Cost-effectiveness of screening with transcriptional signatures for incipient TB among U.S. migrants.

Introduction: Host-response-based transcriptional signatures (HrTS) have been developed to identify "incipient tuberculosis (TB)". No study has reported the cost-effectiveness of HrTS for post-arrival migrant screening programs in low-incidence countries. Objectives: To assess the potential health impact and cost-effectiveness of HrTS for post-arrival TB infection screening among new migrants in the United States. Methods: We used a discrete-event simulation model to compare four strategies: (1) no screening for TB infection or incipient TB; (2) 'IGRA-only', screen all with interferon gamma release assay (IGRA), provide TB preventive treatment for IGRA-positives; (3) 'IGRA-HrTS', screen all with IGRA followed by HrTS for IGRA-positives, provide incipient TB treatment for individuals testing positive with both tests; and (4) 'HrTS-only', screen all with HrTS, provide incipient TB treatment for HrTS-positives. We assessed outcomes over the lifetime of migrants entering the U.S. in 2019, assuming HrTS met the WHO Target Product Profile (TPP) optimal criteria. We conducted sensitivity analyses to evaluate the robustness of results. Results: The IGRA-only strategy dominated the HrTS-based strategies under both healthcare sector and societal perspectives, with an incremental cost-effectiveness ratio of $78,943 and $89,431 per quality-adjusted life-years (QALY) gained, respectively. This conclusion was robust to varying costs ($15-300) and characteristics of HrTS, and the willingness-to-pay threshold ($30,000-150,000/ QALY gained), but sensitive to the rate of decline in TB progression risk after U.S. entry. Conclusions: Our findings suggest that HrTS meeting the WHO TPP is unlikely to be a cost-effective component of post-arrival screening for migrants entering the U.S.

The long-term effects of domestic and international tuberculosis service improvements on tuberculosis trends within the USA: a mathematical modelling study.

BACKGROUND: For settings with low tuberculosis incidence, disease elimination is a long-term goal. We investigated pathways to tuberculosis pre-elimination (incidence <1·0 cases per 100 000 people) and elimination (incidence <0·1 cases per 100 000 people) in the USA, where incidence was estimated at 2·9 per 100 000 people in 2023. METHODS: Using a mathematical modelling framework, we simulated how US tuberculosis incidence could be affected by changes in tuberculosis services in the countries of origin for future migrants to the USA, as well as changes in tuberculosis services inside the USA. To do so, we used a linked set of transmission dynamic models, calibrated to demographic and epidemiological data for each setting. We constructed intervention scenarios representing improvements in tuberculosis services internationally and within the USA, individually and in combination, plus a base-case scenario representing continuation of current services. We simulated health and economic outcomes until 2100, using a Bayesian approach to quantify uncertainty in these outcomes. FINDINGS: Under the base-case scenario, US tuberculosis incidence was projected to decline to 1·8 cases per 100 000 (95% uncertainty interval [UI] 1·5-2·1) in the total population by 2050. Intervention scenarios produced substantial reductions in tuberculosis incidence, with the combination of all domestic and international interventions projected to achieve pre-elimination by 2033 (95% UI 2031-2037). Compared with the base-case scenario, this combination of interventions could avert 101 000 tuberculosis cases (95% UI 84 000-120 000) and 13 300 tuberculosis deaths (95% UI 10 500-16 300) in the USA from 2025 to 2050. Tuberculosis elimination was not projected before 2100. INTERPRETATION: Strengthening tuberculosis services domestically, promoting the development of more effective technologies and interventions, and supporting tuberculosis programmes in countries with a high tuberculosis burden are key strategies for accelerating progress towards tuberculosis elimination in the USA. FUNDING: US Centers for Disease Control and Prevention.

Estimated rates of progression to tuberculosis disease for persons infected with Mycobacterium tuberculosis in the United States.

BACKGROUND: In the United States, over 80% of tuberculosis (TB) disease cases are estimated to result from reactivation of latent TB infection (LTBI) acquired more than 2 years previously ("reactivation TB"). We estimated reactivation TB rates for the US population with LTBI, overall, by age, sex, race-ethnicity, and US-born status, and for selected comorbidities (diabetes, end-stage renal disease, and HIV). METHODS: We collated nationally representative data for 2011-2012. Reactivation TB incidence was based on TB cases reported to the National TB Surveillance System that were attributed to LTBI reactivation. Person-years at risk of reactivation TB were calculated using interferon-gamma release assay (IGRA) positivity from the National Health and Nutrition Examination Survey, published values for interferon-gamma release assay sensitivity and specificity, and population estimates from the American Community Survey. RESULTS: For persons aged ≥6 years with LTBI, the overall reactivation rate was estimated as 0.072 (95% uncertainty interval: 0.047, 0.12) per 100 person-years. Estimated reactivation rates declined with age. Compared to the overall population, estimated reactivation rates were higher for persons with diabetes (adjusted rate ratio [aRR] = 1.6 [1.5, 1.7]), end-stage renal disease (aRR = 9.8 [5.4, 19]), and HIV (aRR = 12 [10, 13]). CONCLUSIONS: In our study, individuals with LTBI faced small, non-negligible risks of reactivation TB. Risks were elevated for individuals with medical comorbidities that weaken immune function.

Model-based dimensionality reduction for single-cell RNA-seq using generalized bilinear models.

Dimensionality reduction is a critical step in the analysis of single-cell RNA-seq (scRNA-seq) data. The standard approach is to apply a transformation to the count matrix followed by principal components analysis (PCA). However, this approach can induce spurious heterogeneity and mask true biological variability. An alternative approach is to directly model the counts, but existing methods tend to be computationally intractable on large datasets and do not quantify uncertainty in the low-dimensional representation. To address these problems, we develop scGBM, a novel method for model-based dimensionality reduction of scRNA-seq data using a Poisson bilinear model. We introduce a fast estimation algorithm to fit the model using iteratively reweighted singular value decompositions, enabling the method to scale to datasets with millions of cells. Furthermore, scGBM quantifies the uncertainty in each cell's latent position and leverages these uncertainties to assess the confidence associated with a given cell clustering. On real and simulated single-cell data, we find that scGBM produces low-dimensional embeddings that better capture relevant biological information while removing unwanted variation.

Radiotherapy-Induced Neurocognitive Impairment Is Driven by Heightened Apoptotic Priming in Early Life and Prevented by Blocking BAX.

Although external beam radiotherapy (xRT) is commonly used to treat central nervous system (CNS) tumors in patients of all ages, young children treated with xRT frequently experience life-altering and dose-limiting neurocognitive impairment (NI) while adults do not. The lack of understanding of mechanisms responsible for these differences has impeded the development of neuroprotective treatments. Using a newly developed mouse model of xRT-induced NI, we found that neurocognitive function is impaired by ionizing radiation in a dose- and age-dependent manner, with the youngest animals being most affected. Histologic analysis revealed xRT-driven neuronal degeneration and cell death in neurogenic brain regions in young animals but not adults. BH3 profiling showed that neural stem and progenitor cells, neurons, and astrocytes in young mice are highly primed for apoptosis, rendering them hypersensitive to genotoxic damage. Analysis of single-cell RNA sequencing data revealed that neural cell vulnerability stems from heightened expression of proapoptotic genes including BAX, which is associated with developmental and mitogenic signaling by MYC. xRT induced apoptosis in primed neural cells by triggering a p53- and PUMA-initiated, proapoptotic feedback loop requiring cleavage of BID and culminating in BAX oligomerization and caspase activation. Notably, loss of BAX protected against apoptosis induced by proapoptotic signaling in vitro and prevented xRT-induced apoptosis in neural cells in vivo as well as neurocognitive sequelae. On the basis of these findings, preventing xRT-induced apoptosis specifically in immature neural cells by blocking BAX, BIM, or BID via direct or upstream mechanisms is expected to ameliorate NI in pediatric patients with CNS tumor. SIGNIFICANCE: Age- and differentiation-dependent apoptotic priming plays a pivotal role in driving radiotherapy-induced neurocognitive impairment and can be targeted for neuroprotection in pediatric patients.

Establishing a natural history of X-linked dystonia parkinsonism.

X-linked dystonia parkinsonism is a neurodegenerative movement disorder that affects men whose mothers originate from the island of Panay, Philippines. Current evidence indicates that the most likely cause is an expansion in the TAF1 gene that may be amenable to treatment. To prepare for clinical trials of therapeutic candidates for X-linked dystonia parkinsonism, we focused on the identification of quantitative phenotypic measures that are most strongly associated with disease progression. Our main objective is to establish a comprehensive, quantitative assessment of movement dysfunction and bulbar motor impairments that are sensitive and specific to disease progression in persons with X-linked dystonia parkinsonism. These measures will set the stage for future treatment trials. We enrolled patients with X-linked dystonia parkinsonism and performed a comprehensive oromotor, speech and neurological assessment. Measurements included patient-reported questionnaires regarding daily living activities and both neurologist-rated movement scales and objective quantitative measures of bulbar function and nutritional status. Patients were followed for 18 months from the date of enrollment and evaluated every 6 months during that period. We analysed a total of 87 men: 29 were gene-positive and had symptoms at enrollment, seven were gene-positive and had no symptoms at enrollment and 51 were gene-negative. We identified measures that displayed a significant change over the study. We used principal variables analysis to identify a minimal battery of 21 measures that explains 67.3% of the variance over the course of the study. These measures included patient-reported, clinician-rated and objective quantitative outcomes that may serve as endpoints in future clinical trials.

Bayesian Data Selection.

Insights into complex, high-dimensional data can be obtained by discovering features of the data that match or do not match a model of interest. To formalize this task, we introduce the "data selection" problem: finding a lower-dimensional statistic-such as a subset of variables-that is well fit by a given parametric model of interest. A fully Bayesian approach to data selection would be to parametrically model the value of the statistic, nonparametrically model the remaining "background" components of the data, and perform standard Bayesian model selection for the choice of statistic. However, fitting a nonparametric model to high-dimensional data tends to be highly inefficient, statistically and computationally. We propose a novel score for performing data selection, the "Stein volume criterion (SVC)", that does not require fitting a nonparametric model. The SVC takes the form of a generalized marginal likelihood with a kernelized Stein discrepancy in place of the Kullback-Leibler divergence. We prove that the SVC is consistent for data selection, and establish consistency and asymptotic normality of the corresponding generalized posterior on parameters. We apply the SVC to the analysis of single-cell RNA sequencing data sets using probabilistic principal components analysis and a spin glass model of gene regulation.

Reproducible Model Selection Using Bagged Posteriors.

Bayesian model selection is premised on the assumption that the data are generated from one of the postulated models. However, in many applications, all of these models are incorrect (that is, there is misspecification). When the models are misspecified, two or more models can provide a nearly equally good fit to the data, in which case Bayesian model selection can be highly unstable, potentially leading to self-contradictory findings. To remedy this instability, we propose to use bagging on the posterior distribution ("BayesBag") - that is, to average the posterior model probabilities over many bootstrapped datasets. We provide theoretical results characterizing the asymptotic behavior of the posterior and the bagged posterior in the (misspecified) model selection setting. We empirically assess the BayesBag approach on synthetic and real-world data in (i) feature selection for linear regression and (ii) phylogenetic tree reconstruction. Our theory and experiments show that, when all models are misspecified, BayesBag (a) provides greater reproducibility and (b) places posterior mass on optimal models more reliably, compared to the usual Bayesian posterior; on the other hand, under correct specification, BayesBag is slightly more conservative than the usual posterior, in the sense that BayesBag posterior probabilities tend to be slightly farther from the extremes of zero and one. Overall, our results demonstrate that BayesBag provides an easy-to-use and widely applicable approach that improves upon Bayesian model selection by making it more stable and reproducible.

Plasma extracellular vesicle proteins as promising noninvasive biomarkers for diagnosis of idiopathic pulmonary fibrosis.

High-resolution computed tomography (HRCT) imaging is critical for diagnostic evaluation of Idiopathic Pulmonary Fibrosis (IPF). However, several other interstitial lung diseases (ILDs) often exhibit radiologic pattern similar to IPF on HRCT making the diagnosis of the disease difficult. Therefore, biomarkers that distinguish IPF from other ILDs can be a valuable aid in diagnosis. Using mass spectrometry, we performed proteomic analysis of plasma extracellular vesicles (EVs) in patients diagnosed with IPF, chronic hypersensitivity pneumonitis, nonspecific interstitial pneumonitis, and healthy subjects. A five-protein signature was identified by lasso regression and was validated in an independent cohort using ELISA. The five-protein signature derived from mass spectrometry data showed an area under the receiver operating characteristic curve of 0.915 (95%CI: 0.819-1.011) and 0.958 (95%CI: 0.882-1.034) for differentiating IPF from other ILDs and from healthy subjects, respectively. Stepwise backwards elimination yielded a model with 3 and 2 proteins for discriminating IPF from other ILDs and healthy subjects, respectively, without compromising diagnostic accuracy. In summary, we discovered and validated EV protein biomarkers for differential diagnosis of IPF in independent cohorts. Interestingly, the biomarker panel could also distinguish IPF and healthy subjects with high accuracy. The biomarkers need to be evaluated in large prospective cohorts to establish their clinical utility.

Exploiting endogenous and therapy-induced apoptotic vulnerabilities in immunoglobulin light chain amyloidosis with BH3 mimetics.

Immunoglobulin light chain (AL) amyloidosis is an incurable hematologic disorder typically characterized by the production of amyloidogenic light chains by clonal plasma cells. These light chains misfold and aggregate in healthy tissues as amyloid fibrils, leading to life-threatening multi-organ dysfunction. Here we show that the clonal plasma cells in AL amyloidosis are highly primed to undergo apoptosis and dependent on pro-survival proteins MCL-1 and BCL-2. Notably, this MCL-1 dependency is indirectly targeted by the proteasome inhibitor bortezomib, currently the standard of care for this disease and the related plasma cell disorder multiple myeloma, due to upregulation of pro-apoptotic Noxa and its inhibitory binding to MCL-1. BCL-2 inhibitors sensitize clonal plasma cells to multiple front-line therapies including bortezomib, dexamethasone and lenalidomide. Strikingly, in mice bearing AL amyloidosis cell line xenografts, single agent treatment with the BCL-2 inhibitor ABT-199 (venetoclax) produces deeper remissions than bortezomib and triples median survival. Mass spectrometry-based proteomic analysis reveals rewiring of signaling pathways regulating apoptosis, proliferation and mitochondrial metabolism between isogenic AL amyloidosis and multiple myeloma cells that divergently alter their sensitivity to therapies. These findings provide a roadmap for the use of BH3 mimetics to exploit endogenous and induced apoptotic vulnerabilities in AL amyloidosis.

Realistic biomarkers from plasma extracellular vesicles for detection of beryllium exposure.

PURPOSE: Exposures related to beryllium (Be) are an enduring concern among workers in the nuclear weapons and other high-tech industries, calling for regular and rigorous biological monitoring. Conventional biomonitoring of Be in urine is not informative of cumulative exposure nor health outcomes. Biomarkers of exposure to Be based on non-invasive biomonitoring could help refine disease risk assessment. In a cohort of workers with Be exposure, we employed blood plasma extracellular vesicles (EVs) to discover novel biomarkers of exposure to Be. METHODS: EVs were isolated from plasma using size-exclusion chromatography and subjected to mass spectrometry-based proteomics. A protein-based classifier was developed using LASSO regression and validated by ELISA. RESULTS: We discovered a dual biomarker signature comprising zymogen granule protein 16B and putative protein FAM10A4 that differentiated between Be-exposed and -unexposed subjects. ELISA-based quantification of the biomarkers in an independent cohort of samples confirmed higher expression of the signature in the Be-exposed group, displaying high predictive accuracy (AUROC = 0.919). Furthermore, the biomarkers efficiently discriminated high- and low-exposure groups (AUROC = 0.749). CONCLUSIONS: This is the first report of EV biomarkers associated with Be exposure and exposure levels. The biomarkers could be implemented in resource-limited settings for Be exposure assessment.

Trends, Mechanisms, and Racial/Ethnic Differences of Tuberculosis Incidence in the US-Born Population Aged 50 Years or Older in the United States.

BACKGROUND: Older age is a risk factor for tuberculosis (TB) in low incidence settings. Using data from the US National TB Surveillance System and American Community Survey, we estimated trends and racial/ethnic differences in TB incidence among US-born cohorts aged ≥50 years. METHODS: In total, 42 000 TB cases among US-born persons ≥50 years were reported during 2001-2019. We used generalized additive regression models to decompose the effects of birth cohort and age on TB incidence rates, stratified by sex and race/ethnicity. Using genotype-based estimates of recent transmission (available 2011-2019), we implemented additional models to decompose incidence trends by estimated recent versus remote infection. RESULTS: Estimated incidence rates declined with age, for the overall cohort and most sex and race/ethnicity strata. Average annual percentage declines flattened for older individuals, from 8.80% (95% confidence interval [CI] 8.34-9.23) in 51-year-olds to 4.51% (95% CI 3.87-5.14) in 90-year-olds. Controlling for age, incidence rates were lower for more recent birth cohorts, dropping 8.79% (95% CI 6.13-11.26) on average between successive cohort years. Incidence rates were substantially higher for racial/ethnic minorities, and these inequalities persisted across all birth cohorts. Rates from recent infection declined at approximately 10% per year as individuals aged. Rates from remote infection declined more slowly with age, and this annual percentage decline approached zero for the oldest individuals. CONCLUSIONS: TB rates were highest for racial/ethnic minorities and for the earliest birth cohorts and declined with age. For the oldest individuals, annual percentage declines were low, and most cases were attributed to remote infection.

Genomic and transcriptomic correlates of immunotherapy response within the tumor microenvironment of leptomeningeal metastases.

Leptomeningeal disease (LMD) is a devastating complication of solid tumor malignancies, with dire prognosis and no effective systemic treatment options. Over the past decade, the incidence of LMD has steadily increased due to therapeutics that have extended the survival of cancer patients, highlighting the need for new interventions. To examine the efficacy of immune checkpoint inhibitors (ICI) in patients with LMD, we completed two phase II clinical trials. Here, we investigate the cellular and molecular features underpinning observed patient trajectories in these trials by applying single-cell RNA and cell-free DNA profiling to longitudinal cerebrospinal fluid (CSF) draws from enrolled patients. We recover immune and malignant cell types in the CSF, characterize cell behavior changes following ICI, and identify genomic features associated with relevant clinical phenomena. Overall, our study describes the liquid LMD tumor microenvironment prior to and following ICI treatment and demonstrates clinical utility of cell-free and single-cell genomic measurements for LMD research.

Identifying longevity associated genes by integrating gene expression and curated annotations.

Aging is a complex process with poorly understood genetic mechanisms. Recent studies have sought to classify genes as pro-longevity or anti-longevity using a variety of machine learning algorithms. However, it is not clear which types of features are best for optimizing classification performance and which algorithms are best suited to this task. Further, performance assessments based on held-out test data are lacking. We systematically compare five popular classification algorithms using gene ontology and gene expression datasets as features to predict the pro-longevity versus anti-longevity status of genes for two model organisms (C. elegans and S. cerevisiae) using the GenAge database as ground truth. We find that elastic net penalized logistic regression performs particularly well at this task. Using elastic net, we make novel predictions of pro- and anti-longevity genes that are not currently in the GenAge database.

Intranasal Dexmedetomidine for Sedation During Magnetoencephalography.

PURPOSE: Magnetoencephalography (MEG) is a noninvasive tool used clinically for presurgical evaluation of patients with medically intractable epilepsy. These recordings require patients to lie still for prolonged periods of time in a magnetically shielded room. Children or uncooperative adults with epilepsy may require sedation to reduce movement artefact and obtain high-quality recordings. Potential challenges related to the use of total intravenous anesthesia in the MEG environment include limited access to the patient's airway, remote location, suppression of cortical activity, and increased patient care expenses. We report our experience with intranasal dexmedetomidine as sedation for intractable epilepsy patients undergoing MEG. METHODS: Sleep deprivation occurred the night before MEG testing. Intranasal dexmedetomidine (2 µg/kg) was administered and oxygen saturation, blood pressure, and pulse rate were recorded continuously on a monitor outside the magnetically shielded room. A recording of spontaneous neuromagnetic activity was immediately followed by median nerve electrical stimulation. RESULTS: Twenty-six patients (mean age 12.2 ± 4.2 years) with medically intractable epilepsy were recorded using this protocol. There were no failures of sedation, and although patients experienced transient bradycardia, none required intervention and the recording did not need to be stopped. In all cases, artefact-free MEG recordings were obtained with sufficient interictal discharges available for source analysis. CONCLUSIONS: Our experience suggests that intranasal dexmedetomidine is an advantageous sedation option for children and adults with intractable epilepsy who are undergoing MEG. Further research is needed to determine the best ways to apply these methods to younger children and those with developmental disabilities.

Balloon Occlusion Testing to Assess Retinal Collateral and Predict Visual Outcomes in the Management of a Fusiform Intraorbital Ophthalmic Artery Aneurysm: Technical Note and Literature Review.

BACKGROUND AND IMPORTANCE: Aneurysms of the ophthalmic artery (OA) within the orbit and optic canal are extremely rare. Given the peripheral location of these aneurysms and the fact that most are fusiform, parent artery occlusion is preferred for symptomatic aneurysms. However, the impact of OA occlusion on vision is not always innocuous. Balloon occlusion testing (BOT) of the OA has the potential to gauge the possibility of visual compromise, in addition to assessing collaterals. While BOT has been assessed for carotid artery occlusion, its role in OA occlusion has not been well defined. CLINICAL PRESENTATION: We describe a patient with a 6-mm intracanalicular OA aneurysm who was treated with endovascular coil embolization of the aneurysm and occlusion of the parent artery. We performed a 30-min balloon occlusion test, in which we verified intact visual and neurological function. Collateral vascularity was confirmed. Complete occlusion was achieved and the patient recovered with intact visual and neurological function. We provide a review of literature pertaining to parent artery occlusion of OA aneurysms that occur within optic canal and orbit. A brief note on anatomy of OA and collaterals is provided. CONCLUSION: Endovascular parent artery occlusion is an effective treatment for OA aneurysms that occur in optic canal and orbit. Assessment of effect of occlusion on vision is important. BOT can assess this and provide valuable information. Recovery or preservation of visual function is highly dependent on preoperative visual status.

Robust Bayesian inference via coarsening.

The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a small violation of this assumption can have a large impact on the outcome of a Bayesian procedure. We introduce a novel approach to Bayesian inference that improves robustness to small departures from the model: rather than conditioning on the event that the observed data are generated by the model, one conditions on the event that the model generates data close to the observed data, in a distributional sense. When closeness is defined in terms of relative entropy, the resulting "coarsened" posterior can be approximated by simply tempering the likelihood-that is, by raising the likelihood to a fractional power-thus, inference can usually be implemented via standard algorithms, and one can even obtain analytical solutions when using conjugate priors. Some theoretical properties are derived, and we illustrate the approach with real and simulated data using mixture models and autoregressive models of unknown order.

Mixture models with a prior on the number of components.

A natural Bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with symmetric Dirichlet weights, and put a prior on the number of components-that is, to use a mixture of finite mixtures (MFM). The most commonly-used method of inference for MFMs is reversible jump Markov chain Monte Carlo, but it can be nontrivial to design good reversible jump moves, especially in high-dimensional spaces. Meanwhile, there are samplers for Dirichlet process mixture (DPM) models that are relatively simple and are easily adapted to new applications. It turns out that, in fact, many of the essential properties of DPMs are also exhibited by MFMs-an exchangeable partition distribution, restaurant process, random measure representation, and stick-breaking representation-and crucially, the MFM analogues are simple enough that they can be used much like the corresponding DPM properties. Consequently, many of the powerful methods developed for inference in DPMs can be directly applied to MFMs as well; this simplifies the implementation of MFMs and can substantially improve mixing. We illustrate with real and simulated data, including high-dimensional gene expression data used to discriminate cancer subtypes.

Comparison of Intranasal Dexmedetomidine and Oral Pentobarbital Sedation for Transthoracic Echocardiography in Infants and Toddlers: A Prospective, Randomized, Double-Blind Trial.

BACKGROUND: Acquisition of transthoracic echocardiographic (TTEcho) images in children often requires sedation. The optimal sedative for TTEcho has not been determined. Children with congenital heart disease are repeatedly exposed to sedatives and anesthetics that may affect brain development. Dexmedetomidine, which in animals alters brain structure to a lesser degree, may offer advantages in this vulnerable population. METHODS: A prospective, randomized, double-blind trial enrolled 280 children 3-24 months of age undergoing outpatient TTEcho, comparing 2.5 µg·kg intranasal dexmedetomidine to 5 mg·kg oral pentobarbital. Rescue sedation, for both groups, was intranasal dexmedetomidine 1 µg·kg. The primary outcome was adequate sedation within 30 minutes without rescue sedation, assessed by blinded personnel. Secondary outcomes included number of sonographer pauses, image quality in relation to motion artifacts, and parental satisfaction. RESULTS: Success rates with a single dose were not different between sedation techniques; 85% in the pentobarbital group and 84% in the dexmedetomidine group (P = .8697). Median onset of adequate sedation was marginally faster with pentobarbital (16.5 [interquartile range, 13-21] vs 18 [16-23] minutes for dexmedetomidine [P = .0095]). Time from drug administration to discharge was not different (P = .8238) at 70.5 (64-83) minutes with pentobarbital and 70 (63-82) minutes with dexmedetomidine. Ninety-five percent of sedation failures with pentobarbital and 100% of dexmedetomidine failures had successful rescue sedation with intranasal dexmedetomidine. CONCLUSIONS: Intranasal dexmedetomidine was comparable to oral pentobarbital sedation for TTEcho sedation in infants and did not increase the risk of clinically important adverse events. Intranasal dexmedetomidine appears to be an effective "rescue" sedative for both failed pentobarbital and dexmedetomidine sedation. Dexmedetomidine could be a safer option for repeated sedation in children, but further studies are needed to assess long-term consequence of repeated sedation in this high-risk population.