ATR-X syndrome protein targets tandem repeats and influences allele-specific expression in a size-dependent manner.

ATRX is an X-linked gene of the SWI/SNF family, mutations in which cause syndromal mental retardation and downregulation of α-globin expression. Here we show that ATRX binds to tandem repeat (TR) sequences in both telomeres and euchromatin. Genes associated with these TRs can be dysregulated when ATRX is mutated, and the change in expression is determined by the size of the TR, producing skewed allelic expression. This reveals the characteristics of the affected genes, explains the variable phenotypes seen with identical ATRX mutations, and illustrates a new mechanism underlying variable penetrance. Many of the TRs are G rich and predicted to form non-B DNA structures (including G-quadruplex) in vivo. We show that ATRX binds G-quadruplex structures in vitro, suggesting a mechanism by which ATRX may play a role in various nuclear processes and how this is perturbed when ATRX is mutated.

Distinct factors control histone variant H3.3 localization at specific genomic regions.

The incorporation of histone H3 variants has been implicated in the epigenetic memory of cellular state. Using genome editing with zinc-finger nucleases to tag endogenous H3.3, we report genome-wide profiles of H3 variants in mammalian embryonic stem cells and neuronal precursor cells. Genome-wide patterns of H3.3 are dependent on amino acid sequence and change with cellular differentiation at developmentally regulated loci. The H3.3 chaperone Hira is required for H3.3 enrichment at active and repressed genes. Strikingly, Hira is not essential for localization of H3.3 at telomeres and many transcription factor binding sites. Immunoaffinity purification and mass spectrometry reveal that the proteins Atrx and Daxx associate with H3.3 in a Hira-independent manner. Atrx is required for Hira-independent localization of H3.3 at telomeres and for the repression of telomeric RNA. Our data demonstrate that multiple and distinct factors are responsible for H3.3 localization at specific genomic locations in mammalian cells.

A Double-Blind, Randomized, Placebo-controlled Trial of Long-Term Doxycycline Therapy on Exacerbation Rate in Patients with Stable Chronic Obstructive Pulmonary Disease.

Rationale: Chronic obstructive pulmonary disease (COPD) exacerbations are a major cause of morbidity and mortality, and preventing them is a key treatment target. Long-term macrolide treatment is effective at reducing exacerbations, but there is a paucity of evidence for other antibiotic classes. Objectives: To assess whether 12-month use of doxycycline reduces the exacerbation rate in people with COPD. Methods: People with moderate to very severe COPD and an exacerbation history were recruited from three UK centers and randomized to 12 months of doxycycline 100 mg once daily or placebo. The primary study outcome was the exacerbation rate per person-year. Results: A total of 222 people were randomized. Baseline mean FEV1 was 1.35 L (SD, 0.35 L), 52.5% predicted (SD, 15.9% predicted). The median number of treated exacerbations in the year before the study was 2 (SD, 1-4). A total of 71% of patients reported two or more exacerbations, and 81% were already prescribed inhaled corticosteroids at baseline. The COPD exacerbation rate did not differ between the groups (doxycycline/placebo rate ratio [RR], 0.86; 95% confidence interval [CI], 0.67-1.10; P = 0.23). No difference was seen if only treated exacerbations or hospitalizations were considered. In preplanned subgroup analysis, doxycycline appeared to better reduce the exacerbation rate among people with severe COPD (RR, 0.36; 95% CI, 0.15-0.85; P = 0.019) and in those with an eosinophil count <300 cells/µl (RR, 0.50; 95% CI, 0.29-0.84; P = 0.01). Health status measured by St. George's Respiratory Questionnaire was 5.2 points worse in the doxycycline group at 12 months (P < 0.007). Conclusions: Doxycycline did not significantly reduce the exacerbation rate, over 12 months, in participants with COPD who exacerbated regularly, but it may have benefitted those with more severe COPD or blood eosinophil counts <300 cells/µl. Clinical trial registered with www.clinicaltrials.gov (NCT02305940).

A stochastically curtailed single-arm phase II trial design for binary outcomes.

Phase II clinical trials are a critical aspect of the drug development process. With drug development costs ever increasing, novel designs that can improve the efficiency of phase II trials are extremely valuable.Phase II clinical trials for cancer treatments often measure a binary outcome. The final trial decision is generally to continue or cease development. When this decision is based solely on the result of a hypothesis test, the result may be known with certainty before the planned end of the trial. Unfortunately, there is often no opportunity for early stopping when this occurs.Some existing designs do permit early stopping in this case, accordingly reducing the required sample size and potentially speeding up drug development. However, more improvements can be achieved by stopping early when the final trial decision is very likely, rather than certain, known as stochastic curtailment. While some authors have proposed approaches of this form, these approaches have various limitations.In this work we address these limitations by proposing new design approaches for single-arm phase II binary outcome trials that use stochastic curtailment. We use exact distributions, avoid simulation, consider a wider range of possible designs and permit early stopping for promising treatments. As a result, we are able to obtain trial designs that have considerably reduced sample sizes on average.

Targeted Retreatment of Incompletely Recovered Chronic Obstructive Pulmonary Disease Exacerbations with Ciprofloxacin. A Double-Blind, Randomized, Placebo-controlled, Multicenter, Phase III Clinical Trial.

Rationale: Chronic obstructive pulmonary disease (COPD) exacerbations are prone to nonrecovery, but there are no data about the effectiveness of retreatment for these prolonged events. We examined whether further therapy with ciprofloxacin for incompletely resolved COPD exacerbations prolonged the time until the next event.Objectives: To assess whether incompletely recovered COPD exacerbations benefit from additional treatment with ciprofloxacin, at Day 14.Methods: In a multicenter, randomized double-blind placebo-controlled trial, we studied retreatment with oral ciprofloxacin 500 mg or matched placebo twice daily for 7 days in patients with Global Initiative for Chronic Obstructive Lung Disease stage II-IV COPD and persistent symptoms and/or serum C-reactive protein ≥8 mg/L initiated 14 (±3) days after an index COPD exacerbation. The primary outcome was the time to the next exacerbation within a 90-day period.Measurements and Main Results: Among 826 patients screened at four centers, 144 eligible participants with incomplete recovery were randomized to receive ciprofloxacin (n = 72) or placebo (n = 72). Within 90 days of randomization, 57% of the patients in the ciprofloxacin group and 53% in the placebo group experienced one or more exacerbations. The median time to the next exacerbation was 32.5 days (interquartile range 13-50) in the placebo arm and 34 days (interquartile range 17-62) in the ciprofloxacin arm, which was not significantly different (adjusted hazard ratio, 1.07; 95% confidence interval, 0.68-1.68; P = 0.76). No significant differences were seen in quality-of-life scores or lung function between the treatment groups.Conclusions: In patients with persistent symptoms and/or raised C-reactive protein 14 days after a COPD exacerbation, an additional course of ciprofloxacin resulted in no additional benefit compared with placebo. This suggests that nonrecovered exacerbations are not driven by ongoing bacterial infection and may potentially be targeted with antiinflammatory therapy.Clinical trial registered with www.clinicaltrials.gov (NCT02300220).

A stochastically curtailed two-arm randomised phase II trial design for binary outcomes.

Randomised controlled trials are considered the gold standard in trial design. However, phase II oncology trials with a binary outcome are often single-arm. Although a number of reasons exist for choosing a single-arm trial, the primary reason is that single-arm designs require fewer participants than their randomised equivalents. Therefore, the development of novel methodology that makes randomised designs more efficient is of value to the trials community. This article introduces a randomised two-arm binary outcome trial design that includes stochastic curtailment (SC), allowing for the possibility of stopping a trial before the final conclusions are known with certainty. In addition to SC, the proposed design involves the use of a randomised block design, which allows investigators to control the number of interim analyses. This approach is compared with existing designs that also use early stopping, through the use of a loss function comprised of a weighted sum of design characteristics. Comparisons are also made using an example from a real trial. The comparisons show that for many possible loss functions, the proposed design is superior to existing designs. Further, the proposed design may be more practical, by allowing a flexible number of interim analyses. One existing design produces superior design realisations when the anticipated response rate is low. However, when using this design, the probability of rejecting the null hypothesis is sensitive to misspecification of the null response rate. Therefore, when considering randomised designs in phase II, we recommend the proposed approach be preferred over other sequential designs.

Two new approaches for the visualisation of models for network meta-analysis.

BACKGROUND: Meta-analysis is a useful tool for combining evidence from multiple studies to estimate a pooled treatment effect. An extension of meta-analysis, network meta-analysis, is becoming more commonly used as a way to simultaneously compare multiple treatments in a single analysis. Despite the variety of approaches available for presenting fitted models, ascertaining an intuitive understanding of these models is often difficult. This is especially challenging in large networks with many different treatments. Here we propose two visualisation methods, so that network meta-analysis models can be more easily interpreted. METHODS: Our methods can be used irrespective of the statistical model or the estimation method used and are grounded in network analysis. We define three types of distance measures between the treatments that contribute to the network. These three distance measures are based on 1) the estimated treatment effects, 2) their standard errors and 3) the corresponding p-values. Then, by using a suitable threshold, we categorise some treatment pairs as being "close" (short distances). Treatments that are close are regarded as "connected" in the network analysis theory. Finally, we group the treatments into communities using standard methods for network analysis. We are then able to identify which parts of the network are estimated to have similar (or different) treatment efficacy and which parts of the network are better identified. We also propose a second method using parametric bootstrapping, where a heat map is used in the visualisation. We use the software R and provide the code used. RESULTS: We illustrate our new methods using a challenging dataset containing 22 treatments, and a previously fitted model for this data. Two communities of treatments that appear to have similar efficacy are identified. Furthermore using our methods we can identify parts of the network that are better (and less well) identified. CONCLUSIONS: Our new visualisation approaches may be used by network meta-analysts to gain an intuitive understanding of the implications of their fitted models. Our visualisation methods may be used informally, to identify the most salient features of the fitted models that can then be reported, or more formally by presenting the new visualisation devices within published reports.

Cumulative Effective Dose and Cancer Risk of Pediatric Population in Repetitive Whole-Body Scan Using Dual-Energy X-Ray Absorptiometry.

This study aims to quantitatively evaluate the cumulative effective dose and associated cancer risk of pediatric patients of US and Hong Kong population undergoing repetitive whole-body scans with dual-energy X-ray absorptiometry (DXA) during their diagnosis and follow-up periods. Organ-absorbed doses of pediatric patients undergoing DXA whole-body scan have been computer simulated using patient imaging parameters input to the Monte Carlo software PCXMC. Gender- and age-specific effective doses have been calculated with the simulated organ-absorbed doses using the ICRP-103 approach. The associated radiation-induced cancer risk, expressed as lifetime attributable cancer risk (LAR), has been estimated according to the method introduced in the Biological Effects of Ionizing Radiation VII report. Mathematical fitting for effective dose and for LAR, as a function of age at exposure, has been analytically obtained to quantitatively estimate the cumulated effective dose and LAR for pediatric patients of US and Hong Kong population with repetitive DXA whole-body scan during their follow-up period. The effective dose of a single DXA whole-body scan for patients exposed at the age between 5 and 18 years was calculated as 8.47-17.68 µSv. The corresponding LAR for US and Hong Kong population was between the range of 4.57 × 10-7 and 7.14 × 10-7. The cumulative effective dose of DXA whole-body scan for patients exposed annually at age between 5 and 18 years was calculated as 180 µSv for girls and 168 µSv for boys. The corresponding cumulative LAR for US and Hong Kong population was calculated as 3.77 × 10-6 to 5.48 × 10-6. Girls would be at a statistically significant higher cumulated cancer risk than boys under the same whole-body DXA protocol (p = 0.03). The probability of cumulative LAR for pediatric populations undergoing annual DXA whole-body scan is regarded as minimal. We demonstrate the use of computer simulation and analytic formulation to quantitatively obtain the cumulated effective dose and cancer risk at any age of exposure, which are useful information for medical personnel to track patient radiation dose and to alleviate patients' parents concern about radiation safety in repetitive whole-body scan using DXA.

A matrix-based method of moments for fitting multivariate network meta-analysis models with multiple outcomes and random inconsistency effects.

Random-effects meta-analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatments) meta-analysis. Here, we provide a new model and corresponding estimation procedure for multivariate network meta-analysis, so that multiple outcomes and treatments can be included in a single analysis. Our new multivariate model is a direct extension of a univariate model for network meta-analysis that has recently been proposed. We allow two types of unknown variance parameters in our model, which represent between-study heterogeneity and inconsistency. Inconsistency arises when different forms of direct and indirect evidence are not in agreement, even having taken between-study heterogeneity into account. However, the consistency assumption is often assumed in practice and so we also explain how to fit a reduced model which makes this assumption. Our estimation method extends several other commonly used methods for meta-analysis, including the method proposed by DerSimonian and Laird (). We investigate the use of our proposed methods in the context of both a simulation study and a real example.

A comparison of seven random-effects models for meta-analyses that estimate the summary odds ratio.

Comparative trials that report binary outcome data are commonly pooled in systematic reviews and meta-analyses. This type of data can be presented as a series of 2-by-2 tables. The pooled odds ratio is often presented as the outcome of primary interest in the resulting meta-analysis. We examine the use of 7 models for random-effects meta-analyses that have been proposed for this purpose. The first of these models is the conventional one that uses normal within-study approximations and a 2-stage approach. The other models are generalised linear mixed models that perform the analysis in 1 stage and have the potential to provide more accurate inference. We explore the implications of using these 7 models in the context of a Cochrane Review, and we also perform a simulation study. We conclude that generalised linear mixed models can result in better statistical inference than the conventional 2-stage approach but also that this type of model presents issues and difficulties. These challenges include more demanding numerical methods and determining the best way to model study specific baseline risks. One possible approach for analysts is to specify a primary model prior to performing the systematic review but also to present the results using other models in a sensitivity analysis. Only one of the models that we investigate is found to perform poorly so that any of the other models could be considered for either the primary or the sensitivity analysis.

Editor's Choice - The Implications of Non-compliance to Endovascular Aneurysm Repair Surveillance: A Systematic Review and Meta-analysis.

OBJECTIVE/BACKGROUND: Increasingly, reports show that compliance rates with endovascular aneurysm repair (EVAR) surveillance are often suboptimal. The aim of this study was to determine the safety implications of non-compliance with surveillance. METHODS: The study was carried out according to the Preferred Items for Reporting of Systematic Reviews and Meta-Analyses (PRISMA) guidelines. An electronic search was undertaken by two independent authors using Embase, MEDLINE, Cochrane, and Web of Science databases from 1990 to July 2017. Only studies that analysed infrarenal EVAR and had a definition of non-compliance described as weeks or months without imaging surveillance were analysed. Meta-analysis was carried out using the random-effects model and restricted maximum likelihood estimation. RESULTS: Thirteen articles (40,730 patients) were eligible for systematic review; of these, seven studies (14,311 patients) were appropriate for comparative meta-analyses of mortality rates. Three studies (8316 patients) were eligible for the comparative meta-analyses of re-intervention rates after EVAR and four studies (12,995 patients) eligible for meta-analysis for abdominal aortic aneurysm related mortality (ARM). The estimated average non-compliance rate was 42.0% (95% confidence interval [CI] 28-56%). Although there is some evidence that non-compliant patients have better survival rates, there was no statistically significant difference in all cause mortality rates (year 1: odds ratio [OR] 5.77, 95% CI 0.74-45.14; year 3: OR 2.28, 95% CI 0.92-5.66; year 5: OR 1.81, 95% CI 0.88-3.74) and ARM (OR 1.47, 95% CI 0.99-2.19) between compliant and non-compliant patients in the first 5 years after EVAR. The re-intervention rate was statistically significantly higher in compliant patients from 3 to 5 years after EVAR (year 1: OR 6.36, 95% CI 0.23-172.73; year 3: OR 3.94, 85% CI 1.46-10.69; year 5: OR 5.34, 95% CI 1.87-15.29). CONCLUSION: This systematic review and meta-analysis suggests that patients compliant with EVAR surveillance programmes may have an increased re-intervention rate but do not appear to have better survival rates than non-compliant patients.

Failure Analysis for Ultrasound Machines in a Radiology Department after Implementation of Predictive Maintenance Method.

OBJECTIVE: The objective of the study was to perform quantitative failure and fault analysis to the diagnostic ultrasound (US) scanners in a radiology department after the implementation of the predictive maintenance (PdM) method; to study the reduction trend of machine failure; to understand machine operating parameters affecting the failure; to further optimize the method to maximize the machine clinically service time. MATERIALS AND METHODS: The PdM method has been implemented to the 5 US machines since 2013. Log books were used to record machine failures and their root causes together with the time spent on repair, all of which were retrieved, categorized, and analyzed for the period between 2013 and 2016. RESULTS: There were a total of 108 cases of failure occurred in these 5 US machines during the 4-year study period. The average number of failure per month for all these machines was 2.4. Failure analysis showed that there were 33 cases (30.5%) due to software, 44 cases (40.7%) due to hardware, and 31 cases (28.7%) due to US probe. There was a statistically significant negative correlation between the time spent on regular quality assurance (QA) by hospital physicists with the time spent on faulty parts replacement over the study period (P = 0.007). However, there was no statistically significant correlation between regular QA time and total yearly breakdown case (P = 0.12), although there has been a decreasing trend observed in the yearly total breakdown. CONCLUSION: There has been a significant improvement on the machine failure of US machines attributed to the concerted effort of sonographers and physicists in our department to practice the PdM method, in that system component repair time has been reduced, and a decreasing trend in the number of system breakdown has been observed.

The Hartung-Knapp modification for random-effects meta-analysis: A useful refinement but are there any residual concerns?

The modified method for random-effects meta-analysis, usually attributed to Hartung and Knapp and also proposed by Sidik and Jonkman, is easy to implement and is becoming advocated for general use. Here, we examine a range of potential concerns about the widespread adoption of this method. Motivated by these issues, a variety of different conventions can be adopted when using the modified method in practice. We describe and investigate the use of a variety of these conventions using a new taxonomy of meta-analysis datasets. We conclude that the Hartung and Knapp modification may be a suitable replacement for the standard method. Despite this, analysts who advocate the modified method should be ready to defend its use against the possible objections to it that we present. We further recommend that the results from more conventional approaches should be used as sensitivity analyses when using the modified method. It has previously been suggested that a common-effect analysis should be used for this purpose but we suggest amending this recommendation and argue that a standard random-effects analysis should be used instead.

Extending DerSimonian and Laird's methodology to perform network meta-analyses with random inconsistency effects.

Network meta-analysis is becoming more popular as a way to compare multiple treatments simultaneously. Here, we develop a new estimation method for fitting models for network meta-analysis with random inconsistency effects. This method is an extension of the procedure originally proposed by DerSimonian and Laird. Our methodology allows for inconsistency within the network. The proposed procedure is semi-parametric, non-iterative, fast and highly accessible to applied researchers. The methodology is found to perform satisfactorily in a simulation study provided that the sample size is large enough and the extent of the inconsistency is not very severe. We apply our approach to two real examples.

Two new methods to fit models for network meta-analysis with random inconsistency effects.

BACKGROUND: Meta-analysis is a valuable tool for combining evidence from multiple studies. Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into account. We propose two new estimation methods for network meta-analysis models with random inconsistency effects. METHODS: The model we consider is an extension of the conventional random-effects model for meta-analysis to the network meta-analysis setting and allows for potential inconsistency using random inconsistency effects. Our first new estimation method uses a Bayesian framework with empirically-based prior distributions for both the heterogeneity and the inconsistency variances. We fit the model using importance sampling and thereby avoid some of the difficulties that might be associated with using Markov Chain Monte Carlo (MCMC). However, we confirm the accuracy of our importance sampling method by comparing the results to those obtained using MCMC as the gold standard. The second new estimation method we describe uses a likelihood-based approach, implemented in the metafor package, which can be used to obtain (restricted) maximum-likelihood estimates of the model parameters and profile likelihood confidence intervals of the variance components. RESULTS: We illustrate the application of the methods using two contrasting examples. The first uses all-cause mortality as an outcome, and shows little evidence of between-study heterogeneity or inconsistency. The second uses "ear discharge" as an outcome, and exhibits substantial between-study heterogeneity and inconsistency. Both new estimation methods give results similar to those obtained using MCMC. CONCLUSIONS: The extent of heterogeneity and inconsistency should be assessed and reported in any network meta-analysis. Our two new methods can be used to fit models for network meta-analysis with random inconsistency effects. They are easily implemented using the accompanying R code in the Additional file 1. Using these estimation methods, the extent of inconsistency can be assessed and reported.

Impact of Prolonged Exacerbation Recovery in Chronic Obstructive Pulmonary Disease.

RATIONALE: Exacerbations are important and heterogeneous events in the natural history of chronic obstructive pulmonary disease (COPD). OBJECTIVES: To examine the consequences of prolonged exacerbation recovery in patients with COPD. METHODS: A cohort of 384 patients with COPD (FEV1 % predicted 45.8 [SD, 16.6] and a median exacerbation rate of 2.13 per year [interquartile range, 1.0-3.2]) were followed for 1,039 days (interquartile range, 660-1,814) between October 1995 and January 2013. Patients recorded daily worsening of respiratory symptoms and peak expiratory flow (PEF), and when stable underwent spirometry every 3 months, and completed the St. George's Respiratory Questionnaire annually. Exacerbations were diagnosed as 2 consecutive days with one major symptom plus another respiratory symptom. Exacerbation duration was defined as the time from onset to the day preceding 2 consecutive symptom-free days and recovery in PEF as return to preexacerbation levels. MEASUREMENTS AND MAIN RESULTS: A total of 351 patients had one or more exacerbations. Patients with a longer symptom duration (mean, 14.5 d) had a worse St. George's Respiratory Questionnaire total score (0.2 units per 1 day; P = 0.040). A longer symptomatic duration was associated with a shorter interval between exacerbation recovery and onset of the next exacerbation (hazard ratio, 1.004; P = 0.013). For 257 (7.3%) exacerbations, PEF did not recover within 99 days. These exacerbations were associated with symptoms of a viral infection (cold and sore throat). Patients with these nonrecovered exacerbations showed a 10.8 ml/yr (P < 0.001) faster decline in FEV1. CONCLUSIONS: Prolonged exacerbation symptomatic duration is associated with poorer health status and a greater risk of a new event. Exacerbations where lung function does not recover are associated with symptoms of viral infections and accelerated decline in FEV1.

Effects of different antibiotic classes on airway bacteria in stable COPD using culture and molecular techniques: a randomised controlled trial.

BACKGROUND: Long-term antibiotic therapy is used to prevent exacerbations of COPD but there is uncertainty over whether this reduces airway bacteria. The optimum antibiotic choice remains unknown. We conducted an exploratory trial in stable patients with COPD comparing three antibiotic regimens against placebo. METHODS: This was a single-centre, single-blind, randomised placebo-controlled trial. Patients aged ≥45 years with COPD, FEV1<80% predicted and chronic productive cough were randomised to receive either moxifloxacin 400 mg daily for 5 days every 4 weeks, doxycycline 100 mg/day, azithromycin 250 mg 3 times a week or one placebo tablet daily for 13 weeks. The primary outcome was the change in total cultured bacterial load in sputum from baseline; secondary outcomes included bacterial load by 16S quantitative PCR (qPCR), sputum inflammation and antibiotic resistance. RESULTS: 99 patients were randomised; 86 completed follow-up, were able to expectorate sputum and were analysed. After adjustment, there was a non-significant reduction in bacterial load of 0.42 log10 cfu/mL (95% CI -0.08 to 0.91, p=0.10) with moxifloxacin, 0.11 (-0.33 to 0.55, p=0.62) with doxycycline and 0.08 (-0.38 to 0.54, p=0.73) with azithromycin from placebo, respectively. There were also no significant changes in bacterial load measured by 16S qPCR or in airway inflammation. More treatment-related adverse events occurred with moxifloxacin. Of note, mean inhibitory concentrations of cultured isolates increased by at least three times over placebo in all treatment arms. CONCLUSIONS: Total airway bacterial load did not decrease significantly after 3 months of antibiotic therapy. Large increases in antibiotic resistance were seen in all treatment groups and this has important implications for future studies. TRIAL REGISTRATION NUMBER: clinicaltrials.gov (NCT01398072).

Dexmedetomidine improves pulmonary outcomes in thoracic surgery under one-lung ventilation: A meta-analysis.

INTRODUCTION: Dexmedetomidine improves intrapulmonary shunt in thoracic surgery and minimizes inflammatory response during one-lung ventilation (OLV). However, it is unclear whether such benefits translate into less postoperative pulmonary complications (PPCs). Our objective was to determine the impact of dexmedetomidine on the incidence of PPCs after thoracic surgery. METHODS: Major databases were used to identify randomized trials that compared dexmedetomidine versus placebo during thoracic surgery in terms of PPCs. Our primary outcome was atelectasis within 7 days after surgery. Other specific PPCs included hypoxemia, pneumonia, and acute respiratory distress syndrome (ARDS). Secondary outcome included intraoperative respiratory mechanics (respiratory compliance [Cdyn]) and postoperative lung function (forced expiratory volume [FEV1]). Random effects models were used to estimate odds ratios (OR). RESULTS: Twelve randomized trials, including 365 patients in the dexmedetomidine group and 359 in the placebo group, were analyzed in this meta-analysis. Patients in the dexmedetomidine group were less likely to develop postoperative atelectasis (2.3% vs 6.8%, OR 0.42, 95%CI 0.18-0.95, P = 0.04; low certainty) and hypoxemia (3.4% vs 11.7%, OR 0.26, 95%CI 0.10-0.68, P = 0.01; moderate certainty) compared to the placebo group. The incidence of postoperative pneumonia (3.2% vs 5.8%, OR 0.57, 95%CI 0.25-1.26, P = 0.17; moderate certainty) or ARDS (0.9% vs 3.5%, OR 0.39, 95%CI 0.07-2.08, P = 0.27; moderate certainty) was comparable between groups. Both intraoperative Cdyn and postoperative FEV1 were higher among patients that received dexmedetomidine with a mean difference of 4.42 mL/cmH2O (95%CI 3.13-5.72) and 0.27 L (95%CI 0.12-0.41), respectively. CONCLUSION: Dexmedetomidine administration during thoracic surgery may potentially reduce the risk of postoperative atelectasis and hypoxemia. However, current evidence is insufficient to demonstrate an effect on pneumonia or ARDS.

Patient dosimetry for 90Y selective internal radiation treatment based on 90Y PET imaging.

Until recently, the radiation dose to patients undergoing the 90Y selective internal radiation treatment (SIRT) procedure is determined by applying the partition model to 99mTc MAA pretreatment scan. There can be great uncertainty in radiation dose calculated from this approach and we presented a method to compute the 3D dose distributions resulting from 90Y SIRT based on 90Y positron emission tomography (PET) imaging. Five 90Y SIRT treatments were retrospectively analyzed. After 90Y SIRT, patients had 90Y PET/CT imaging within 6 hours of the procedure. To obtain the 3D dose distribution of the patients, their respective 90Y PET images were convolved with a Monte Carlo generated voxel dose kernel. The sensitivity of the PET/CT scanner for 90Y was determined through phantom studies. The 3D dose distributions were then presented in DICOM RT dose format. By applying the linear quadratic model to the dose data, we derived the biologically effective dose and dose equivalent to 2 Gy/fraction delivery, taking into account the spatial and temporal dose rate variations specific for SIRT. Based on this data, we intend to infer tumor control probability and risk of radiation induced liver injury from SIRT by comparison with established dose limits. For the five cases, the mean dose to target ranged from 51.7 ± 28.6 Gy to 163 ± 53.7 Gy. Due to the inhomogeneous nature of the dose distribution, the GTVs were not covered adequately, leading to very low values of tumor control probability. The mean dose to the normal liver ranged from 21.4 ± 30.7 to 36.7 ± 25.9 Gy. According to QUANTEC recommendation, a patient with primary liver cancer and a patient with metastatic liver cancer has more than 5% risk of radiotherapy-induced liver disease (RILD).