Association between dynamic dose increases of buprenorphine for treatment of opioid use disorder and risk of relapse

BACKGROUND AND AIMS

Dynamic, adaptive pharmacologic treatment for opioid use disorder (OUD) has been previously recommended over static dosing to prevent relapse, and is aligned with personalized medicine. However, there has been no quantitative evidence demonstrating its advantage. Our objective was to estimate the extent to which a hypothetical intervention that increased buprenorphine dose in response to opioid use would affect risk of relapse over 24 weeks of follow-up.

DESIGN

A secondary analysis of the buprenorphine arm of an open-label randomized controlled 24-week comparative effectiveness trial, 2014-17.

SETTING

Eight community addiction treatment programs in the United States.

PARTICIPANTS

English-speaking adults with DSM-5 OUD, recruited during inpatient admission (n = 270). Participants were mainly white (65%) and male (72%).

INTERVENTION(S)

Participants were treated with daily sublingual buprenorphine-naloxone (BUP-NX), with dose based on clinical indication, determined by the provider. We examined a hypothetical intervention of increasing dose in response to opioid use.

MEASUREMENTS

Outcome was relapse to regular opioid use during the 24 weeks of outpatient treatment, assessed in a survival framework. We estimated the relapse-free survival curves of participants under a hypothetical (i.e. counterfactual) intervention in which their BUP-NX dosage would be increased following their own subject-specific opioid use during the first 12 weeks of treatment versus a hypothetical intervention in which dose would remain constant.

FINDINGS

We estimated that increasing BUP-NX dose in response to recent opioid use would lower risk of relapse by 19.17 percentage points [95% confidence interval (CI) = -32.17, -6.18) (additive risk)] and 32% (0.68, 95% CI = 0.49, 0.86) (relative risk). The number-needed-to-treat with this intervention to prevent a single relapse is 6.

CONCLUSIONS

In people with opioid use disorder, a hypothetical intervention that increases sublingual buprenorphine-naloxone dose in response to opioid use during the first 12 weeks of treatment appears to reduce risk of relapse over 24 weeks, compared with holding the dose constant after week 2.

Transesophageal echocardiography and risk of respiratory failure in patients who had ischemic stroke or transient ischemic attack: an IDEAL phase 4 study

Objective

Transesophageal echocardiography (TEE) is sometimes used to search for cardioembolic sources after ischemic stroke or transient ischemic attack (TIA). TEE visualizes some sources better than transthoracic echocardiography, but TEE is invasive and may cause aspiration. Few data exist on the risk of respiratory complications after TEE in patients who had stroke or TIA. Our objective was to determine whether TEE was associated with increased risk of respiratory failure in patients who had ischemic stroke or TIA.

Design

This is a retrospective cohort study using administrative data from inpatient and outpatient insurance claims collected by the US federal government’s Centers for Medicare and Medicaid Services.

Setting

Hospitals and outpatient clinics throughout the USA.

Participants

99 081 patients ≥65 years old hospitalized for out-of-hospital ischemic stroke or TIA, defined by validated International Classification of Disease-9/10 diagnosis codes and present-on-admission codes, using claims data from 2008 to 2018 in a random 5% sample of Medicare beneficiaries.

Main outcome measures

Acute respiratory failure, defined as endotracheal intubation and/or mechanical ventilation, starting on the first day after admission through 28 days afterward.

Results

Of 99 081 patients included in this analysis, 73 733 (74.4%) had an ischemic stroke and 25 348 (25.6%) a TIA. TEE was performed in 4677 (4.7%) patients and intubation and/or mechanical ventilation in 1403 (1.4%) patients. The 28-day cumulative risk of respiratory failure after TEE (1.4%; 95% CI 0.8% to 2.7%) was similar to that seen in those without TEE (1.4%; 95% CI 1.4% to 1.5%) (p=0.84). After adjustment for age, sex, race, Charlson comorbidities, diagnosis of stroke versus TIA, intravenous thrombolysis, and mechanical thrombectomy, TEE was not associated with an increased risk of respiratory failure (HR, 0.9; 95% CI 0.6 to 1.2).

Conclusions

In a cohort of older patients who had ischemic stroke or TIA, TEE was not associated with an increased risk of subsequent respiratory failure.

Improving precision and power in randomized trials for COVID-19 treatments using covariate adjustment, for binary, ordinal, and time-to-event outcomes

Time is of the essence in evaluating potential drugs and biologics for the treatment and prevention of COVID-19. There are currently 876 randomized clinical trials (phase 2 and 3) of treatments for COVID-19 registered on clinicaltrials.gov. Covariate adjustment is a statistical analysis method with potential to improve precision and reduce the required sample size for a substantial number of these trials. Though covariate adjustment is recommended by the U.S. Food and Drug Administration and the European Medicines Agency, it is underutilized, especially for the types of outcomes (binary, ordinal, and time-to-event) that are common in COVID-19 trials. To demonstrate the potential value added by covariate adjustment in this context, we simulated two-arm, randomized trials comparing a hypothetical COVID-19 treatment versus standard of care, where the primary outcome is binary, ordinal, or time-to-event. Our simulated distributions are derived from two sources: longitudinal data on over 500 patients hospitalized at Weill Cornell Medicine New York Presbyterian Hospital and a Centers for Disease Control and Prevention preliminary description of 2449 cases. In simulated trials with sample sizes ranging from 100 to 1000 participants, we found substantial precision gains from using covariate adjustment-equivalent to 4-18% reductions in the required sample size to achieve a desired power. This was the case for a variety of estimands (targets of inference). From these simulations, we conclude that covariate adjustment is a low-risk, high-reward approach to streamlining COVID-19 treatment trials. We provide an R package and practical recommendations for implementation.

Optimizing opioid use disorder treatment with naltrexone or buprenorphine

BACKGROUND

Relapse rates during opioid use disorder (OUD) treatment remain unacceptably high. It is possible that optimally matching patients with medication type would reduce risk of relapse. Our objective was to learn a rule by which to assign type of medication for OUD to reduce risk of relapse, and to estimate the extent to which risk of relapse would be reduced if such a rule were used.

METHODS

This was a secondary analysis of an open-label randomized controlled, 24-week comparative effectiveness trial of injection extended-release naltrexone (XR-NTX), delivered approximately every 28 days, or daily sublingual buprenorphine-naloxone (BUP-NX) for treating OUD, 2014-2017 (N = 570). Outcome was a binary indicator of relapse to regular opioid use during the 24 weeks of outpatient treatment.

RESULTS

We found that applying an estimated individualized treatment rule-i.e., a rule that assigns patients with OUD to either XR-NTX or BUP-NX based on their individual characteristics in such a way that risk of relapse is minimized-would reduce risk of relapse by 24 weeks by 12% compared to randomly assigned treatment.

CONCLUSIONS

The number-needed-to-treat with the estimated treatment rule to prevent a single relapse is 14. A simpler, alternative estimated rule in which homeless participants would be treated with XR-NTX and stably housed participants would be treated with BUP-NX performed similarly. These results provide an estimate of the amount by which a relatively simple change in clinical practice could be expected to improve prevention of OUD relapse.

Explaining differential effects of medication for opioid use disorder using a novel approach incorporating mediating variables

BACKGROUND AND AIMS

A recent study found that homeless individuals with opioid use disorder (OUD) had a lower risk of relapse on extended-release naltrexone (XR-NTX) versus buprenorphine-naloxone (BUP-NX), whereas non-homeless individuals had a lower risk of relapse on BUP-NX. This secondary study examined differences in mediation pathways to medication effect between homeless and non-homeless participants.

DESIGN

Secondary analysis of an open-label randomized controlled, 24-week comparative effectiveness trial, 2014-17.

SETTING

Eight community addiction treatment programs in the United States.

PARTICIPANTS

English-speaking adults with DSM-5 OUD, recruited during inpatient admission (n = 570).

INTERVENTION(S)

Randomization to monthly injection of XR-NTX or daily sublingual BUP-NX.

MEASUREMENTS(S)

Mediation analysis estimated the direct effect of XR-NTX versus BUP-NX on relapse and indirect effect through mediators of medication adherence, use of illicit opioids, depressive symptoms and pain, separately by homeless status.

FINDINGS

For the homeless subgroup, the protective indirect path contributed a 3.4 percentage point reduced risk of relapse [95% confidence interval (CI) = -12.0, 5.3] comparing XR-NTX to BUP-NX (explaining 21% of the total effect). For the non-homeless subgroup, the indirect path contributed a 9.4 percentage point increased risk of relapse (95% CI = 3.1, 15.7) comparing XR-NTX to BUP-NX (explaining 57% of the total effect).

CONCLUSIONS

A novel approach to mediation analysis shows that much of the difference in medication effectiveness (extended-release naltrexone versus buprenorphine-naloxone) on opioid relapse among non-homeless adults with opioid use disorder appears to be explained by mediators of adherence, illicit opioid use, depressive symptoms and pain.

Effects of Multisession Anodal Electrical Stimulation of the Auditory Cortex on Temporary Noise-Induced Hearing Loss in the Rat

The protective effect of the efferent system against acoustic trauma (AT) has been shown by several experimental approaches, including damage to one ear, sectioning of the olivocochlear bundle (OCB) in the floor of the IV ventricle, and knock-in mice overexpressing outer hair cell (OHC) cholinergic receptors, among others. Such effects have been related to changes in the regulation of the cholinergic efferent system and in cochlear amplification, which ultimately reverse upon protective hearing suppression. In addition to well-known circuits of the brainstem, the descending corticofugal pathway also regulates efferent neurons of the olivary complex. In this study, we applied our recently developed experimental paradigm of multiple sessions of electrical stimulation (ES) to activate the efferent system in combination with noise overstimulation. ABR thresholds increased 1 and 2 days after AT (8-16 kHz bandpass noise at 107 dB for 90 min) recovering at AT + 14 days. However, after multiple sessions of epidural anodal stimulation, no changes in thresholds were observed following AT. Although an inflammatory response was also observed 1 day after AT in both groups, the counts of reactive macrophages in both experimental conditions suggest decreased inflammation in the epidural stimulation group. Quantitative immunocytochemistry for choline acetyltransferase (ChAT) showed a significant decrease in the size and optical density of the efferent terminals 1 day after AT and a rebound at 14 days, suggesting depletion of the terminals followed by a long-term compensatory response. Such a synthesis recovery was significantly higher upon cortical stimulation. No significant correlation was found between ChAT optical density and size of the buttons in sham controls (SC) and ES/AT + 1day animals; however, significant negative correlations were shown in all other experimental conditions. Therefore, our comparative analysis suggests that cochleotopic cholinergic neurotransmission is also better preserved after multisession epidural stimulation.

Access to Mechanical Thrombectomy for Ischemic Stroke in the United States

[Figure: see text].

Helped into Harm: Mediation of a Housing Voucher Intervention on Mental Health and Substance Use in Boys

BACKGROUND

Interventions can have harmful effects among subgroups they intend to help. The Moving To Opportunity experiment, in which families were randomized to receive a Section 8 housing voucher, was one example. Voucher receipt generally resulted in better long-term mental health and lower substance use and risk behavior outcomes among adolescent girls, but resulted in worse outcomes among adolescent boys. Reasons for this discrepancy and the unintended harmful health effects for boys are unclear. We used mediation analysis to estimate processes through which voucher receipt was hypothesized to affect adolescent mental health and substance use.

METHODS

We used longitudinal data (10-15 years) on boys enrolled in Moving To Opportunity. We estimated interventional (also known as stochastic) indirect effects of voucher receipt on mental health and substance use outcomes through mediators capturing aspects of the school environment, neighborhood poverty, and instability of the social environment. We also estimated interventional direct effects not operating through these mediators. We used a robust, efficient, nonparametric substitution estimator in the targeted minimum loss-based framework.

RESULTS

Housing voucher receipt increased long-term risk of any diagnostic statistical manual disorder, any mood disorder, any externalizing disorder, and cigarette smoking among boys. The majority (between 69% and 90%) of the total negative long-term effects could be explained by indirect effects through the mediators considered.

CONCLUSIONS

This evidence suggests that, even though the intervention had the desired effects on neighborhood poverty and the school environment, these "positives" ultimately negatively impacted the long-term mental health and behaviors of boys.

Porcine reproductive and respiratory syndrome virus impacts on gut microbiome in a strain virulence-dependent fashion

Porcine reproductive and respiratory syndrome (PRRS) is a viral disease defined by reproductive problems, respiratory distress and a negative impact on growth rate and general condition. Virulent PRRS virus (PRRSV) strains have emerged in the last years with evident knowledge gaps in their impact on the host immune response. Thus, the present study examines the impact of acute PRRS virus (PRRSV) infection, with two strains of different virulence, on selected immune parameters and on the gut microbiota composition of infected pigs using 16S rRNA compositional sequencing. Pigs were infected with a low virulent (PRRS_3249) or a virulent (Lena) PRRSV‐1 strain and euthanized at 1, 3, 6, 8 or 13 days post‐inoculation (dpi). Faeces were collected from each animal at the necropsy time‐point. Alpha and beta diversity analyses demonstrated that infection, particularly with the Lena strain, impacted the microbiome composition from 6 dpi onwards. Taxonomic differences revealed that infected pigs had higher abundance of Treponema and Methanobrevibacter (FDR < 0.05). Differences were more considerable for Lena‐ than for PRRS_3249‐infected pigs, showing the impact of strain virulence in the intestinal changes. Lena‐infected pigs had reduced abundancies of anaerobic commensals such as Roseburia, Anaerostipes, Butyricicoccus and Prevotella (P < 0.05). The depletion of these desirable commensals was significantly correlated to infection severity measured by viraemia, clinical signs, lung lesions and immune parameters (IL‐6, IFN‐γ and Hp serum levels). Altogether, the results from this study demonstrate the indirect impact of PRRSV infection on gut microbiome composition in a strain virulence‐dependent fashion and its association with selected immune markers.

Assessment of three commercial ELISAs for the detection of antibodies against Porcine epidemic diarrhea virus at different stages of the immune response

Three commercial ELISAs -two based on spike (E1 and E3) and one on nucleocapsid protein (E2)-were used to analyze the development and persistence of antibodies against Porcine epidemic diarrhea virus (PEDV). Seventy-five four-week-old PEDV-negative piglets were inoculated orally with a European G1b PEDV (INOC) and fourteen were kept as controls (CTRL). After the inoculation, E3 detected positive animals as soon as 7 days post inoculation (dpi), while the earliest detection with E1 and E2 was at 14 dpi. All samples were positive at 21 and 28 dpi using E1 and E3, respectively, while E2 failed to detect 23.3 % of the inoculated pigs at any time point. The percentages of positive samples were different through the study: E1 and E3 > E2 from 14 to 56 dpi; and E3 > E1 > E2 from 56 to 154 dpi (P < 0.05). Five months after the inoculation, E3 still detected 92.0 % (IC95 % = 85.1-98.8 %) of pigs as positive, while E1 and E2 detected only 27.0 % (IC95 % = 16.0-37.9 %) and 0%, respectively. The sensitivity for E2 never exceeded 0.62. Specificity was 1 for all ELISAs. These different outcomes could be related to the ELISA strategies (indirect versus competition), the antigens used, the cut-off, or to other intrinsic factors of each test. The observed differences could be of importance when assessing whether older animals, such as fatteners or gilts, had previously been in contact with PEDV.

Efficiently transporting causal direct and indirect effects to new populations under intermediate confounding and with multiple mediators

The same intervention can produce different effects in different sites. Existing transport mediation estimators can estimate the extent to which such differences can be explained by differences in compositional factors and the mechanisms by which mediating or intermediate variables are produced; however, they are limited to consider a single, binary mediator. We propose novel nonparametric estimators of transported interventional (in)direct effects that consider multiple, high-dimensional mediators and a single, binary intermediate variable. They are multiply robust, efficient, asymptotically normal, and can incorporate data-adaptive estimation of nuisance parameters. They can be applied to understand differences in treatment effects across sites and/or to predict treatment effects in a target site based on outcome data in source sites.

Machine learning in the estimation of causal effects: targeted minimum loss-based estimation and double/debiased machine learning

In recent decades, the fields of statistical and machine learning have seen a revolution in the development of data-adaptive regression methods that have optimal performance under flexible, sometimes minimal, assumptions on the true regression functions. These developments have impacted all areas of applied and theoretical statistics and have allowed data analysts to avoid the biases incurred under the pervasive practice of parametric model misspecification. In this commentary, I discuss issues around the use of data-adaptive regression in estimation of causal inference parameters. To ground ideas, I focus on two estimation approaches with roots in semi-parametric estimation theory: targeted minimum loss-based estimation (TMLE; van der Laan and Rubin, 2006) and double/debiased machine learning (DML; Chernozhukov and others, 2018). This commentary is not comprehensive, the literature on these topics is rich, and there are many subtleties and developments which I do not address. These two frameworks represent only a small fraction of an increasingly large number of methods for causal inference using machine learning. To my knowledge, they are the only methods grounded in statistical semi-parametric theory that also allow unrestricted use of data-adaptive regression techniques.

Nonparametric efficient causal mediation with intermediate confounders

Interventional effects for mediation analysis were proposed as a solution to the lack of identifiability of natural (in)direct effects in the presence of a mediator-outcome confounder affected by exposure. We present a theoretical and computational study of the properties of the interventional (in)direct effect estimands based on the efficient influence function in the nonparametric statistical model. We use the efficient influence function to develop two asymptotically optimal nonparametric estimators that leverage data-adaptive regression for the estimation of nuisance parameters: a one-step estimator and a targeted minimum loss estimator. We further present results establishing the conditions under which these estimators are consistent, multiply robust, $n^{1/2}$-consistent and efficient. We illustrate the finite-sample performance of the estimators and corroborate our theoretical results in a simulation study. We also demonstrate the use of the estimators in our motivating application to elucidate the mechanisms behind the unintended harmful effects that a housing intervention had on risky behaviour in adolescent girls.

Machine Learning Prediction of Stroke Mechanism in Embolic Strokes of Undetermined Source

BACKGROUND AND PURPOSE

One-fifth of ischemic strokes are embolic strokes of undetermined source (ESUS). Their theoretical causes can be classified as cardioembolic versus noncardioembolic. This distinction has important implications, but the categories' proportions are unknown.

METHODS

Using data from the Cornell Acute Stroke Academic Registry, we trained a machine-learning algorithm to distinguish cardioembolic versus non-cardioembolic strokes, then applied the algorithm to ESUS cases to determine the predicted proportion with an occult cardioembolic source. A panel of neurologists adjudicated stroke etiologies using standard criteria. We trained a machine learning classifier using data on demographics, comorbidities, vitals, laboratory results, and echocardiograms. An ensemble predictive method including L1 regularization, gradient-boosted decision tree ensemble (XGBoost), random forests, and multivariate adaptive splines was used. Random search and cross-validation were used to tune hyperparameters. Model performance was assessed using cross-validation among cases of known etiology. We applied the final algorithm to an independent set of ESUS cases to determine the predicted mechanism (cardioembolic or not). To assess our classifier's validity, we correlated the predicted probability of a cardioembolic source with the eventual post-ESUS diagnosis of atrial fibrillation.

RESULTS

Among 1083 strokes with known etiologies, our classifier distinguished cardioembolic versus noncardioembolic cases with excellent accuracy (area under the curve, 0.85). Applied to 580 ESUS cases, the classifier predicted that 44% (95% credibility interval, 39%-49%) resulted from cardiac embolism. Individual ESUS patients' predicted likelihood of cardiac embolism was associated with eventual atrial fibrillation detection (OR per 10% increase, 1.27 [95% CI, 1.03-1.57]; c-statistic, 0.68 [95% CI, 0.58-0.78]). ESUS patients with high predicted probability of cardiac embolism were older and had more coronary and peripheral vascular disease, lower ejection fractions, larger left atria, lower blood pressures, and higher creatinine levels.

CONCLUSIONS

A machine learning estimator that distinguished known cardioembolic versus noncardioembolic strokes indirectly estimated that 44% of ESUS cases were cardioembolic.

Improving Precision and Power in Randomized Trials for COVID-19 Treatments Using Covariate Adjustment, for Binary, Ordinal, and Time-to-Event Outcomes

Time is of the essence in evaluating potential drugs and biologics for the treatment and prevention of COVID-19. There are currently over 400 clinical trials (phase 2 and 3) of treatments for COVID-19 registered on clinicaltrials.gov. Covariate adjustment is a statistical analysis method with potential to improve precision and reduce the required sample size for a substantial number of these trials. Though covariate adjustment is recommended by the U.S. Food and Drug Administration and the European Medicines Agency, it is underutilized, especially for the types of outcomes (binary, ordinal and time-to-event) that are common in COVID-19 trials. To demonstrate the potential value added by covariate adjustment in this context, we simulated two-arm, randomized trials comparing a hypothetical COVID-19 treatment versus standard of care, where the primary outcome is binary, ordinal, or time-to-event. Our simulated distributions are derived from two sources: longitudinal data on over 500 patients hospitalized at Weill Cornell Medicine New York Presbyterian Hospital, and a Centers for Disease Control and Prevention (CDC) preliminary description of 2449 cases. We found substantial precision gains from using covariate adjustment--equivalent to 9-21% reductions in the required sample size to achieve a desired power--for a variety of estimands (targets of inference) when the trial sample size was at least 200. We provide an R package and practical recommendations for implementing covariate adjustment. The estimators that we consider are robust to model misspecification.

Nonparametric targeted Bayesian estimation of class proportions in unlabeled data

We introduce a novel Bayesian estimator for the class proportion in an unlabeled dataset, based on the targeted learning framework. The procedure requires the specification of a prior (and outputs a posterior) only for the target of inference, and yields a tightly concentrated posterior. When the scientific question can be characterized by a low-dimensional parameter functional, this focus on target prior and posterior distributions perfectly aligns with Bayesian subjectivism. We prove a Bernstein-von Mises-type result for our proposed Bayesian procedure, which guarantees that the posterior distribution converges to the distribution of an efficient, asymptotically linear estimator. In particular, the posterior is Gaussian, doubly robust, and efficient in the limit, under the only assumption that certain nuisance parameters are estimated at slower-than-parametric rates. We perform numerical studies illustrating the frequentist properties of the method. We also illustrate their use in a motivating application to estimate the proportion of embolic strokes of undetermined source arising from occult cardiac sources or large-artery atherosclerotic lesions. Though we focus on the motivating example of the proportion of cases in an unlabeled dataset, the procedure is general and can be adapted to estimate any pathwise differentiable parameter in a non-parametric model.

Activation of pro- and anti-inflammatory responses in lung tissue injury during the acute phase of PRRSV-1 infection with the virulent strain Lena

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Lena virulent strain caused an increase in sera levels of IFN-γ and IL-6.

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Lung viral load and PRRSV-N-protein⁺ cells were inversely correlated with CD163⁺ macrophages in the lung.

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CD14⁺ cells infiltrated interstitium to possibly replenish macrophages subsets.

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Lena-induced microscopic lung injury was linked to an increase of iNOS⁺ cells.

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The increase of CD200R1⁺ and FoxP3⁺ cells was associated with the course of lung injury.

Porcine reproductive and respiratory syndrome virus (PRRSV) plays a key role in porcine respiratory disease complex modulating the host immune response and favouring secondary bacterial infections. Pulmonary alveolar macrophages (PAMs) are the main cells supporting PRRSV replication, with CD163 as the essential receptor for viral infection. Although interstitial pneumonia is by far the representative lung lesion, suppurative bronchopneumonia is described for PRRSV virulent strains. This research explores the role of several immune markers potentially involved in the regulation of the inflammatory response and sensitisation of lung to secondary bacterial infections by PRRSV-1 strains of different virulence. Conventional pigs were intranasally inoculated with the virulent subtype 3 Lena strain or the low virulent subtype 1 3249 strain and euthanised at 1, 3, 6 and 8 dpi. Lena-infected pigs exhibited more severe clinical signs, macroscopic lung score and viraemia associated with an increase of IL-6 and IFN-γ in sera compared to 3249-infected pigs. Extensive areas of lung consolidation corresponding with suppurative bronchopneumonia were observed in Lena-infected pigs. Lung viral load and PRRSV-N-protein⁺ cells were always higher in Lena-infected animals. PRRSV-N-protein⁺ cells were linked to a marked drop of CD163⁺ macrophages. The number of CD14⁺ and iNOS⁺ cells gradually increased along PRRSV-1 infection, being more evident in Lena-infected pigs. The frequency of CD200R1⁺ and FoxP3⁺ cells peaked late in both PRRSV-1 strains, with a strong correlation between CD200R1⁺ cells and lung injury in Lena-infected pigs. These results highlight the role of molecules involved in the earlier and higher extent of lung lesions in piglets infected with the virulent Lena strain, pointing out the activation of routes potentially involved in the restraint of the local inflammatory response.

Trends in Active Cigarette Smoking Among Stroke Survivors in the United States, 1999 to 2018

Background and Purpose—

Patients who continue to smoke after a stroke face a higher risk of recurrent stroke. While several effective drugs for smoking cessation became available over the past 2 decades, whether active smoking has decreased among stroke survivors is unknown. We, therefore, evaluated trends in active smoking among stroke survivors during this period.

Methods—

We performed trends analyses using cross-sectional data collected every 1 to 2 years from 2 US health surveys spanning 1999 to 2018. In the National Health and Nutrition Examination Survey (NHANES) and the Behavioral Risk Factor Surveillance System (BRFSS) survey, participants were asked about prior stroke and active tobacco smoking. In NHANES, serum cotinine levels were available as a secondary measure of active smoking. We used multivariable logistic regression models for survey data to assess trends in active smoking among participants with and without prior stroke.

Results—

Among 49 375 participants in NHANES during 1999 to 2016 and 3 621 741 participants in BRFSS during 2011 to 2018, the prevalence of stroke was ≈3%. The overall prevalence of active smoking among stroke survivors was 24% in NHANES and 23% in BRFSS. Among individuals without prior stroke, the odds of smoking decreased over time in both NHANES (odds ratio, 0.95 per 2 years [95% CI, 0.93–0.96]) and BRFSS (odds ratio, 0.96 per year [95% CI, 0.96–0.96]). In contrast, there was no decrease in smoking among stroke survivors in NHANES (odds ratio, 1.00 [95% CI, 0.93–1.07]) or BRFSS (odds ratio, 0.99 [95% CI, 0.98–1.004]). Results were consistent in secondary analysis using biochemical ascertainment of active smoking in NHANES and in sensitivity analyses accounting for potential demographic changes in stroke epidemiology.

Conclusions—

In contrast to the general population, the prevalence of active smoking among stroke survivors has not decreased during the past 2 decades.

Cross-modal reaction of auditory and visual cortices after long-term bilateral hearing deprivation in the rat

Visual cortex (VC) over-activation analysed by evoked responses has been demonstrated in congenital deafness and after long-term acquired hearing loss in humans. However, permanent hearing deprivation has not yet been explored in animal models. Thus, the present study aimed to examine functional and molecular changes underlying the visual and auditory cross-modal reaction. For such purpose, we analysed cortical visual evoked potentials (VEPs) and the gene expression (RT-qPCR) of a set of markers for neuronal activation (c-Fos) and activity-dependent homeostatic compensation (Arc/Arg3.1). To determine the state of excitation and inhibition, we performed RT-qPCR and quantitative immunocytochemistry for excitatory (receptor subunits GluA2/3) and inhibitory (GABAA-α1, GABAB-R2, GAD65/67 and parvalbumin-PV) markers. VC over-activation was demonstrated by a significant increase in VEPs wave N1 and by up-regulation of the activity-dependent early genes c-Fos and Arc/Arg3.1 (thus confirming, by RT-qPCR, our previously published immunocytochemical results). GluA2 gene and protein expression were significantly increased in the auditory cortex (AC), particularly in layers 2/3 pyramidal neurons, but inhibitory markers (GAD65/67 and PV-GABA interneurons) were also significantly upregulated in the AC, indicating a concurrent increase in inhibition. Therefore, after permanent hearing loss in the rat, the VC is not only over-activated but also potentially balanced by homeostatic regulation, while excitatory and inhibitory markers remain imbalanced in the AC, most likely resulting from changes in horizontal intermodal regulation.

Correction to: Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards

The R code used for the data analysis and simulations in our manuscript (Díaz et al. 2018) had two errors, which we have corrected.

Reclassification of Ischemic Stroke Etiological Subtypes on the Basis of High-Risk Nonstenosing Carotid Plaque

Background and Purpose- Carotid artery plaque with <50% luminal stenosis may be an underappreciated stroke mechanism. We assessed how many stroke causes might be reclassified after accounting for nonstenosing plaques with high-risk features. Methods- We included patients enrolled in the Cornell Acute Stroke Academic Registry from 2011 to 2015 who had anterior circulation infarction, magnetic resonance imaging of the brain, and magnetic resonance angiography of the neck. High-risk plaque was identified by intraplaque hemorrhage ascertained from routine neck magnetic resonance angiography studies using validated methods. Infarct location was determined from diffusion-weighted imaging. Intraplaque hemorrhage and infarct location were assessed separately in a blinded fashion by a neuroradiologist. We used the McNemar test for matched data to compare the prevalence of intraplaque hemorrhage ipsilateral versus contralateral to brain infarction. We reclassified stroke subtypes by including large-artery atherosclerosis as a cause if there was intraplaque hemorrhage ipsilateral to brain infarction, regardless of the degree of stenosis. Results- Among the 1721 acute ischemic stroke patients registered in the Cornell Acute Stroke Academic Registry from 2011 to 2015, 579 were eligible for this analysis. High-risk plaque was more common ipsilateral versus contralateral to brain infarction in large-artery atherosclerotic (risk ratio [RR], 3.7 [95% CI, 2.2-6.1]), cryptogenic (RR, 2.1 [95% CI, 1.4-3.1]), and cardioembolic strokes (RR, 1.7 [95% CI, 1.1-2.4]). There were nonsignificant ipsilateral-contralateral differences in high-risk plaque among lacunar strokes (RR, 1.2 [95% CI, 0.4-3.5]) and strokes of other determined cause (RR, 1.5 [95% CI, 0.7-3.3]). After accounting for ipsilateral high-risk plaque, 88 (15.2%) patients were reclassified: 38 (22.6%) cardioembolic to multiple potential etiologies, 6 (8.5%) lacunar to multiple, 3 (15.8%) other determined cause to multiple, and 41 (20.8%) cryptogenic to large-artery atherosclerosis. Conclusions- High-risk carotid plaque was more prevalent ipsilateral to brain infarction across several ischemic stroke subtypes. Accounting for such plaques may reclassify the etiologies of up to 15% of cases in our sample.