantly than the lesions in the lungs and lymph nodes. Altogether, the high spatiotemporal heterogeneity across metastases should be integrated into current methods for treatment evaluation and patient prognosis.SignificanceThe spatiotemporal heterogeneity across metastases in response to first-line therapies in colorectal cancer is informative for drug efficacy and patient survival, particularly in targeted therapy. Our findings provide evidence to support the inclusion of individual lesion response in the RECIST to improve the assessment of drug efficacy and patient survival.

The objective of this study is to document the prevalence of traditional, complementary, and alternative medicine (TCAM) use by adult cancer patients at a national teaching hospital in Malawi. We aim to document the products/therapies used, the reason for use, as well as patient-reported satisfaction with TCAM practitioners and modalities.We conducted investigator-administered interviews with adult cancer patients presenting to the Kamuzu Central Hospital (KCH) Cancer Clinic in Lilongwe, Malawi between January and July 2018. The KCH is a national teaching hospital in the capital of Lilongwe, which serves patients with cancer from the northern half of Malawi. Descriptive statistics were used to describe TCAM use and logistic regression was applied to identify predictors of TCAM.A total of 263 participants completed the survey, of which 70% (n = 183) were female and average age was 45 (SD 14) years old. The prevalence of overall TCAM use was 84% (n = 222), and 60% (n = 157) of participants reported combining TCAM with conventional cancer treatment. The majority of patients used TCAM to directly treat their cancer versus for symptom management. Patients reported using faith-based healing (64%, n = 168), herbal medicine (56%, n = 148), diet change (46%, n = 120), and vitamins/minerals (23%, n = 61). Participants reported the highest satisfaction for physicians among practitioners and diet change for modalities. Female gender was found to be a predictor of TCAM with conventional treatment use, no other significant predictors were observed.There is a high prevalence of TCAM use among an adult population with cancer in Malawi, and a wide variety in the TCAM modalities used among patients. Additional studies are needed to identify risks and benefits of TCAM use to assist with policy and public health, patient safety, and holistically address the global burden of cancer.

BACKGROUND: More rapid fluid removal during hemodialysis is associated with adverse cardiovascular outcomes and longer dialysis recovery times. The effect of ultrafiltration (UF) profiling, independent of concomitant sodium profiling, on markers of intradialytic hemodynamics and other outcomes has been inadequately studied. METHODS: Four-phase, blinded crossover trial. Participants (UF rates >10 mL/h/kg) were assigned in random order to receive hemodialysis with UF profiling (constantly declining UF rate, intervention) vs. hemodialysis with conventional UF (control). Each 3-week 9-treatment period was followed by a 1-week 3-treatment washout period. Participants crossed into each study arm twice (2 phases/arm); 18 treatments per treatment type. The primary outcomes were intradialytic hypotension, pre- to post-dialysis troponin T change, and change from baseline in left ventricular global longitudinal strain. Other outcomes included intradialytic symptoms and blood volume measured-plasma refill (post-dialysis volume status measure), among others. Each participant served as their own control. RESULTS: On average, the 34 randomized patients (mean age 56 years, 24% female, mean dialysis vintage 6.3 years) had UF rates >10 mL/h/kg in 56% of treatments during the screening period. All but 2 patients completed the 15-week study (prolonged hospitalization, kidney transplant). There was no significant difference in intradialytic hypotension, troponin T change, or left ventricular strain between hemodialysis with UF profiling and conventional UF. With UF profiling, participants had significantly lower odds of light-headedness and plasma refill compared to hemodialysis with conventional UF. CONCLUSIONS: UF profiling did not reduce the odds of treatment-related cardiac stress but did reduce the odds of light-headedness and post-dialysis hypervolemia. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT03301740 (registered October 4, 2017)

To identify associations with either early or late permanent pacemaker (PPM) implantation after transcatheter aortic valve replacement (TAVR) in order to develop an easily interpretable management algorithm.Injury to the conduction system after TAVR occasionally requires PPM. There is limited data on how to identify which patients will require PPM, particularly after discharge from index hospitalization after TAVR.All patients having undergone TAVR at the University of North Carolina through August 2019 were identified and records were manually reviewed. Multivariable analyses were performed to identify associations with post-TAVR PPM due to high-degree atrioventricular block (HAVB). Comparisons were made between patients with no PPM (n = 304) and PPM required, stratified into early (during index hospitalization, n = 32) and late (during subsequent hospitalization, n = 11) PPM cohorts.Of the 347 patents included for analysis, 43 (12.4%) underwent post-TAVR PPM. In multivariable regression models, early PPM was associated with baseline bifascicular block (OR: 42.16; p .001), requiring any pacing on first post-TAVR electrocardiogram (ECG) (OR: 31.55; p .001), and valve oversizing15% (OR: 3.61; p .05). Late PPM was associated with baseline right bundle branch block (RBBB) (OR 12.62; p .001) and history of atrial fibrillation/flutter (OR 4.83; p .05).Bifascicular block, any pacing on first post-TAVR ECG, and15% valve oversizing are associated with early PPM, while RBBB and history of atrial fibrillation/flutter are associated with late PPM. We suggest a management strategy for post-TAVR surveillance and management of HAVB.

Study objective Our study sought to determine whether or not nitrous oxide analgesia decreases pain compared to oxygen placebo during external cephalic version for breech presentation. Procedural success may be limited by pain and anxiety. Although neuraxial anesthesia has been studied extensively for these procedures, many centers lack resources for routine use. Nitrous oxide is noninvasive, has minimal side effects and requires limited facilities. We hypothesized that its analgesic properties would decrease pain compared to oxygen placebo during external cephalic version. Design Double-blinded randomized placebo-controlled trial. Setting Labor and delivery triage room. Patients Forty-eight patients, ≥18 years of age, 37-weeks' gestation or beyond, singleton pregnancy, breech presentation, and American Society of Anesthesiology physical status I-III, having an external cephalic version. Interventions Patients undergoing external cephalic version were randomized to receive self-administered 50% nitrous oxide/50% oxygen versus 100% oxygen placebo. Measurements The primary outcome measured was intra-procedural pain. Secondary outcomes were intra-procedural anxiety, patient satisfaction, and procedure difficulty. Main results Forty-eight patients were enrolled; 23 received nitrous oxide and 25 received oxygen. No difference was noted in mean pain scores (nitrous oxide 5.5 ± 2.3, placebo 5.4 ± 2.7, [CI95% = −1.40, 1.51]; P = 0.943) or anxiety scores (nitrous oxide 1.6 ± 2.0, placebo 1.2 ± 1.8, [CI95% = −0.74, 1.45]; P = 0.515). Procedural difficulty (1–10 scale, 1 = very easy, 10 = extremely difficult) was not different between groups (nitrous oxide 6.1 ± 2.2, placebo 6.1 ± 3.2, [CI95% = −1.54, 1.66]; P = 0.944). There was a significant increase in the number of version attempts in the nitrous oxide group (nitrous oxide 3.9 ± 1.9, placebo 2.8 ± 1.4, [CI95% = 0.05, 2]; P = 0.046). Patient satisfaction was significantly lower in the nitrous oxide group (nitrous oxide 4.3 ± 4.0, placebo 6.9 ± 3.6, [CI95% = −4.93, −0.34]; P = 0.025). Conclusion Despite the desirable properties of nitrous oxide, there was no analgesic benefit over oxygen for external cephalic version. Its routine use for these procedures was not supported.

Converging evidence shows that Alzheimer’s disease (AD) is a neurodegenerative disease that represents a disconnection syndrome, whereby a large-scale brain network is progressively disrupted by one or more neuropathological processes. However, the mechanism by which pathological entities spread across a brain network is largely unknown. Since pathological burden may propagate trans-neuronally, we propose to characterize the propagation pattern of neuropathological events spreading across relevant brain networks that are regulated by the organization of the network. Specifically, we present a novel mixed-effect model to quantify the relationship between longitudinal network alterations and neuropathological events observed at specific brain regions, whereby the topological distance to hub nodes, high-risk AD genetics, and environmental factors (such as education) are considered as predictor variables. Similar to many cross-section studies, we find that AD-related neuropathology preferentially affects hub nodes. Furthermore, our statistical model provides strong evidence that abnormal neuropathological burden diffuses from hub nodes to non-hub nodes in a prion-like manner, whereby the propagation pattern follows the intrinsic organization of the large-scale brain network.

Data from a large number of covariates with known population totals are frequently observed in survey studies. These auxiliary variables contain valuable information that can be incorporated into estimation of the population total of a survey variable to improve the estimation precision. We consider the generalized regression estimator formulated under the model-assisted framework in which a regression model is utilized to make use of the available covariates while the estimator still has basic design-based properties. The generalized regression estimator has been shown to improve the efficiency of the design-based Horvitz-Thompson estimator when the number of covariates is fixed. In this study, we investigate the performance of the generalized regression estimator when the number of covariates p is allowed to diverge as the sample size n increases. We examine two approaches where the model parameter is estimated using the weighted least squares method when p < n and the LASSO method when the model parameter is sparse. We show that under an assisted model and certain conditions on the joint distribution of the covariates as well as the divergence rates of n and p, the generalized regression estimator is asymptotically more efficient than the Horvitz-Thompson estimator, and is robust against model misspecification. We also study the consistency of variance estimation for the generalized regression estimator. Our theoretical results are corroborated by simulation studies and an example.

Background Mechanisms underlying oral immunotherapy (OIT) are unclear and the effects on immune cells at varying maintenance doses are unknown. Objective We aimed to determine the immunologic changes caused by peanut OIT in preschool aged children and determine the effect on these immune responses in groups ingesting low or high-dose peanut OIT (300 mg or 3000 mg, respectively) as maintenance therapy. Methods Blood was drawn at several time-points throughout the OIT protocol and PBMCs isolated and cultured with peanut antigens. Secreted cytokines were quantified via multiplex assay, whereas Treg and peanut-responsive CD4 T cells were studied with flow cytometry. Basophil activation assays were also conducted. Results Th2-, Th1-, Th9- and Tr1-type cytokines decreased over the course of OIT in groups on high- and low-dose OIT. There were no significant differences detected in cytokine changes between the high- and low-dose groups. The initial increase in both the number of peanut-responsive CD4 T cells and the number of Tregs was transient and no significant differences were found between groups. Basophil activation following peanut stimulation was decreased over the course of OIT and associated with increased peanut-IgG4/IgE ratios. No differences were found between high- and low-dose groups in basophil activation at the time of desensitization or sustained unresponsiveness oral food challenges. Conclusions and clinical relevance Peanut OIT leads to decreases in pro-allergic cytokines, including IL-5, IL-13, and IL-9 and decreased basophil activation. No differences in T cell or basophil responses were found between subjects on low or high-dose maintenance OIT, which has implications for clinical dosing strategies.

In the genomic era, the identification of gene signatures associated with disease is of significant interest. Such signatures are often used to predict clinical outcomes in new patients and aid clinical decision-making. However, recent studies have shown that gene signatures are often not replicable. This occurrence has practical implications regarding the generalizability and clinical applicability of such signatures. To improve replicability, we introduce a novel approach to select gene signatures from multiple datasets whose effects are consistently non-zero and account for between-study heterogeneity. We build our model upon some rank-based quantities, facilitating integration over different genomic datasets. A high dimensional penalized Generalized Linear Mixed Model (pGLMM) is used to select gene signatures and address data heterogeneity. We compare our method to some commonly used strategies that select gene signatures ignoring between-study heterogeneity. We provide asymptotic results justifying the performance of our method and demonstrate its advantage in the presence of heterogeneity through thorough simulation studies. Lastly, we motivate our method through a case study subtyping pancreatic cancer patients from four gene expression studies.

Introduction Physical activity and sedentary behavior are major risk factors for chronic disease. These behaviors may change at retirement, with implications for health in later life. The study objective was to describe longitudinal patterns of moderate to vigorous and domain-specific physical activity and TV watching by retirement status. Methods Participants in the Multi-Ethnic Study of Atherosclerosis (n=6,814) were recruited from six U.S. communities and were aged 45–84 years at baseline. Retirement status and frequency and duration of domain-specific physical activity (recreational walking, transport walking, non-walking leisure activity, caregiving, household, occupational/volunteer) and TV watching were self-reported at four study exams (2000 to 2012). Fixed effect linear regression models were used to describe longitudinal patterns in physical activity and TV watching by retirement status overall and stratified by socioeconomic position. Analyses were conducted in 2017. Results Of 4,091 Multi-Ethnic Study of Atherosclerosis participants not retired at baseline, 1,012 (25%) retired during a median of 9 years follow-up. Retirement was associated with a 10% decrease (95% CI= –15%, –5%) in moderate to vigorous physical activity and increases of 13% to 29% in recreational walking, household activity, and TV watching. Among people of low socioeconomic position, the magnitude of association was larger for moderate to vigorous physical activity. Among people of high socioeconomic position, the magnitude of association was larger for non-walking leisure and household activity. Conclusions The retirement transition was associated with changes in physical activity and TV watching. To inform intervention development, future research is needed on the determinants of behavior change after retirement, particularly among individuals of low socioeconomic position.

Background Virtual Reality (VR) is used as an effective tool for distraction and as an adjunct for pain management. This study was conducted to compare VR to standard iPad use after surgery and examine its effect on pain score and opioid consumption. Methods This was a randomized controlled study, with stratification by surgery type, age group (7-12yo, 13-18yo) and gender. Pain and anxiety were assessed with validated scales (STAI, FACES, VAS, FLACC) and outcomes were compared between each group. Results 50 of the 106 enrolled patients used the VR device. After adjusting for age, gender, and STAI, patients had a decreased FLACC score while using the VR device compared to the iPad group (odds ratio 2.95, P = .021). The younger patients were found to have lower FLACC scores while using the VR device (odds ratio 1.15, p=0.044); this finding was most significant when patients used the VR device for 20-30 minutes (odds ratio 1.67, P = .0003). Additionally, after adjusting for treatment group, gender, and STAI, the younger patients had higher odds of withdrawal or exclusion from the study (odds ratio 1.18, P = .021). No significant difference in opioid consumption between the groups was found. Discussion Virtual reality was well tolerated and more effective in decreasing pain during the immediate postoperative period than iPad use. Despite a slightly higher withdrawal rate, younger patients benefited more from the intervention.

In the era of big data, integrative analyses that pool data from different sources are now extensively conducted in order to improve performance. Among many interesting applications, genomics research is an area where integrative methods become popular tools to identify prognostic biomarkers for various diseases. In this paper, we propose such a framework for pathway and gene identification. Our method employs a hierarchical decomposition on genes’ effects followed by a proper regularization to identify important pathways and genes across multiple studies. Asymptotic theories are provided to show that our method is both pathway and gene selection consistent. More importantly, we explicitly show that pathway selection consistency needs milder statistical conditions than gene selection consistency, as it would allow false positives and negatives at the gene selection level. Finite-sample performance of our method is shown to be superior than other ad hoc methods in various simulation studies. We further apply our method to analyze five cardiovascular disease studies. Our method is intrinsically a general method on group-wise and element-wise selections from integrative analysis, which can have other applications beyond genomic research.

Background Peanut sublingual immunotherapy (SLIT) for 1 year has been shown to induce modest clinical desensitization in allergic children. Studies of oral immunotherapy, epicutaneous immunotherapy, and SLIT have suggested additional benefit with extended treatment. Objective We sought to investigate the safety, clinical effectiveness, and immunologic changes with long-term SLIT in children with peanut allergy. Methods Children with peanut allergy aged 1 to 11 years underwent extended maintenance SLIT with 2 mg/d peanut protein for up to 5 years. Subjects with peanut skin test wheals of less than 5 mm and peanut-specific IgE levels of less than 15 kU/L were allowed to discontinue therapy early. Desensitization was assessed through a double-blind, placebo-controlled food challenge (DBPCFC) with up to 5000 mg of peanut protein after completion of SLIT dosing. Sustained unresponsiveness was further assessed by using identical DBPCFCs after 2 to 4 weeks without peanut exposure. Results Thirty-seven of 48 subjects completed 3 to 5 years of peanut SLIT, with 67% (32/48) successfully consuming 750 mg or more during DBPCFCs. Furthermore, 25% (12/48) passed the 5000-mg DBPCFC without clinical symptoms, with 10 of these 12 demonstrating sustained unresponsiveness after 2 to 4 weeks. Side effects were reported with 4.8% of doses, with transient oropharyngeal itching reported most commonly. Side effects requiring antihistamine treatment were uncommon (0.21%), and no epinephrine was administered. Peanut skin test wheals, peanut-specific IgE levels, and basophil activation decreased significantly, and peanut-specific IgG4 levels increased significantly after peanut SLIT. Conclusion Extended-therapy peanut SLIT provided clinically meaningful desensitization in the majority of children with peanut allergy that was balanced with ease of administration and a favorable safety profile.

Several studies have sought to identify novel transcriptional biomarkers in normal breast or breast microenvironment to predict tumor risk and prognosis. However, systematic efforts to evaluate intra-individual variability of gene expression within normal breast have not been reported. This study analyzed the microarray gene expression data of 288 samples from 170 women in the Normal Breast Study (NBS), wherein multiple histologically normal breast samples were collected from different block regions and different sections at a given region. Intra-individual differences in global gene expression and selected gene expression signatures were quantified and evaluated in association with other patient-level factors. We found that intra-individual reliability was relatively high in global gene expression, but differed by signatures, with composition-related signatures (i.e., stroma) having higher intra-individual variability and tumorigenesis-related signatures (i.e., proliferation) having lower intra-individual variability. Histological stroma composition was the only factor significantly associated with heterogeneous breast tissue (defined as > median intra-individual variation; high nuclear density, odds ratio [OR] = 3.42, 95% confidence interval [CI] = 1.15–10.15; low area, OR = 0.29, 95% CI = 0.10–0.86). Other factors suggestively influencing the variability included age, BMI, and adipose nuclear density. Our results underscore the importance of considering intra-individual variability in tissue-based biomarker development, and have important implications for normal breast research.

High-dimensional data are often most plausibly generated from distributions with complex structure and leptokurtosis in some or all components. Covariance and precision matrices provide a useful summary of such structure, yet the performance of popular matrix estimators typically hinges upon a sub-Gaussianity assumption. This paper presents robust matrix estimators whose performance is guaranteed for a much richer class of distributions. The proposed estimators, under a bounded fourth moment assumption, achieve the same minimax convergence rates as do existing methods under a sub-Gaussianity assumption. Consistency of the proposed estimators is also established under the weak assumption of bounded [Formula: see text] moments for [Formula: see text]. The associated convergence rates depend on [Formula: see text].

Low adherence to anti-hypertensive medications contributes to worse outcomes. The authors conducted a secondary data analysis to examine the effects of a health-coaching intervention on medication adherence and blood pressure (BP), and to explore whether changes in medication adherence over time were associated with changes in BP longitudinally in 477 patients with hypertension. Data regarding medication adherence and BP were collected at baseline, 6, 12, 18, and 24 months. The intervention resulted in increases in medication adherence (5.75→5.94, P = .04) and decreases in diastolic BP (81.6→76.1 mm Hg, P < .001) over time. The changes in medication adherence were associated with reductions in diastolic BP longitudinally (P = .047). Patients with low medication adherence at baseline had significantly greater improvement in medication adherence and BP over time than those with high medication adherence. The intervention demonstrated improvements in medication adherence and diastolic BP and offers promise as a clinically applicable intervention in rural primary care.

Factor modeling is an essential tool for exploring intrinsic dependence structures among high-dimensional random variables. Much progress has been made for estimating the covariance matrix from a high-dimensional factor model. However, the blessing of dimensionality has not yet been fully embraced in the literature: much of the available data are often ignored in constructing covariance matrix estimates. If our goal is to accurately estimate a covariance matrix of a set of targeted variables, shall we employ additional data, which are beyond the variables of interest, in the estimation? In this article, we provide sufficient conditions for an affirmative answer, and further quantify its gain in terms of Fisher information and convergence rate. In fact, even an oracle-like result (as if all the factors were known) can be achieved when a sufficiently large number of variables is used. The idea of using data as much as possible brings computational challenges. A divide-and-conquer algorithm is thus proposed to alleviate the computational burden, and also shown not to sacrifice any statistical accuracy in comparison with a pooled analysis. Simulation studies further confirm our advocacy for the use of full data, and demonstrate the effectiveness of the above algorithm. Our proposal is applied to a microarray data example that shows empirical benefits of using more data. Supplementary materials for this article are available online.

To facilitate comparative treatment selection when there is substantial heterogeneity of treatment effectiveness, it is important to identify subgroups that exhibit differential treatment effects. Existing approaches model outcomes directly and then define subgroups according to interactions between treatment and covariates. Because outcomes are affected by both the covariate-treatment interactions and covariate main effects, direct modeling outcomes can be hard due to model misspecification, especially in presence of many covariates. Alternatively one can directly work with differential treatment effect estimation. We propose such a method that approximates a target function whose value directly reflects correct treatment assignment for patients. The function uses patient outcomes as weights rather than modeling targets. Consequently, our method can deal with binary, continuous, time-to-event, and possibly contaminated outcomes in the same fashion. We first focus on identifying only directional estimates from linear rules that characterize important subgroups. We further consider estimation of comparative treatment effects for identified subgroups. We demonstrate the advantages of our method in simulation studies and in analyses of two real data sets.

We prospectively evaluated adolescent organized physical activity (PA) as a factor in adult female bone traits. Annual DXA scans accompanied semiannual records of anthropometry, maturity, and PA for 42 participants in this preliminary analysis (criteria: appropriately timed DXA scans at ~1 year premenarche [predictor] and ~5 years postmenarche [dependent variable]). Regression analysis evaluated total adolescent interscan PA and PA over 3 maturity subphases as predictors of young adult bone outcomes: 1) bone mineral content (BMC), geometry, and strength indices at nondominant distal radius and femoral neck; 2) subhead BMC; 3) lumbar spine BMC. Analyses accounted for baseline gynecological age (years pre- or postmenarche), baseline bone status, adult body size and interscan body size change. Gymnastics training was evaluated as a potentially independent predictor, but did not improve models for any outcomes (p < .07). Premenarcheal bone traits were strong predictors of most adult outcomes (semipartial r2 = .21-0.59, p < .001). Adult 1/3 radius and subhead BMC were predicted by both total PA and PA 1-3 years postmenarche (p < .03). PA 3-5 years postmenarche predicted femoral narrow neck width, endosteal diameter, and buckling ratio (p < .05). Thus, participation in organized physical activity programs throughout middle and high school may reduce lifetime fracture risk in females.

Summary Data subject to heavy-tailed errors are commonly encountered in various scientific fields. To address this problem, procedures based on quantile regression and least absolute deviation regression have been developed in recent years. These methods essentially estimate the conditional median (or quantile) function. They can be very different from the conditional mean functions, especially when distributions are asymmetric and heteroscedastic. How can we efficiently estimate the mean regression functions in ultrahigh dimensional settings with existence of only the second moment? To solve this problem, we propose a penalized Huber loss with diverging parameter to reduce biases created by the traditional Huber loss. Such a penalized robust approximate (RA) quadratic loss will be called the RA lasso. In the ultrahigh dimensional setting, where the dimensionality can grow exponentially with the sample size, our results reveal that the RA lasso estimator produces a consistent estimator at the same rate as the optimal rate under the light tail situation. We further study the computational convergence of the RA lasso and show that the composite gradient descent algorithm indeed produces a solution that admits the same optimal rate after sufficient iterations. As a by-product, we also establish the concentration inequality for estimating the population mean when there is only the second moment. We compare the RA lasso with other regularized robust estimators based on quantile regression and least absolute deviation regression. Extensive simulation studies demonstrate the satisfactory finite sample performance of the RA lasso.

Objective Lower health literacy is associated with poorer health outcomes. Few interventions poised to mitigate the impact of health literacy in hypertensive patients have been published. We tested if a multi-level quality improvement intervention could differentially improve Systolic Blood Pressure (SBP) more so in patients with low vs. higher health literacy. Methods We conducted a non-randomized prospective cohort trial of 525 patients referred with uncontrolled hypertension. Stakeholder informed and health literacy sensitive strategies were implemented at the practice and patient level. Outcomes were assessed at 0, 6, 12, 18 and 24 months. Results At 12 months, the low and higher health literacy groups had statistically significant decreases in mean SBP (6.6 and 5.3 mmHg, respectively), but the between group difference was not significant (Δ 1.3 mmHg, P = 0.067). At 24 months, the low and higher health literacy groups reductions were 8.1 and 4.6 mmHg, respectively, again the between group difference was not significant (Δ 3.5 mmHg, p = 0.25). Conclusions/practice implications A health literacy sensitive multi-level intervention may equally lower SBP in patients with low and higher health literacy. Practical health literacy appropriate tools and methods can be implemented in primary care settings using a quality improvement approach.

Recent advance in biotechnology and its wide applications have led to the generation of many high-dimensional gene expression data sets that can be used to address similar biological questions. Meta-analysis plays an important role in summarizing and synthesizing scientific evidence from multiple studies. When the dimensions of datasets are high, it is desirable to incorporate variable selection into meta-analysis to improve model interpretation and prediction. According to our knowledge, all existing methods conduct variable selection with meta-analyzed data in an "all-in-or-all-out" fashion, that is, a gene is either selected in all of studies or not selected in any study. However, due to data heterogeneity commonly exist in meta-analyzed data, including choices of biospecimens, study population, and measurement sensitivity, it is possible that a gene is important in some studies while unimportant in others. In this article, we propose a novel method called meta-lasso for variable selection with high-dimensional meta-analyzed data. Through a hierarchical decomposition on regression coefficients, our method not only borrows strength across multiple data sets to boost the power to identify important genes, but also keeps the selection flexibility among data sets to take into account data heterogeneity. We show that our method possesses the gene selection consistency, that is, when sample size of each data set is large, with high probability, our method can identify all important genes and remove all unimportant genes. Simulation studies demonstrate a good performance of our method. We applied our meta-lasso method to a meta-analysis of five cardiovascular studies. The analysis results are clinically meaningful.

Background To test whether the vitamin D status of anesthesia department caregivers practicing at high Northern latitudes is compatible with current recommendations, the 25-hydroxyvitamin D (25(OH)D) levels of caregivers at hospitals in Iceland (64°08′ N) and in Wisconsin (43°07′ N) were compared at the end of winter. Methods Anesthesia department faculty and resident physicians, non-physician anesthetists, and critical care nurses completed a questionnaire, and provided blood samples for analysis of 25(OH)D by reverse-phase high performance liquid chromatography. Results One hundred and six participants in Iceland and 124 participants in Wisconsin were enrolled. No difference in mean serum 25(OH)D levels between Iceland [70.53 nmol/l, standard deviation (SD) 30.87 nmol/l] and Wisconsin (70.0 nmol/l, SD 30.0 nmol/l) was observed. In Iceland and Wisconsin, 25(OH)D levels below 25 nmol/l were observed in 4.7% and 4.0%, below 50 nmol/l in 34.9% and 25.0%, and below 75 nmol/l in 56.6% and 61.3% of caregivers, respectively. Conclusions 25(OH)D levels below the 50 nmol/l (20 ng/ml) threshold recommended by the Institute of Medicine and the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis, and below the 75 nmol/l (30 ng/ml) threshold recommended by The Endocrine Society, are highly prevalent among anesthesia caregivers working at two Northern hospitals at the end of winter who may otherwise not meet criteria to be tested. Anesthesia and critical care providers may wish to determine their 25(OH)D levels and use effective, safe, and low cost supplementation to target a 25(OH)D level compatible with optimal health.

Background: Lower adherence to anti-hypertensive medications contributes to sub-optimal patient outcomes, yet there are few successful interventions in rural primary care that target improved adherence. The purpose of this study was to determine whether a multi-component quality improvement intervention that included literacy-sensitive health coaching with motivational interviewing was associated with improved medication adherence and reductions in blood pressure (BP) in patients with a history of uncontrolled hypertension (HTN). Methods: Adult patients in six rural primary care settings with one or more visits in the last year with a systolic BP > 150 mmHg were recruited. Project faculty facilitated systematic changes in care delivery in local practices. Patients also received monthly phone-based literacy-sensitive health coaching including a focus on medication adherence, and a BP cuff for home monitoring. Data regarding medication adherence (Morisky Medication Adherence Scale-8) and BP were collected at baseline, 6, 12, 18, and 24 months. Linear mixed effects modeling was used to determine the effects of the multi-component intervention on medication adherence and whether changes in medication adherence were associated with changes in systolic and diastolic BP. Results: There were 477 patients enrolled; the majority were female, black, and reported an annual household income of < $40,000. At baseline, 39% of the patients had low medication adherence (MMAS-8 score < 6). In linear mixed effects models, the intervention resulted in modest increases in medication adherence [5.75 ± 1.37 at baseline to 5.94 ± 1.33 at 24 months (p = .04)]. Corresponding changes in BP were: from 138.6 ± 21.8/81.6 ± 12.9 mmHg at baseline to 132.7 ± 19.5/76.1 ± 14.5 mmHg at 24 months follow-up [mean 0.22-0.25/0.24-0.26 mmHg per month before and after adjustment for covariates (p < .001)]. Changes in medication adherence were significantly associated with reductions in diastolic BP longitudinally (p = .047). Conclusion: A practice-based quality improvement intervention that includes health coaching is associated with improvements in medication adherence and BP, and offers promise as a clinically applicable intervention in rural primary care.

We examined site-specific bone development in relation to childhood and adolescent artistic gymnastics exposure, comparing up to 10years of prospectively acquired longitudinal data in 44 subjects, including 31 non-gymnasts (NON) and 13 gymnasts (GYM) who participated in gymnastics from pre-menarche to ≥1.9years post-menarche. Subjects underwent annual regional and whole-body DXA scans; indices of bone geometry and strength were calculated. Anthropometrics, physical activity, and maturity were assessed annually, coincident with DXA scans. Non-linear mixed effect models centered growth in bone outcomes at menarche and adjusted for menarcheal age, height, and non-bone fat-free mass to evaluate GYM-NON differences. A POST-QUIT variable assessed the withdrawal effect of quitting gymnastics. Curves for bone area, mass (BMC), and strength indices were higher in GYM than NON at both distal radius metaphysis and diaphysis (p

Many contemporary studies involve the classification of a subject into two classes based on n observations of the p variables associated with the subject. Under the assumption that the variables are normally distributed, the well-known linear discriminant analysis (LDA) assumes a common covariance matrix over the two classes while the quadratic discriminant analysis (QDA) allows different covariance matrices. When p is much smaller than n, even if they both diverge, the LDA and QDA have the smallest asymptotic misclassification rates for the cases of equal and unequal covariance matrices, respectively. However, modern statistical studies often face classification problems with the number of variables much larger than the sample size n, and the classical LDA and QDA can perform poorly. In fact, we give an example in which the QDA performs as poorly as random guessing even if we know the true covariances. Under some sparsity conditions on the unknown means and covariance matrices of the two classes, we propose a sparse QDA based on thresholding that has the smallest asymptotic misclassification rate conditional on the training data. We discuss an example of classifying normal and tumor colon tissues based on a set of p = 2; 000 genes and a sample of size n = 62, and another example of a cardiovascular study for n = 222 subjects with p = 2; 434 genes. A simulation is also conducted to check the performance of the proposed method.

LASSO for variable selection in linear regression has been studied by many authors. To achieve asymptotic selection consistency, it is well known that the LASSO method requires a strong irrepresentable condition. Even adding a thresholding step after LASSO is still too conservative, especially when the number of explanatory variables p is much larger than the number of observations n. Another well-known method, the sure independence screening (SIS), applies thresholding to an estimator of marginal covariate effect vector and, therefore, is not selection consistent unless the zero components of the marginal covariate effect vector are asymptotically the same as the zero components of the regression effect vector. Since the weakness of LASSO is caused by the fact that it utilizes the covariate sample covariance matrix that is not well behaved when p is larger than n, we propose a regularized LASSO (RLASSO) method for replacing the covariate sample covariance matrix in LASSO by a regularized estimator of covariate covariance matrix and adding a thresholding step. Using a regularized estimator of covariate covariance matrix, we can consistently estimate the regression effects and, hence, our method also extends and improves the SIS method that estimates marginal covariate effects. We establish selection consistency of RLASSO under conditions that the regression effect vector is sparse and the covariate covariance matrix or its inverse is sparse. Some simulation results for comparing variable selection performances of RLASSO and various other methods are presented. A data example is also provided.