Bayesian network-based Mendelian randomization for variant prioritization and phenotypic causal inference.

Mendelian randomization is a powerful method for inferring causal relationships. However, obtaining suitable genetic instrumental variables is often challenging due to gene interaction, linkage, and pleiotropy. We propose Bayesian network-based Mendelian randomization (BNMR), a Bayesian causal learning and inference framework using individual-level data. BNMR employs the random graph forest, an ensemble Bayesian network structural learning process, to prioritize candidate genetic variants and select appropriate instrumental variables, and then obtains a pleiotropy-robust estimate by incorporating a shrinkage prior in the Bayesian framework. Simulations demonstrate BNMR can efficiently reduce the false-positive discoveries in variant selection, and outperforms existing MR methods in terms of accuracy and statistical power in effect estimation. With application to the UK Biobank, BNMR exhibits its capacity in handling modern genomic data, and reveals the causal relationships from hematological traits to blood pressures and psychiatric disorders. Its effectiveness in handling complex genetic structures and modern genomic data highlights the potential to facilitate real-world evidence studies, making it a promising tool for advancing our understanding of causal mechanisms.

Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects.

BACKGROUND: Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI to AD is desired but, to date, remains challenging. Here, we developed an interpretable deep learning model featuring a novel design that incorporates interaction effects and multimodality to improve the prediction accuracy and horizon for MCI-to-AD progression. METHODS: This multi-center, multi-cohort retrospective study collected structural magnetic resonance imaging (sMRI), clinical assessments, and genetic polymorphism data of 252 patients with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our deep learning model was cross-validated on the ADNI-1 and ADNI-2/GO cohorts and further generalized in the ongoing ADNI-3 cohort. We evaluated the model performance using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score. RESULTS: On the cross-validation set, our model achieved superior results for predicting MCI conversion within 4 years (AUC, 0.962; accuracy, 92.92%; sensitivity, 88.89%; specificity, 95.33%) compared to all existing studies. In the independent test, our model exhibited consistent performance with an AUC of 0.939 and an accuracy of 92.86%. Integrating interaction effects and multimodal data into the model significantly increased prediction accuracy by 4.76% (P = 0.01) and 4.29% (P = 0.03), respectively. Furthermore, our model demonstrated robustness to inter-center and inter-scanner variability, while generating interpretable predictions by quantifying the contribution of multimodal biomarkers. CONCLUSIONS: The proposed deep learning model presents a novel perspective by combining interaction effects and multimodality, leading to more accurate and longer-term predictions of AD progression, which promises to improve pre-dementia patient care.

A generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints.

A basket trial simultaneously evaluates a treatment in multiple cancer subtypes, offering an effective way to accelerate drug development in multiple indications. Many basket trials are designed and monitored based on a single efficacy endpoint, primarily the tumor response. For molecular targeted or immunotherapy agents, however, a single efficacy endpoint cannot adequately characterize the treatment effect. It is increasingly important to use more complex endpoints to comprehensively assess the risk-benefit profile of such targeted therapies. We extend the calibrated Bayesian hierarchical modeling approach to monitor phase II basket trials with multiple endpoints. We propose two generalizations, one based on the latent variable approach and the other based on the multinomial-normal hierarchical model, to accommodate different types of endpoints and dependence assumptions regarding information sharing. We introduce shrinkage parameters as functions of statistics measuring homogeneity among subgroups and propose a general calibration approach to determine the functional forms. Theoretical properties of the generalized hierarchical models are investigated. Simulation studies demonstrate that the monitoring procedure based on the generalized approach yields desirable operating characteristics.

Survival prediction of stomach cancer using expression data and deep learning models with histopathological images.

Accurately predicting patient survival is essential for cancer treatment decision. However, the prognostic prediction model based on histopathological images of stomach cancer patients is still yet to be developed. We propose a deep learning-based model (MultiDeepCox-SC) that predicts overall survival in patients with stomach cancer by integrating histopathological images, clinical data, and gene expression data. The MultiDeepCox-SC not only automatedly selects patches with more information for survival prediction, without manual labeling for histopathological images, but also identifies genetic and clinical risk factors associated with survival in stomach cancer. The prognostic accuracy of the MultiDeepCox-SC (C-index = 0.744) surpasses the result only based on histopathological image (C-index = 0.660). The risk score of our model was still an independent predictor of survival outcome after adjustment for potential confounders, including pathologic stage, grade, age, race, and gender on The Cancer Genome Atlas dataset (hazard ratio 1.555, p = 3.53e-08) and the external test set (hazard ratio 2.912, p = 9.42e-4). Our fully automated online prognostic tool based on histopathological images, clinical data, and gene expression data could be utilized to improve pathologists' efficiency and accuracy (https://yu.life.sjtu.edu.cn/DeepCoxSC).

Particulate matter of air pollution may increase risk of kidney failure in IgA nephropathy.

IgA nephropathy (IgAN) is characterized by deposition of galactose-deficient IgA1 (Gd-IgA1) in glomerular mesangium associated with mucosal immune disorders. Since environmental pollution has been associated with the progression of chronic kidney disease in the general population, we specifically investigated the influence of exposure to fine particulate matter less than 2.5 µm in diameter (PM2.5) on IgAN progression. Patients with biopsy-proven primary IgAN were recruited from seven Chinese kidney centers. PM2.5 exposure from 1998 to 2016 was derived from satellite aerosol optical depth data and a total of 1,979 patients with IgAN, including 994 males were enrolled. The PM2.5 exposure levels for patients from different provinces varied but, in general, the PM2.5 exposure levels among patients from the north were higher than those among patients from the south. The severity of PM2.5 exposure in different regions was correlated with regional kidney failure burden. In addition, each 10 µg/m3 increase in annual average concentration of PM2.5 exposure before study entry (Hazard Ratio, 1.14; 95% confidence interval, 1.06-1.22) or time-varying PM2.5 exposure after study entry (1.10; 1.01-1.18) were associated with increased kidney failure risk after adjustment for age, gender, estimated glomerular filtration rate, urine protein, uric acid, hemoglobin, mean arterial pressure, Oxford classification, glucocorticoid and renin-angiotensin system blocker therapy. The associations were robust when the time period, risk factors of cardiovascular diseases or city size were further adjusted on the basis of the above model. Thus, our results suggest that PM2.5 is an independent risk factor for kidney failure in patients with IgAN, but these findings will require validation in more diverse populations and other geographic regions.

Eye-Preserving Therapies for Advanced Retinoblastoma: A Multicenter Cohort of 1678 Patients in China.

PURPOSE: This study attempted to estimate the impact of eye-preserving therapies for the long-term prognosis of patients with advanced retinoblastoma with regard to overall survival and ocular salvage. DESIGN: Retrospective cohort study covering all 31 provinces (38 retinoblastoma treating centers) of mainland China. PARTICIPANTS: One thousand six hundred seventy-eight patients diagnosed with group D or E retinoblastoma from January 2006 through May 2016. METHODS: Chart review was performed. The patients were divided into primary enucleation and eye-preserving groups, and they were followed up for survival status. The impact of initial treatment on survival was evaluated by Cox analyses. MAIN OUTCOME MEASURES: Overall survival and final eye preservation. RESULTS: After a median follow-up of 43.9 months, 196 patients (12%) died, and the 5-year overall survival was 86%. In total, the eyeball preservation rate was 48%. In this cohort, 1172 patients (70%) had unilateral retinoblastoma, whereas 506 patients (30%) had bilateral disease. For patients with unilateral disease, 570 eyes (49%) underwent primary enucleation, and 602 patients (51%) received eye-preserving therapies initially. During the follow-up (median, 45.6 months), 59 patients (10%) from the primary enucleation group and 56 patients (9.3%) from the eye-preserving group died. Multivariate Cox analyses indicated no significant difference in overall survival between the 2 groups (hazard ratio [HR], 1.25; 95% confidence interval [CI], 0.85-1.84; P = 0.250). For patients with bilateral disease, 95 eyes (19%) underwent primary enucleation, and 411 patients (81%) received eye-preserving therapies initially. During the follow-up (median, 40.1 months), 12 patients (13%) from the primary enucleation group and 69 patients (17%) from the eye-preserving group died. For bilateral retinoblastoma with the worse eye classified as group E, patients undergoing primary enucleation exhibited better overall survival (HR, 2.35; 95% CI, 1.10-5.01; P = 0.027); however, this survival advantage was not evident until passing 22.6 months after initial diagnosis. CONCLUSIONS: Eye-preserving therapies have been used widely for advanced retinoblastoma in China. Patients with bilateral disease whose worse eye was classified as group E and who initially underwent eye-preserving therapies exhibited a worse overall survival. The choice of primary treatment for advanced retinoblastoma should be weighed carefully.

A kernel regression model for panel count data with nonparametric covariate functions.

The local kernel pseudo-partial likelihood is employed for estimation in a panel count model with nonparametric covariate functions. An estimator of the derivative of the nonparametric covariate function is derived first, and the nonparametric function estimator is then obtained by integrating the derivative estimator. Uniform consistency rates and pointwise asymptotic normality are obtained for the local derivative estimator under some regularity conditions. Moreover, the baseline function estimator is shown to be uniformly consistent. Demonstration of the asymptotic results strongly relies on the modern empirical theory, which generally does not require the Poisson assumption. Simulation studies also illustrate that the local derivative estimator performs well in a finite-sample regardless of whether the Poisson assumption holds. We also implement the proposed methodology to analyze a clinical study on childhood wheezing.

BOB: Bayesian optimal design for biosimilar trials with co-primary endpoints.

For regulatory approval of a biosimilar product, extensive evaluations should be performed by rigorous clinical trials to establish the similarity between the reference product and the proposed biosimilar in terms of both efficacy and safety. Existing designs for biosimilar trials often use a single primary efficacy endpoint in trial monitoring, and then separately evaluate the safety of the biosimilar product in a secondary analysis at the trial completion. However, ignoring the safety endpoint and the correlation between safety and efficacy in trial monitoring may lead to a high false positive rate, or it may delay the termination of the trial when dissimilarity in safety is early detected. We propose a Bayesian optimal design for biosimilar trials by incorporating both safety and efficacy endpoints in a unified framework. Based on a Bayesian joint safety and efficacy model, we sequentially use a so-called Bayesian biosimilar probability to make go/no-go decisions. We calibrate the Bayesian design to maximize the statistical power while maintaining the frequentist type I error rate at the nominal level. We carry out extensive simulation studies to show that the design has desirable performance in terms of the false positive rate and the average sample size. We also apply the proposed design to a biosimilar trial evaluating a ranibizumab product.

Association of China's two-child policy with changes in number of births and birth defects rate, 2008-2017.

BACKGROUND: In October 2015, China's one-child policy was universally replaced by a so-called two-child policy. This study investigated the association between the enactment of the new policy and changes in the number of births, and health-related birth outcomes. METHODS: We used difference-in-difference model to analyse the birth record data in Pudong New Area, Shanghai.The design is descriptive before-and-after comparative study. RESULTS: The data covered three policy periods: the one-child policy period (January 2008 to November 2014); the partial two-child policy period (December 2014 to June 2016); the universal two-child policy period (July 2016 to December 2017). There was an estimate of 7656 additional births during the 18 months of the implementation of the universal two-child policy. The trend of monthly percentage of births to mothers aged ≥35 increased by 0.24 percentage points (95% confidence interval 0.19 to 0.28, p < 0.001) during the same period. Being a baby boy, preterm birth, low birth weight, parents with lower educational attainment, and assisted delivery were associated with a higher risk of birth defects. CONCLUSIONS: The universal two-child policy was associated with an increase in the number of births and maternal age. Preterm birth, low birth weight, and assisted delivery were associated with a higher risk of birth defects, which suggested that these infants needed additional attention in the future.

High-dimensional mediation analysis in survival models.

Mediation analysis with high-dimensional DNA methylation markers is important in identifying epigenetic pathways between environmental exposures and health outcomes. There have been some methodology developments of mediation analysis with high-dimensional mediators. However, high-dimensional mediation analysis methods for time-to-event outcome data are still yet to be developed. To address these challenges, we propose a new high-dimensional mediation analysis procedure for survival models by incorporating sure independent screening and minimax concave penalty techniques for variable selection, with the Sobel and the joint method for significance test of indirect effect. The simulation studies show good performance in identifying correct biomarkers, false discovery rate control, and minimum estimation bias of the proposed procedure. We also apply this approach to study the causal pathway from smoking to overall survival among lung cancer patients potentially mediated by 365,307 DNA methylations in the TCGA lung cancer cohort. Mediation analysis using a Cox proportional hazards model estimates that patients who have serious smoking history increase the risk of lung cancer through methylation markers including cg21926276, cg27042065, and cg26387355 with significant hazard ratios of 1.2497(95%CI: 1.1121, 1.4045), 1.0920(95%CI: 1.0170, 1.1726), and 1.1489(95%CI: 1.0518, 1.2550), respectively. The three methylation sites locate in the three genes which have been showed to be associated with lung cancer event or overall survival. However, the three CpG sites (cg21926276, cg27042065 and cg26387355) have not been reported, which are newly identified as the potential novel epigenetic markers linking smoking and survival of lung cancer patients. Collectively, the proposed high-dimensional mediation analysis procedure has good performance in mediator selection and indirect effect estimation.

Promotion time cure rate model with a neural network estimated nonparametric component.

Promotion time cure rate models (PCM) are often used to model the survival data with a cure fraction. Medical images or biomarkers derived from medical images can be the key predictors in survival models. However, incorporating images in the PCM is challenging using traditional nonparametric methods such as splines. We propose to use neural network to model the nonparametric or unstructured predictors' effect in the PCM context. Expectation-maximization algorithm with neural network for the M-step is used for parameter estimation. Asymptotic properties of the proposed estimates are derived. Simulation studies show good performance in terms of both prediction and estimation. We finally apply our methods to analyze the brain images from open access series of imaging studies data.

Opioid Use as a Predictor of Pain Outcomes in Iraq and Afghanistan Veterans with Chronic Pain: Analysis of a Randomized Controlled Trial.

OBJECTIVE: Our objectives were to: 1) assess the relationship between self-reported opioid use and baseline demographics, clinical characteristics and pain outcomes; and 2) examine whether baseline opioid use moderated the intervention effect on outcomes at 9 months. DESIGN: We conducted a secondary analysis of data from the Evaluation of Stepped Care for Chronic Pain (ESCAPE) trial, which found stepped-care to be effective for chronic pain in military veterans. SETTING: A post-deployment clinic and five general medicine clinics at a Veteran Affairs Medical Center. SUBJECTS: In total 241 veterans with chronic musculoskeletal pain; 220 with complete data at 9 months. METHODS: Examination of baseline relationships and multivariable linear regression to examine baseline opioid use as a moderator of pain-related outcomes including Roland Morris Disability Questionnaire (RMDQ), Brief Pain Inventory (BPI) Interference scale, and Graded Chronic Pain Scale (GCPS) at 9 months. RESULTS: Veterans reporting baseline opioid use (n = 80) had significantly worse RMDQ (16.0 ± 4.9 vs. 13.4 ± 4.2, P < .0001), GCPS (68.7 ± 12.0 vs. 65.0 ± 14.4, P = .049), BPI Interference (6.2 ± 2.2 vs. 5.0 ± 2.1, P < .0001), and depression (PHQ-9 12.5 ± 6.2 vs. 10.6 ± 5.7, P = .016) compared to veterans not reporting baseline opioid use. Using multivariable modeling we found that baseline opioid use moderated the intervention effect on pain-related disability (RMDQ) at 9 months (interaction Beta = -3.88, P = .0064) but not pain intensity or interference. CONCLUSIONS: In a stepped-care trial for pain, patients reporting baseline opioid use had greater improvement in pain disability at 9 months compared to patients not reporting opioid use.

Multifocal Pain as a Predictor of Pain Outcomes in Military Veterans with Chronic Musculoskeletal Pain: A Secondary Data Analysis of a Randomized Controlled Trial.

OBJECTIVE: We aimed to examine 1) the relationship between multifocal pain and clinical characteristics, including demographics, pain outcomes, somatic symptoms, health-related quality of life, depression, and anxiety, and 2) whether multifocal pain was independently associated with treatment response. METHODS: We conducted a secondary data analysis on veterans with chronic pain enrolled in the Evaluation of Stepped Care for Chronic Pain (ESCAPE) trial with complete data at 9 months (n = 222). We examined baseline relationships and used multivariable linear regression to examine whether multifocal pain was independently associated with outcomes that included Brief Pain Inventory (BPI) Interference scale and Graded Chronic Pain Scale (GCPS) scores between baseline and 9 months. RESULTS: The sample had a mean BPI Interference score of 5.3 ± 2.2 and a mean GCPS score of 65.6 ± 13.7, 55% had significant depression (Patient Health Questionnaire 9-item depression scale [PHQ-9] score of ≥10), and 42% had significant anxiety (Generalized Anxiety Disorder Scale [GAD-7] score of ≥10). Veterans reporting three or more pain sites (the "more diffuse pain" group) had significantly less improvement on GCPS (b = 4.6, standard error [SE] = 2.3, P = 0.045), BPI Interference (b = 1.0, SE = 0.2, P = 0.0011), and health-related quality of life (Short-Form 36-item scale, Physical Component Summary) (b = 4.1, SE = 1.0, P < 0.0001) than did veterans reporting fewer than three pain sites (the "less diffuse pain" group). More diffuse pain was not associated with changes in PHQ-9 or GAD-7 scores. CONCLUSIONS: Multifocal pain predicted worse pain outcomes between baseline and 9 months in veterans enrolled in a trial for treating chronic musculoskeletal pain.

Pain and Psychological Outcomes Among Iraq and Afghanistan Veterans with Chronic Pain and PTSD: ESCAPE Trial Longitudinal Results.

OBJECTIVE: To compare pain and psychological outcomes in veterans with chronic musculoskeletal pain and comorbid post-traumatic stress disorder (PTSD) or pain alone and to determine if veterans with comorbidity respond differently to a stepped-care intervention than those with pain alone. DESIGN: Secondary analysis of data from the Evaluation of Stepped Care for Chronic Pain (ESCAPE) trial. SETTING: Six Veterans Health Affairs clinics. SUBJECTS: Iraq and Afghanistan veterans (N = 222) with chronic musculoskeletal pain. METHODS: Longitudinal analysis of veterans with chronic musculoskeletal pain and PTSD or pain alone and available baseline and nine-month trial data. Participants randomized to either usual care or a stepped-care intervention were analyzed. The pain-PTSD comorbidity group screened positive for PTSD and had a PTSD Checklist-Civilian score ≥41 at baseline. RESULTS: T tests demonstrated statistically significant differences and worse outcomes on pain severity, pain cognitions, and psychological outcomes in veterans with comorbid pain and PTSD compared with those with pain alone. Analysis of covariance (ANCOVA) modeling change scores from baseline to nine months indicated no statistically significant differences, controlling for PTSD, on pain severity, pain centrality, or pain self-efficacy. Significant differences emerged for pain catastrophizing (t = 3.10, P < 0.01), depression (t = 3.39, P < 0.001), and anxiety (t = 3.80, P < 0.001). The interaction between PTSD and the stepped-care intervention was not significant. CONCLUSIONS: Veterans with the pain-PTSD comorbidity demonstrated worse pain and psychological outcomes than those with chronic pain alone. These findings indicate a more intense chronic pain experience for veterans when PTSD co-occurs with pain. PTSD did not lead to a differential response to a stepped-care intervention.

Linear and nonlinear variable selection in competing risks data.

Subdistribution hazard model for competing risks data has been applied extensively in clinical researches. Variable selection methods of linear effects for competing risks data have been studied in the past decade. There is no existing work on selection of potential nonlinear effects for subdistribution hazard model. We propose a two-stage procedure to select the linear and nonlinear covariate(s) simultaneously and estimate the selected covariate effect(s). We use spectral decomposition approach to distinguish the linear and nonlinear parts of each covariate and adaptive LASSO to select each of the 2 components. Extensive numerical studies are conducted to demonstrate that the proposed procedure can achieve good selection accuracy in the first stage and small estimation biases in the second stage. The proposed method is applied to analyze a cardiovascular disease data set with competing death causes.

Apurinic/apyrimidinic endonuclease/redox factor-1 (APE1/Ref-1) redox function negatively regulates NRF2.

Apurinic/apyrimidinic endonuclease/redox factor-1 (APE1/Ref-1) (henceforth referred to as Ref-1) is a multifunctional protein that in addition to its base excision DNA repair activity exerts redox control of multiple transcription factors, including nuclear factor κ-light chain enhancer of activated B cells (NF-κB), STAT3, activator protein-1 (AP-1), hypoxia-inducible factor-1 (HIF-1), and tumor protein 53 (p53). In recent years, Ref-1 has emerged as a promising therapeutic target in cancer, particularly in pancreatic ductal carcinoma. Although a significant amount of research has centered on Ref-1, no wide-ranging approach had been performed on the effects of Ref-1 inhibition and transcription factor activity perturbation. Starting with a broader approach, we identified a previously unsuspected effect on the nuclear factor erythroid-related factor 2 (NRF2), a critical regulator of cellular defenses against oxidative stress. Based on genetic and small molecule inhibitor-based methodologies, we demonstrated that repression of Ref-1 potently activates NRF2 and its downstream targets in a dose-dependent fashion, and that the redox, rather than the DNA repair function of Ref-1 is critical for this effect. Intriguingly, our results also indicate that this pathway does not involve reactive oxygen species. The link between Ref-1 and NRF2 appears to be present in all cells tested in vitro, noncancerous and cancerous, including patient-derived tumor samples. In particular, we focused on understanding the implications of the novel interaction between these two pathways in primary pancreatic ductal adenocarcinoma tumor cells and provide the first evidence that this mechanism has implications for overcoming the resistance against experimental drugs targeting Ref-1 activity, with clear translational implications.

Patient Health Questionnaire Anxiety and Depression Scale: Initial Validation in Three Clinical Trials.

OBJECTIVE: We examine the reliability and validity of the Patient Health Questionnaire Anxiety and Depression Scale (PHQ-ADS)-which combines the nine-item Patient Health Questionnaire depression scale and seven-item Generalized Anxiety Disorder scale-as a composite measure of depression and anxiety. METHODS: Baseline data from 896 patients enrolled in two primary-care based trials of chronic pain and one oncology-practice-based trial of depression and pain were analyzed. The internal reliability, standard error of measurement, and convergent, construct, and factor structure validity, as well as sensitivity to change of the PHQ-ADS were examined. RESULTS: The PHQ-ADS demonstrated high internal reliability (Cronbach α values of .8 to .9) in all three trials. PHQ-ADS scores can range from 0 to 48 (with higher scores indicating more severe depression/anxiety), and the estimated standard error of measurement was approximately 3 to 4 points. The PHQ-ADS showed strong convergent (most correlations, 0.7-0.8 range) and construct (most correlations, 0.4-0.6 range) validity when examining its association with other mental health, quality of life, and disability measures. PHQ-ADS cutpoints of 10, 20, and 30 indicated mild, moderate, and severe levels of depression/anxiety, respectively. Bifactor analysis showed sufficient unidimensionality of the PHQ-ADS score. PHQ-ADS change scores at 3 months differentiated (p < .0001) between individuals classified as worse, stable, or improved by a reference measure, providing preliminary evidence for sensitivity to change. CONCLUSIONS: The PHQ-ADS may be a reliable and valid composite measure of depression and anxiety which, if validated in other populations, could be useful as a single measure for jointly assessing two of the most common psychological conditions in clinical practice and research. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT00926588 (SCOPE); NCT00386243 (ESCAPE); NCT00313573 (INCPAD).

Comparative Responsiveness of the PROMIS Pain Interference Short Forms, Brief Pain Inventory, PEG, and SF-36 Bodily Pain Subscale.

PURPOSE: To compare the sensitivity to change and the responsiveness to intervention of the PROMIS Pain Interference short forms, Brief Pain Inventory (BPI), 3-item PEG scale, and SF-36 Bodily Pain subscale in a sample of patients with persistent musculoskeletal pain of moderate severity. METHODS: Standardized response means, standardized effect sizes, and receiver operating curve analyses were used to assess change between baseline and 3-month assessments in 250 participants who participated in a randomized clinical effectiveness trial of collaborative telecare management for moderate to severe and persistent musculoskeletal pain. RESULTS: The BPI, PEG, and SF-36 Bodily Pain measures were more sensitive to patient-reported global change than the PROMIS Pain Interference short forms, especially for the clinically improved group, for which the change detected by the PROMIS short forms was not statistically significant. The BPI was more responsive to the clinical intervention than the SF-36 Bodily Pain and PROMIS Pain Interference measures. Post hoc analyses exploring these findings did not suggest that differences in content or rating scale structure (number of response options or anchoring language) adequately explained the observed differences in the detection of change. CONCLUSIONS: In this clinical trial, the BPI and PEG measures were better able to detect change than the SF-36 Bodily Pain and PROMIS Pain Interference measures.

Opioid Use as a Predictor of Health Care Use and Pain Outcomes: Analysis of Clinical Trial Data.

OBJECTIVE: To examine effects of pre-enrollment opioid use on outcomes of a 12-month collaborative pain care management trial. We hypothesized that participants with opioid use would have worse pain at baseline; use more health care services and analgesics; and have worse pain outcomes during the trial. DESIGN: Secondary analysis of randomized controlled trial data. SETTING: Veterans Affairs (VA) primary care. SUBJECTS: Patients age 18-65 years with chronic pain of at least moderate severity who were enrolled in a 12-month pragmatic trial of a telephone-based collaborative care intervention for chronic musculoskeletal pain. METHODS: Participants were categorized as opioid users (n = 84) or non-users (n = 166) at baseline and trial randomization was stratified by opioid use. We used logistic regression to examine cross-sectional associations with baseline opioid use and mixed-effect models for repeated measures to examine baseline opioid use as a predictor of Brief Pain Inventory (BPI) scores over 12 months. RESULTS: At baseline, 33.6% reported use of prescribed opioids. Baseline opioid users had higher baseline BPI scores and higher health-related disability than non-users. Baseline opioid users also had more outpatient visits (15.0 vs. 10.1; p = 0.001) and received more analgesics (p < 0.001) during the trial. In the final multivariable model examining effects of baseline opioid use on BPI over 12 months, opioid users and nonusers had a non-significant difference of 0.25 points (p = 0.098). In conclusion, although baseline opioid users had worse pain at baseline and used more health care during the study, response to the intervention was not significantly modified by pre-existing opioid therapy.

Exhaled nitric oxide during infancy as a risk factor for asthma and airway hyperreactivity.

Childhood asthma is often characterised by elevated exhaled nitric oxide (eNO), decreased lung function, increased airway reactivity and atopy; however, our understanding of when these phenotypic airway characteristics develop remains unclear. This study evaluated whether eNO, lung function, airway reactivity and immune characteristics during infancy are risk factors of asthma at age 5 years. Infants with eczema, enrolled prior to wheezy illness (n=116), had eNO, spirometry, airway reactivity and allergen sensitisation assessed at entry to the study and repeated at age 5 years (n=90). Increasing eNO at entry was associated with an increased risk of asthma (p=0.037) and increasing airway reactivity (p=0.015) at age 5 years. Children with asthma at 5 years of age had a greater increase in eNO between infancy and age 5 years compared with those without asthma (p=0.002). Egg sensitisation at entry was also associated with an increased risk of asthma (p=0.020), increasing eNO (p = 0.002) and lower forced expiratory flows (p=0.029) as a 5 year-old. Our findings suggest that, among infants at high risk for developing asthma, eNO early in life may provide important insights into the subsequent risk of asthma and its airway characteristics.