This comparative effectiveness study examines current US Centers for Medicare & Medicaid Services 30-day mortality risk models vs novel risk models for acute myocardial infarction, heart failure, and pneumonia hospitalizations. Could present on admission indicators and ungrouped diagnostic codes enhance risk models for acute myocardial infarction, heart failure, and pneumonia mortality measures and improve discrimination of hospital-level performance?International Classification of Diseases, Ninth Revision, Clinical Modification codes was associated with greater discrimination in patient-level and hospital-level 30-day mortality risk models.In this comparative effectiveness study including all Medicare fee-for-service beneficiaries hospitalized for acute myocardial infarction, heart failure, or pneumonia at acute care hospitals, incorporating present on admission coding and ungrouped historical and index admission Changes incurring no additional cost could enhance the risk adjustment for mortality and increase discrimination of hospital-level performance. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) coding system and advances in computational capabilities may provide an opportunity for enhancement.Risk adjustment models using claims-based data are central in evaluating health care performance. Although US Centers for Medicare & Medicaid Services (CMS) models apply well-vetted statistical approaches, recent changes in the To examine whether changes using already available data would enhance risk models and yield greater discrimination in hospital-level performance measures.ICD-9-CM codes from all Medicare fee-for-service beneficiary claims for hospitalizations for acute myocardial infarction (AMI), heart failure (HF), or pneumonia among patients 65 years and older from July 1, 2013, through September 30, 2015. Changes to current CMS mortality risk models were applied incrementally to patient-level models, and the best model was tested on hospital performance measures to model 30-day mortality. Analyses were conducted from April 19, 2018, to September 19, 2018.This comparative effectiveness study used ICD-9-CM codes.The main outcome was all-cause death within 30 days of hospitalization for AMI, HF, or pneumonia, examined using 3 changes to current CMS mortality risk models: (1) incorporating present on admission coding to better exclude potential complications of care, (2) separating index admission diagnoses from those of the 12-month history, and (3) using ungrouped There were 361 175 hospital admissions  for AMI, 716 790 hospital admissions  for HF, and 988 225 hospital admissions  for pneumonia during the study; mean 30-day mortality rates were 13.8% for AMI, 12.1% for HF, and 16.1% for pneumonia. Each change to the models was associated with incremental gains in C statistics. The best model, incorporating all changes, was associated with significantly improved patient-level C statistics, from 0.720 to 0.826 for AMI, 0.685 to 0.776 for HF, and 0.715 to 0.804 for pneumonia. Compared with current CMS models, the best model produced wider predicted probabilities with better calibration and Brier scores. Hospital risk-standardized mortality rates had wider distributions, with more hospitals identified as good or bad performance outliers.ICD-9-CM codes were associated with improved patient-level and hospital-level risk models for mortality compared with the current CMS models for all 3 conditions.Incorporating present on admission coding and using ungrouped index and historical  First, the models could potentially improve by using present on admission (POA) codes. In 2014, the US Centers for Medicare & Medicaid Services (CMS) began mandating hospitals to add POA designations, which denoted conditions that predated the hospitalization.8 Many diagnosis codes used exclusively on the index hospitalization were excluded from models predicting outcomes. For example, the CMS performance models excluded diagnoses because they might have represented complications associated with clinical quality. The use of these codes with the knowledge that they were present on admission would increase the number of codes available for risk adjustment. Second, claims-based models often combine diagnoses into clinically coherent groups to reduce the number of variables. For example, CMS bases many of its models on modifications of the hierarchical condition categories (HCCs) to group codes.10 For example, in the CMS mortality model, the codes for historical diagnoses and procedures from the previous 12 months are combined with codes from the index admission into 1 set of risk variables. A potential limitation is that codes that comprise a group may have different associations with the outcome but the group’s overall effect is a weighted mean. Also, the effect of lower-frequency codes could be overwhelmed by that of higher-frequency codes. Advancements in computational capabilities and analytical methods enable us to handle much larger amounts of information efficiently and provide an opportunity to consider risk variables using ungrouped codes.11Risk models using administrative claims–based data play a central role in evaluating health care performance, setting payments, and conducting research.Accordingly, we tested whether leveraging POA coding and using individual codes rather than grouped codes could be associated with improved model performance. These changes use data already available in claims and incur no additional marginal cost. Specifically, we explored 3 changes to current patient-level risk models: (1) incorporating POA coding to distinguish conditions present at the time of admission from those emerging during hospitalization, (2) separating POA diagnosis codes from those present in encounters during the prior 12 months, and (3) disaggregating codes currently used in the risk variable groupers. The best-performing models were compared with publicly reported CMS hospital performance measures. This assessment focused on the publicly reported 30-day mortality measures for acute myocardial infarction (AMI), heart failure (HF), and pneumonia.5 HF,6 or pneumonia.12 Medicare Standard Analytic and denominator files identified all hospitalizations at acute care hospitals with a principal discharge diagnosis of AMI, HF, or pneumonia from July 1, 2013, through September 30, 2015. We defined cohorts with the same International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)13 codes used in the CMS publicly reported mortality measures. We chose to focus on a period exclusively using ICD-9-CM codes because it was not practical to combine both ICD-9-CM and International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes in the same models. We did not have enough data coded in ICD-10-CM to conduct these analyses exclusively in the ICD-10-CM data. We included hospitalizations for patients 65 years or older. We excluded hospitalizations from which patients were discharged against medical advice and for patients with less than 1 year of prior enrollment in Medicare fee-for-service. We further excluded records for which POA coding was missing for the principal diagnosis of the index admission. We linked transfers into a single episode of care and assigned patients to the index admitting hospital. We used Medicare claims for 12 months before the index admission. The Human Investigation Committee at Yale University approved an exemption for this study to use CMS claims and enrollment data and waived the requirement for informed consent because the research involved no more than minimal risk and could not be practicably carried out without the waiver. Data analyses were conducted from April 19, 2018, to September 19, 2018.We applied the cohort definitions for the CMS 30-day all-cause mortality measures after hospitalization for AMI,The outcome was death from any cause within 30 days of the hospital admission date for AMI, HF, and pneumonia. Death date was identified by data in CMS enrollment files or from inpatient claim discharge status for death during the index hospitalization.ICD-9-CM codes instead. The changes were applied incrementally to patient-level models, with the best model then tested at the hospital level. To develop and test the potential changes, we used 5-fold cross-validation to control for model overfitting, with each iteration training generalized linear models (GLMs) on 80% of the cohort and then testing on the remaining 20%.We tested 3 changes to current risk models: (1) incorporating POA coding to better distinguish conditions that were POA from those that were complications of care, (2) separating diagnosis codes in the index admission from those coded within the prior 12 months, and (3) disaggregating codes within risk variable groupers and using individual 14 We tested whether incorporating POA coding into the CMS model could be associated with improved patient-level mortality model discrimination. The official CMS POA exempt list was reviewed by a panel of clinical experts, and conditions that were judged to have any significant potential to be a complication of care were removed  to capture patient-level severity and to reduce dimensionality and 4 non–diagnosis-based variables . The MCCs were constructed from the 201 condition categories (CCs) that are the bottom level of the version 22 HCCs using previously established methods.ICD-9-CM codes, eliminating the use of a grouper. We replaced the MCCs with a subset of individual ICD-9-CM codes as risk variables. We restricted index admission ICD-9-CM codes to those that were identified as POA or those that were not explicitly identified as not POA while also on the modification of CMS’s POA-exempt list. Also, we considered all individual ICD-9-CM codes in claims of the 12-month history, pooling together inpatient, outpatient, and Part B settings. For CMS risk models, any groupers or risk factors with less than 1% frequency among all admissions are considered low frequency. For our study, we restricted to codes with a frequency of more than 0.5%, keeping index and history ICD-9-CM codes separate.Next, we considered using the full set of CCs. To incrementally explore using more disaggregated groupers, we replaced the current MCCs with all 201 individual CCs. Finally, we investigated using individual P values to reliably converge and be practical for the time-consuming bootstrapping required in CMS methodology. Hence, with the end goal of demonstrating a better separation of performance of the hospitals, we limited the modified patient-level models tested at the hospital level to 200 or fewer variables. The likelihood-ratio test was used to find P values. To further improve HGLM convergence issues, we required variables to have a 2-tailed P value less than .005 in the patient-level GLM.We incrementally incorporated the 3 proposed model changes in risk-adjusted GLMs at the patient level with the goal of choosing the best one to explore hospital-level performance within the practical constraints of fitting hospital-level hierarchical GLMs (HGLMs), given the large number of hospitals and admission records. All the GLM and HGLM models use a binary distribution and a logit link function. In our experience, these hospital-level HGLMs need to be limited to a maximum of approximately 200 variables with low associated ICD-9-CM codes, we used a basic variable-selection strategy. After excluding codes with less than 0.5% frequency, we used a least absolute shrinkage and selection operator parameter grid search to select the best set of variables, with total count not exceeding 200, from the combined set of index admission ICD-9-CM codes, history ICD-9-CM codes, and non–diagnosis-based variables in the CMS models. Next, a GLM was fit using these selected codes, and only those with a P value less than .005 were retained for the final model, hereafter the individual-codes model  and is a combination of several components, including reliability, a measure of the error in a calibration curve (lower is better), and resolution, reflecting how well a model separates predictions from the mean event rate (higher is better). We also compared the calibration slope (closer to 1 is better) and predictive range (wider is better) across both models. We included additional model comparisons, such as receiver operating characteristic curves, calibration plots , and range would indicate that the RSMRs of different hospitals are spread out wider and thus more distinguishable. We used the F test  to examine whether between-hospital variances from HGLM models were equal. We used folded F tests to compare variances of the RSMR distributions. We also calculated the weighted Pearson correlation coefficient between the RSMRs using the CMS model and the individual-codes model, using the inverse of the variance of ICD-9-CM RSMRs as weights.19 Hospital RSMR variances were calculated using bootstrap methods defined in CMS public reporting.14 We used 5000 bootstraps for each condition for the CMS and individual-codes models.To assess the association of the proposed changes to patient-level risk adjustment with hospital performance profiling, we used the CMS HGLM approach to calculate the hospital risk-standardized mortality rates (RSMRs).20 CMS reports annually on hospitals with at least 25 cases of AMI, HF, or pneumonia, using the 3 performance categories of no different than the national rate, better than the national rate, or worse than the national rate. In addition to point estimate and CI, we classified hospitals into these categories by whether RSMR CIs contained the national rate.14 We compared the frequency of hospitals in each category and studied the hospital category shifts between the CMS and individual-codes model.On its public Hospital Compare website,All GLM-related analyses were conducted in R statistical software version 3.3  using the stats package or in Python version 2.7 (Python Software Foundation) using the scikit-learn or StatsModels packages. Our least absolute shrinkage and selection operator analysis was implemented using the GLMNET library in both R and Python. All hospital-level analyses were performed using SAS statistical software version 9.4 (SAS Institute).The AMI, HF, and pneumonia cohorts are described in P < .001), 0.685 to 0.776 for HF (P < .001), and 0.715 to 0.804 for pneumonia (P < .001).Each additional change to the models resulted in incremental gains in C statistics . The hig14 is trained and tested on the full cohort. Using the individual index POA and history codes selected by our method  was associated with substantially improved model performance compared with the CMS risk model for all 3 mortality measures. We found significantly higher C statistics for the individual-codes models for all 3 conditions, increasing from 0.720 to 0.828 for AMI, 0.685 to 0.778 for HF, and 0.715 to 0.805 for pneumonia. Brier scores for the individual-codes models were 0.092 for AMI, 0.102 for HF, and 0.111 for pneumonia; for the CMS models, the Brier scores were 0.110 for AMI, 0.102 for HF, and 0.126 for pneumonia. Resolution scores were 0.027 for AMI, 0.013 for HF, and 0.025 for pneumonia for individual-codes models and 0.0086 for AMI, 0.0044 for HF, and 0.001 for pneumonia for the CMS models. The 2 models had similar reliability for all 3 conditions.The range of predicted probabilities of 30-day mortality was wider for the individual-codes models than for the CMS models . Standard deviations of the predicted probabilities were also higher for the individual-codes models than for CMS models .A total of 4036 hospitals contributed claims for the AMI cohort, 4380 hospitals contributed claims for HF, and 4462 hospitals contributed claims for pneumonia. Of these, there were at least 25 cases at 2218 hospitals for AMI, 3323 hospitals for HF, and 3877 hospitals for pneumonia.t test comparing the RSMRs from the 2 models had P < .001), while the AMI RSMR distribution appeared to be wider but not statistically significant (P < .001), 0.051 to 0.071 for HF (P < .001), and 0.048 to 0.180 for pneumonia (P < .001). The SDs, IQRs, and ranges for RSMRs using individual-codes models were also higher for all 3 conditions (The RSMRs calculated using individual-codes models had a wider distribution compared with those using CMS models for HF and pneumonia measures (nificant . The betnditions . The modWhen comparing performance categories assigned by the 2 models , the ind21We introduce a new approach to the risk adjustment used in conducting research and profiling hospital mortality performance. The proposed methods use data already available in claims and therefore incur no additional marginal cost to include in risk models. By leveraging the introduction of POA coding, separating codes first noted on the index admission from those of the prior year, and disaggregating previously grouped codes, we markedly improved the discrimination of the risk-adjusted models for 30-day AMI, HF, and pneumonia mortality. In hospital profiling, this approach allowed us to detect more variation, identifying many more hospitals as better or worse than the national mean, which is important because lack of variation in performance has been identified as a problem with the current measures.The improvement associated with the use of POA coding would be easy to implement. For example, CMS 30-day mortality outcome measures, developed before POA information was reliably available, exclude a list of diagnoses that have the potential to be complications of care but could also convey valuable information about preadmission risk. Using the POA coding that is now available yields substantially improved patient-level model performance across all 3 conditions. Also, separating historical and index diagnoses was associated with model improvement. Incorporating these changes into the risk models is not difficult. Experiments using custom, data-driven groupers showed some improvement over traditional groupers, with better results as the number of categories increased and the sizes of the individual categories decreased. However, the most substantial improvement was associated with replacing risk factors based on traditional groupers with a larger number of variables consisting of individual diagnosis codes. Groupers may obscure low-frequency codes and the heterogeneity of effect among the constituent codes.There are several implications of the findings of our approach to use ungrouped diagnostic codes with their associated POA designations and index or prior timing. They provide a rationale for revisiting the prior assumptions about our approach to risk adjustment for outcomes performance models. Groupers have been used to efficiently handle extremely large sets of variables and simplify the computations to model them, but we found that the grouping of variables may not be necessary owing to the availability of computationally advanced algorithms. Many stakeholders potentially benefit from more accurate measures.Although examination of improvements in measure score validity resulting from these methods was beyond the scope of this study, the gains demonstrated in discrimination of the patient-level models could translate into more precise characterization of hospitals’ performance. More investigation is required to reach such a conclusion. However, because these measures are currently used to profile hospital quality and to assess payments for better or worse performance, there would be a clear benefit to patients and hospitals if gains in the performance of the patient-level model also enhanced the measures’ accuracy.ICD-9-CM codes because of our interest in using a large sample and not mixing ICD-9-CM and ICD-10-CM codes. Our approach would need to be thoroughly tested with ICD-10-CM codes, although, from preliminary experiments, we do not expect the results to change substantially, even with a potentially greater impact from the 0.5% frequency restriction. With the greater specificity of ICD-10-CM codes, performance may improve further. Another potential issue is that our approach could ignore low-frequency codes; however, they do not exert much influence in groupers even when included. Additionally, using least absolute shrinkage and selection operator analysis for code selection could result in some relevant codes being dropped owing to correlation, possibly diminishing predictive power. We chose this method owing to its ease in selecting a specified number of codes as required by our hospital-level algorithm and to avoid possible issues that multicollinearity can cause in HGLM.22 Nevertheless, we were consistent in our approach in all of the new models, so it should not have biased our comparative assessment. Also, we chose not to modify the currently used hospital-level HGLM method. However, a 2-stage HGLM or other novel methods would have allowed us to consider more sophisticated machine learning methods for patient-level risk adjustment.24 In addition, our approach may not be applicable for data with extreme outcome rates in combination with extreme risk factor prevalence in which using logistic regression would be problematic owing to the complete or quasi-complete separation. Moreover, the large number of variables included in the final models reduce the computational efficiency. Also, to our knowledge, there are few contemporary studies on the accuracy of POA codes, and the studies that have been published provide conflicting results on their accuracy.27 As such, there is a need for more research on this topic. Additionally, the use of individual diagnostic codes could inadvertently augment the effect of clinically insignificant differences in code use among hospitals. To address this possibility, it might be necessary to monitor the coding and potentially reselect codes and refit the model at more frequent intervals.There are several potential limitations to consider. We restricted our analysis to claims using ICD-9-CM codes was associated with substantially improved patient-level and hospital-level models for AMI, HF, and pneumonia mortality. Disaggregating the groupers appeared to provide the largest improvement in many of the risk models. It also enabled hospital performance measures to identify more hospitals as better or worse than the national mean for all 3 conditions. These findings suggest that there may be opportunities to improve the risk models for research and outcome performance measures. In particular, further investigation of the effect of such changes on measure score validity might provide additional support for incorporating these methods in the future.We identify several key strategies, made possible in part by an evolution of claims coding, to improve prediction models with implications for research and performance measurement. We found that incorporating POA coding into risk variable definitions, distinguishing between diagnoses coded in the claims within 12 months before the index admission from those coded at admission, and disaggregating from groupers into individual


Gastric cancer is the third leading cause of cancer death in the world. The benefit of adjuvant chemotherapy has been demonstrated by published individual patient data meta-analysis and Cochrane systematic review. However, there is no consensus on which is the optimal adjuvant chemotherapy regimens. Present network meta-analysis aims to compare the differences of effect between all available adjuvant chemotherapy regimens in improving overall survival and disease-free survival, and to rate the certainty of evidence from present network meta-analysis.http://clinicaltrials.gov/) to identify randomized controlled trials (RCTs) comparing adjuvant chemotherapy to surgery alone. We will assess the risk of bias of individual RCTs using a modified version of Cochrane tool. We will also use the advance of GRADE to rate the certainty of network meta-analysis. Data analysis will be performed with R-3.4.1 and WinBUGS software.We will conduct this systematic review and network meta-analysis using Bayesian method and according to Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) statement. We will search PubMed, EMBASE.com, the Cochrane Central Register of Controlled Trials (CENTRAL), Chinese National Knowledge Infrastructure (CNKI), and Chinese Biological Medical Database (CBM), and ClinicalTrials.gov (The results of this study will be published in a peer-reviewed journal.To the best of our knowledge, this systematic review and network meta-analysis will firstly use both direct and indirect evidence to compare the differences of all available adjuvant chemotherapy regimens for resected gastric cancer patients. This is a protocol of systematic review and meta-analysis, so the ethical approval and patient consent are not required. Although surgical resection remains the cornerstone of treatment and the only curative treatment for patients with gastric cancer, 40% to 80% of patients die of disease relapse. Meta-analyses have shown improved survival in gastric cancer patients treated with adjuvant chemotherapy compared to those who underwent surgery alone, especially in Asia.–7 An individual patient data meta-analysis including 17 randomized controlled trials (RCTs) demonstrated that adjuvant chemotherapy was associated with a statistically significant benefit in terms of overall survival (OS)  and disease-free survival (DFS) . A Cochrane systematic review published in 2013 also demonstrated the benefit of adjuvant chemotherapy in resected gastric cancer. However, there is no consensus on which is the optimal adjuvant chemotherapy regimens. The national and international guidelines also showed inconsistent recommendations of adjutant chemotherapy. For example, ESMO clinical guidelines recommended 5-FU-based chemotherapy for patients with ≥Stage IB gastric cancer who have undergone surgery without administration of preoperative chemotherapy, and S-1 following D2 resection for Asian patients. While National Comprehensive Cancer Network (NCCN) gastric cancer recommended capecitabine combined with oxaliplatin following primary D2 lymph node dissection. Well-conducted conventional meta-analyses of RCTs are accepted as the best-quality evidence to inform clinical practice and health policy.,11 However, it has been impractical to test all available intervention directly in a conventional meta-analysis or RCT. Network meta-analysis has become increasingly popular to evaluate healthcare interventions since it allows for estimation of the relative effectiveness among all interventions and rank ordering of the interventions even if head-to-head comparisons are lacking.,13Gastric cancer is the third leading cause of cancer death in the world.,15 Although a network meta-analysis published in 2014 compared several adjuvant chemotherapy regimens, they failed to include some main adjuvant chemotherapy regimens such as XELOX  and S-1 regimens. In addition, there are some new trials comparing other adjuvant chemotherapy and long-term results of previous RCTs have been updated.,18 What's more, all the results of the above network meta-analyses were summarized as relative treatment effects. However, absolute effects are more straightforward for shared clinical decision making. Although network meta-analysis is highly attractive, the treatment rankings and effect estimates often are uncertain and imprecise.,21 GRADE approach should be used to rate the confidence or certainty of evidence from network meta-analysis, which could reflect the extent to which confidence in an estimate of the effect.Two recent network meta-analyses have focused on the relevant effective of adjuvant chemotherapy, adjuvant radiotherapy and adjuvant radiochemotherapy in resected gastric cancer, however, they failed to compare different adjuvant chemotherapy regimens.Present study will systematically collect all available RCTs that compared different adjuvant chemotherapy regimens with surgery resection alone, to comprehensively compare the differences between all available adjuvant chemotherapy regimens in improving OS and DFS, and to rate the certainty of evidence from present network meta-analysis.2 and this network meta-analysis will be performed and reported in accordance with PRISMA Extension version (PRISMA-NMA). As a part of our project, this study protocol has been registered on the international prospective register of systematic review (PROSPERO) (CRD42018104782).This protocol will be reported according to preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P),2.1 Type of participants: eligible participants are age of 18 years or older, have undergone partial or total gastrectomy, irrespective of the location of the cancer. Trials including patients with oesophagogastric junction will be excluded.  Type of design: Only RCTs focusing on different adjuvant chemotherapy for patients with resected gastric cancer.  Type of interventions: surgery alone and all adjuvant chemotherapy regimens.  Type of outcomes: the outcomes of interest will include OS, which is defined as the time from randomization until the date of death, and DFS, which is the time from date of random assignment to date of recurrence or death.  Other criteria: there are no limitations on language of publication, year of publication, publication status.Studies will be included in this systematic review and network meta-analysis if meet the following eligibility criteria: We excluded RCTs assessing the following interventions: preoperative and perioperative treatments, postoperative radiotherapy and radiochemotherapy, and immunotherapy.2.2http://clinicaltrials.gov/) will be searched. The search strategies have been developed by LG and reviewed by an experienced librarian researcher (KHY) to improve the search quality. The references of relevant systematic reviews/meta-analyses will be tracked to identify additional studies. The PubMed search strategies as follows,PubMed, EMBASE.com, the Cochrane Central Register of Controlled Trials (CENTRAL), Chinese National Knowledge Infrastructure (CNKI), and Chinese Biological Medical Database (CBM), and ClinicalTrials.gov  to collect data of interest, which including general characteristics of included trials , details of participants , details of interventions , and outcomes .A standard data extraction form will be created using Microsoft Excel 2013  and “Definitely or Probably No” (assigned a high risk of bias). The modified tool includes the method of random sequence generation, concealment of treatment allocation; blinding ; infrequent missing outcome data and free from selective outcome reporting. We will use a threshold of >20% missing data as indicative of high risk of bias for missing data.Paired reviewers will evaluate independently the risk of bias of included RCTs using a modified version of Cochrane tool,2.6A network plot will be drawn to describe and present the geometry of the treatment network of comparisons across trials to ensure if a network meta-analysis is feasible. Trials will be excluded from network meta-analysis and just describe the findings of studies if the trials are not connected by treatments. Network geometry will use nodes to represent different chemotherapy regimens and edges to represent the head-to-head comparisons between network nodes. The size of nodes and thickness of edges are associated with sample sizes of intervention and numbers of included trials, respectively.We will categorize the network nodes as follows: surgery alone, S1, XELOX, S1 + paclitaxel, S1 + platinum, 5-Fu alone, etoposide + doxorubicin + cisplatin (EAP), epirubicin + formyltetrahydrofolate + 5-fluorouracil-based (ELF-based), formyltetrahydrofolate + 5-fluorouracil (LF), 5-fluorouracil + doxorubicin + mitomycin C (FMA), irinotecan-based, other 5-Fu-based two drugs, other 5-Fu-based 3 drugs.2.7gemtc” package version 0.8–2 of R-3.4.1 software. The function mtc.run will be used to generate samples from using the Markov Chains Monte Carlo sampler. Four Markov Chains will be run simultaneously. We will set 10 000 simulations for each chain as the ‘burn-in’ period. Then posterior summaries are based on 100 000 subsequent simulations. The model convergence will be assessed using Brooks–Gelman–Rubin plots method.A Bayesian network meta-analysis will be performed using package “Pooled risk ratios (RRs) with 95% credible intervals (CrIs) will be calculated for 5-years OS and 5-years DFS. Pooled HRs with 95% CrI for OS and DFS. In addition, rank probabilities will be calculated, which indicates the probability for each treatment to be best, second best, etc. Finally, we will present pooled absolute effect differences for 5-years OS and 5-years DFS. To estimate absolute effect for 5-years OS and 5-years DFS, we will use median baseline risk from surgery alone and will apply it to the relative effect from the network meta-analysis.2 statistics, which represents the proportion of heterogeneity that is not due to chance . The values of 25%, 50%, and 75% for the I2 as indicative of low, moderate, and high statistical heterogeneity, respectively. In addition, we will assess the global heterogeneity from the network meta-analysis models using the mtc.anohe command of the “gemtc” package.Conventional meta-analyses will be conducted using a random effects model. Heterogeneity across head-to-head trials will be assessed using IRandom effects and fixed effect models network meta-analyses will be conducted and the deviance information criterion (DIC) will be used to compare model fit and parsimony. The model with the lowest DIC will be preferred (differences >3 are considered meaningful). We will assess statistical inconsistency between direct and indirect evidence at the paired comparison level using node splitting method.We will also conduct subgroup analyses and network meta-regression analyses to explore statistical heterogeneity across trials and inconsistency between direct and indirect evidence. We will focus on following possible effect modifiers: gender, age, and percentage of total gastrectomy.We will use STATA version 15.1 software  to draw a comparison-adjusted funnel plot to identify whether there will be a small sample effect among the networks.2.8,35Two trained GRADE methodologists will use the GRADE framework to rate the certainty of evidence and classify evidence as high, moderate, low, or very low certainty. The starting point for certainty in estimates for RCTs is high but may be rated down based on limitations in risk of bias, imprecision, inconsistency, indirectness, and publication bias. The evidence for indirect will focus on the dominant first order loop, rating certainty of indirect evidence as the lowest certainty of the direct comparisons informing that dominant loop. In the absence of a first order loop, we will use a higher order loop to rate certainty in evidence and, similarly, we will use the lowest of the ratings of certainty for the direct estimates contributing to the loop. We will consider further rating down each indirect comparison for intransitivity if the distribution of potential effect modifiers is different in the 2 contributing direct comparisons. For the network estimate, we start with the certainty of evidence from the direct or indirect evidence that dominates the comparison and subsequently consider rating down our certainty in the network estimate for incoherence between the indirect and direct estimates. We also rate down the certainty of evidence if there is imprecision (wide CrIs) around the network estimates.We will rate the certainty of evidence for each direct comparison according to traditional GRADE guidance without imprecision domain for pairwise meta-analysis.LG and KHY planned and designed the research; LG, YTW, LYH, and QXY tested the feasibility of the study; LG and JL wrote the manuscript; all authors approved the final version of the manuscript.Conceptualization: Long Ge, Kehu Yang.Investigation: Long Ge, Liangying Hou, Qingxia Yang, Yiting Wu, Jiang Li.Methodology: Long Ge.Project administration: Long Ge, Liangying Hou, Qingxia Yang, Yiting Wu, Xiue Shi, Jiang Li.Supervision: Xiue Shi, Kehu Yang.Writing – original draft: Long Ge, Jiang Li.Writing – review & editing: Long Ge, Liangying Hou, Qingxia Yang, Yiting Wu, Xiue Shi, Jiang Li, Kehu Yang.


The chance of surviving an out-of-hospital cardiac arrest (OHCA) seems to be increased if the cardiac arrests occurs in relation to exercise. Hypothetically, an exercise-related OHCA at a sports arena would have an even better prognosis, because of an increased likelihood of bystander cardiopulmonary resuscitation (CPR) and higher availability of automated external defibrillators (AEDs). The purpose of the study was to compare survival rates between exercise-related OHCA at sports arenas versus outside of sports arenas.Data from all treated exercise-related OHCA outside home reported to the Swedish Register of Cardiopulmonary Resuscitation (SRCR) from 2011 to 2014 in 10 counties of Sweden was analyzed (population 6 million). The registry has in those counties a coverage of almost 100% of all OHCAs.3714 cases of OHCA outside of home were found. Amongst them, 268(7%) were exercise-related and 164 (61.2%) of those occurred at sports arenas. The 30-day survival rate was higher for exercise-related OHCA at sports arenas compared to outside . OHCA-victims at sports arenas were younger , less likely female  and had a higher frequency of shockable rhythm . OHCAs at arenas were more often witnessed , received bystander CPR to a higher extent  and the AED-use before EMS-arrival was also higher in this group .The prognosis is markedly better for exercise-related OHCA occurring at sports arenas compared to outside. Victims of exercise-related OHCA at sports arenas are more likely to receive bystander CPR and to be connected to a public AED. These findings support an increased use of public AEDs and implementation of Medical Action Plans (MAP), to possibly increase survival of exercise-related OHCA even further. Physical activity has long-term health benefits on cardiovascular health, including reduced mortality in cardiovascular disease. –4 ConseqThe chance of surviving an out-of-hospital cardiac arrest (OHCA) has increased over the years due to improvements in several links in the classical “chain of survival”, although survival rates are still relatively low (2–20%). –12 SpeciHypothetically, an exercise-related cardiac arrest occurring at a sports arena would have an even better prognosis in comparison to an exercise-related arrest occurring outside of such facility, since the likelihood of bystander CPR and availability of an automated external defibrillator (AED) is presumably even higher in this setting. A previous French study in fact reported higher survival rates in SCAs at sports arenas compared to those occurring outside of sports facilities. Interestingly, this was found despite a very low frequency of AED use (<1%).  Further Patients suffering exercise-related SCAs are in general younger (on average 10 years younger) compared to victims of non-exercise-related cardiac arrests, implying a lesser degree of coronary artery disease (CAD) in exercise-related SCA-victims. Still, the significant difference in survival rate remains after adjusting for age. –14 ParadThe primary objective of the present study was to compare survival rates between exercise-related sudden cardiac arrests occurring at sports arenas versus outside of sports arenas, to better understand the higher survival rates in exercise-related OHCA. We also aimed to study whether there are other factors than exercise contributing to the high survival rate of exercise-related OHCA.This is a retrospective study from the Swedish Registry of Cardiopulmonary Resuscitation (SRCR), a registry which has previously been described in more detail.  All EmerThe study period reached from 1 January 2011 to 31 December 2014 and the study population consisted of inhabitants of ten Swedish counties. In total, the population was 6,092,861 in the year 2011 compared with 6,289,418 in the year 2014, representing 65% of Sweden’s population. Ethical permission was authorised by the ethical committee in Stockholm (Reference number 2015/1122-31/5).All cases of OHCA occurring outside of home in the ten counties included in the study, were identified using the SRCR. Out of those OHCAs, a selection was made due to whether the cardiac arrest was exercise-related or not. This selection was made by manually search of the report sheet from EMS crew and CPR coordinators as well as EMS medical records.Exercise-related SCA/SCD was defined as a cardiac arrest occurring during or within one hour after exercise, according to international standards, , 20, 23 Data variables collected from the SRCR and from EMS journals, for each case of exercise-related OHCA were reported in regards to UTSTEIN style  and inclA comparative statistical analysis was made betweenOut of Hospital Cardiac Arrests that took place outside home and were related to exercise and cases that took place outside home and were not related to exercise.Here, the study population was also further subdivided according to age and sex and separate analyses were made among patients 0–35 years, 36–65 years and > 65 years.Exercise-related OHCAs occurring at sports arenas and the exercise-related OHCAs occurring outside of sports arenas.Descriptive statistics was used, and variables are presented as numbers, percentages, mean and median. In the comparison of proportions, Fishers Exact Test was used. In the comparison of continuous variables, Wilcoxons two-sample test was used. Differences in age adjusted survival was estimated with logistic regression and described as Odds Ratio (OR) with 95% Confidence Interval (CI). A p-value <0.05 was regarded as significant. Two sided tests were applied.During the study period a total number of 3714 OHCAs occurring outside of home and where resuscitation was attempted were identified. Of these, 268 (7%) cases were exercise-related. The 30-day survival rate of exercise-related OHCA was 46.3% compared to 17.5% in the non-exercise-related group(p<0.0001). The age adjusted OR and 95% CI for survival to 30 days was 3.77;2.88–4.92; p<0.0001 when comparing exercise related OHCA with non-exercise related OHCA.Characteristics of the exercise-related OHCAs according to their site of occurrence are presented in Out-of-hospital cardiac arrests occurring at sports facilities were more likely to be bystander witnessed  and were also more frequently receiving CPR before arrival of EMS . AED-connection prior to EMS-arrival was more likely to occur when the OHCA took place at a sports arena compared to outside . Initial rhythm differed significantly between the groups, with a higher frequency of shockable rhythm i.e. ventricular tachycardia or ventricular fibrillation for cardiac arrests at sports arenas .Delay time from collapse to call for EMS did not differ between the groups  nor was there a significant difference in the median delay time from call to arrival of EMS . The median delay time from collapse to start of CPR  and from collapse to defibrillation  differed in favor of occurrence at sports facilities.Survival at 30 days was higher among exercise-related OHCA occurring at sports arenas compared to those occurring outside . The age adjusted OR and 95% CI was 2.85; 1.64–5.02; p = 0.0002 when exercise-related OHCA at sports arena were compared with those occurring outside. Return of spontaneous circulation at any time was more common in the sports arena group  and patients in this group more often had spontaneous circulation at hospital admission .Victims in both groups were considered to have an OHCA of cardiac origin to the same extent .The higher survival rates among exercise-related OHCA remained significant throughout the study population when comparing different age-groups. There were in total 22 cases and 13 survivors of exercise-related OHCA in the age-group 0–35 years and a survival rate of 59.1% compared to 16.4% in the non-exercise-related group, which included 214 cases and 35 survivors(p<0.0001). In the age group 36–65 years, there were 68 survivors out of 136 cases in the exercise-related group and 239 survivors out of 1096 in the non-exercise-related group, with a survival rate of 50% vs 21.8%(p<0.0001). Among victims older than 65 years, 36 out of 90 survived their exercise-related OHCA compared to 293 out of the 1812 who suffered a non-exercise-related OHCA, resulting in a difference in survival rate of 40.0% vs 16.2%(p<0.0001). Survival rates in relation to exercise, gender and age are presented in Male victims suffering OHCA in relation to exercise had significantly higher survival rates in comparison to male victims of non-exercise-related OHCA . On the other hand, there was no statistically significant difference in survival rates comparing female victims of exercise-related OHCA with females suffering non-exercise-related cardiac arrest . However, the number of females with OHCA was much lower.n = 34, 13%), followed by running , golf  and gym workout/group training . Number of cardiac arrests in relation to sport activity and location is presented in The most common sport activity associated with exercise-related OHCA was cycling  in the French study compared to the present Swedish study, which report an AED use of 29.8% in sports facilities. Another difference was the lower frequency of bystander CPR in the French study (35.4% at arenas), despite a very high witness degree (99.8%).  Several The highest survival rate was found in the age group 0–35 years (59.1%), not supporting some studies showing a lower survival rate in the young, athletic SCA-patients , 18 The A surprise finding was that, while females have a lower incidence of exercise-related OHCA overall, compared to men, , 27–28 tThe present study has several important clinical implications. Previous studies have demonstrated the benefits of public CPR and AEDs. For example, the use of AEDs has resulted in higher survival rates in US High Schools, –33 airpoThe present study has its limitations. The low number of cases in the youngest age group (0–35 years) as well as the low number of female victims make it difficult to draw firm conclusions about survival in these groups. Although the SRCR covers nearly 100% of all OHCAs were resuscitation has been attempted, there is still a risk that some unwitnessed exercise-related OHCA are not reported or wrongly classified as non-exercise-related. Furthermore, in some non-witnessed cases there is a possibility that CPR was never attempted. We do lack full information about the underlying diagnosis of the OHCAs and the assessment whether the OHCA is of cardiac origin or not is made by EMS crew, which is a potential limitation. Finally, the study cohort includes patients up to the end of 2014 only. However, during the last three years the overall 30-day survival rate of OHCA has remained unchanged. In conclusion, survival rates are markedly better in exercise-related OHCA occurring at sports arenas vs outside arenas. A significantly higher degree of witnessed arrests and AED use at sports arenas are most likely of great importance. Our results emphasize a more widespread use of AEDs and the implementation of a structured emergency preparedness as part of MAPs, not just at sports arenas but also in other public locations where people engage in exercise. Interestingly, the survival rate was higher for exercise-related OHCA at all ages, while there was a tendency towards worse prognosis for females, encouraging further studies on mechanisms of exercise-related OHCA in different age groups and among females.


Previous studies showed that troponin blood levels may increase after exercise. In this study we assessed whether, among patients admitted with suspected unstable angina, the increase in high-sensitive troponin T (hs-TnT) levels after exercise stress test (EST) might help identify those with obstructive coronary artery disease (CAD) and predict symptom recurrence during short term follow-up.Maximal treadmill EST was performed in 69 consecutive patients admitted to the emergency room with a suspicion of unstable angina  was measured before and 4 hours after EST. Coronary angiography was performed in 22 patients (32.8%).hs-TnT increased after EST compared to baseline in the whole population . The increase was similar in patients with positive (n = 14) and negative (n = 55) EST (p = 0.72), and was also similar in patients with (n = 12) and without (n = 10) obstructive CAD at angiography (p = 0.91). The achievement of a heart rate at peak EST ≥85% of that predicted for age was the variable mainly associated with the post-EST hs-TnT increase at multivariable linear regression analysis (p = 0.005). The change after EST of hs-TnT did not predict the recurrence of symptoms or readmission for chest pain at 6-month follow-up.Our data show that hs-TnT increased after EST in patients with suspected unstable angina, which seemed largely independent of most clinical and laboratory variables. Thus, hs-TnT assessed after EST does not seem to be helpful to identify patients with obstructive CAD in this kind of patients. The detection of increased blood levels of troponins has become the reference marker for the diagnosis of acute myocardial infarction ,2. HowevAll previous studies, however, were conducted in patients with stable forms of definite or suspected CAD, or even in apparently healthy subjects. No previous study, instead, investigated the response to exercise of troponins in patients with a suspicion of acute coronary syndrome (ACS). In these patients the detection of ischemic abnormalities at the electrocardiogram (ECG) and/or increased levels of troponins is usually an indication to admission and invasive management –20. In tThus, the primary aim of this study was to assess whether the changes of troponin serum levels after EST might help identify, among patients admitted with acute chest pain suggesting unstable unstable angina, those with significant atherothrombotic CAD. Furthermore, we assessed whether the post-EST troponin levels might be helpful to identify those with recurrence of symptoms over a 6-month period of follow-up.We studied consecutive patients admitted to the Emergency Department of our hospitals for acute chest pain suspected for NSTE-ACS, showing normal ECG and normal serum levels of routine cardiac troponins on two determinations obtained with an interval of 6 hours (possible unstable angina).Patients were excluded if they had evidence of any other relevant cardiac or systemic disease, increased creatinine serum levels or ECG abnormalities that precluded a careful assessment of ST-segment changes. Patients were also excluded if they had a history of previous acute myocardial infarction, coronary by-pass surgery or percutaneous coronary intervention (PCI) with incomplete revascularization. Patients were included, however, in case of previous PCI with documentation of complete revascularization.The presence of cardiovascular risk factors was carefully assessed and drug therapy recorded. Hypertension was defined as the detection of blood pressure (BP) ≥140/90 mmHg or use of anti-hypertensive drugs. Hypercholesterolemia was defined as total cholesterol levels >220 mg/dL or use of anti-cholesterolemic drugs. Diabetes mellitus was defined as the detection of use of antidiabetic drugs or a glycated hemoglobin >6.5%. Active smoking was defined as having smoked any cigarettes in the last 6 months.The study protocol was approved by the Review Boards of our Institutions  and a written informed consent to participate in the study was always obtained from patients.All patients underwent a symptom/sign-limited Bruce treadmill EST under continuous ECG monitoring as soon as possible after the second determination of serum troponin was available. EST was considered positive for myocardial ischemia when a horizontal or downsloping ST-segment depression ≥1 mm or an upsloping ST-segment depression ≥1.5 mm at 0.08 s from the J point was detected.Criteria for EST interruption included physical exhaustion, worsening symptoms , occurrence of any potentially dangerous clinical condition , ST-segment elevation ≥1 mm or ST-segment depression ≥3 mm in two or more contiguous leads.th URL percentiles being 1.4 ng/dL. Accordingly, hs-TnT levels measured immediately before and 4 hours after exercise were unknown to the clinicians and, therefore, in no case could be used to guide patients’ management.The clinical management of the patients following EST was left at the total discretion of the attending physicians and consultant cardiologist of the emergency department. Blood samples were collected immediately before and 4 hours after peak EST. Samples were centrifuged at 2800 rpm for 20 min, and serum aliquots of 1.5 mL were stored for subsequent analyses. hs-TnT levels were measured after the completion of the study using a high sensitivity electro-chemio-immune-luminescence assay , with the 99When indicated by caring physicians, coronary angiography was performed by radial or femoral artery access using standard procedures. Significant CAD was defined as the presence of a ≥50% diameter stenosis or evidence, with the help of intracoronary ultrasound or optical coherence tomography, of thrombotic lesions in any epicardial coronary vessel.A clinical follow-up was conducted at 6 months by on-site visit or telephone interview. Clinical end-points included death, recurrence of chest pain and re-admission for acute chest pain.Statistical analyses were performed by SPSS 21.0 statistical software . The Kolmogorov-Smirnov test was applied to assess whether continuous variables had a distribution significantly different from the normal one. Since both basal and post-EST hs-TnT levels showed a non-normal distribution, a logarithmic transformation of values was done and all statistical analyses were done by parametric tests using logarithmic values of hs-TnT concentrations, although raw data are shown in the text. Between-group comparisons of baseline continuous variables were done by independent t-test, whereas proportions were compared by Fisher exact test. The influence of variables on the EST-induced changes of hs-TnT was first assessed by two-way repeated measure analysis of variance (ANOVA). To this aim, continuous variables were dichotomized according to pre-specified cut-off values, including: 1) age ≥65 vs. <65 years; 2) HR at peak EST ≥85% vs. <85% of maximal HR predicted for age; 3) systolic BP at peak EST >median vs. ≤median value. In case of global statistical significance post-hoc intra-group comparisons were done by paired t-test. Multivariable linear regression was applied to identify variables independently associated with the increase of hs-TnT after EST, including in the models only variables showing a significant or borderline statistical association (p≤0.1) with the increase of hs-TnT at univariable analysis. Data are reported as means with standard deviations or proportions unless differently indicated. A p<0.05 was always required for statistical significance.Overall, 69 patients were enrolled in the study. The main clinical characteristics of the patients are summarized in Serum hs-TnT concentrations increased 4 hours after EST compared to baseline in the whole population  . OverallThe main clinical characteristics of patients and the results of EST are summarized in Overall, 22 patients (32.8%) were referred for coronary angiography by the attending clinicians, including all 14 patients with positive EST and 8 out of 55 patients (14.6%) with negative EST. The reason for indication to coronary angiography despite negative EST in the latter 8 patients was a persisting suspect by the attending physicians of an ischemic origin of the index chest pain, based on clinical characteristics, particularly in patients with a previous history of PCI. Indeed, 6 out of these 8 patients (75%) had undergone a previous PCI vs. only 5 out of 47 patients (10.6%) with negative EST who were discharged without undergoing coronary angiography (p<0.01). Obstructive CAD was found in 12 patients (54.5%), 7 with positive EST (50%) and 5 with negative EST . Basal hs-TnT tended to be higher in patients with compared to those without obstructive CAD . However, a similar increase in hs-TnT (p<0.01 for both) was found in the 2 groups after EST  of the maximal value predicted for age.The results of multivariable analyses are summarized in During 6-month follow-up no major clinical event occurred. Overall, 23 patients (33.3%) reported recurrence of chest pain and 6 patients (8.7%) were readmitted to hospital for acute chest pain. Both basal hs-TnT and hs-TnT changes after exercise did not differ between patients with or without symptoms at follow-up .This is the first study that assessed whether the changes in cardiac troponins after an EST in patients with a suspicion of unstable angina may be helpful to identify those who actually have atherothrombotic disease and/or are at increased risk of events during short-term follow-up.Our data show that hs-TnT increased significantly after EST in patients with a clinical presentation of suspected unstable angina, but the increase was independent of the evidence of myocardial ischemia at EST and obstructive CAD at angiography, thus suggesting that it was largely related to exercise in itself. Of note, the changes did not also predict the recurrence of chest pain over a follow-up period of 6 months.The increase of troponins after exercise was reported in several previous studies that involved clinically stable patients and apparently healthy subjects but showed some discordant results. Some studies, indeed, reported a greater increase of troponins in ischemic, compared to non-ischemic, patients ,8,11, buA practical consequence of our data is that, in patients with chest pain suspected for an acute coronary syndrome who show a small increase of serum hs-TnT levels, it is mandatory to exclude, together with other non-ischemic conditions able to increase troponin levels ,4,22,23,It is worth noting that the increase of hs-TnT in our patients was also largely independent of various clinical and EST variables, although, at univariable analysis, it was found greater in hypertensive patients and in patients who during EST achieved a maximal HR ≥85% of that predicted for age, while no significant changes were detected in diabetic patients. The greater hs-TnT increase after exercise in patients achieving higher HR and in hypertensive patients can be attributed to the high cardiac workload and a likely increased left ventricle mass , respectImportantly, however, at multivariable linear regression analyses, however, only HR at peak EST emerged as the variable that independently influenced the EST-related increase of hs-TnT, thus suggesting that a higher cardiac myocardial oxygen consumption and work played a major role in the increased levels of hs-TnT with exercise.Some limitations of our study should be acknowledged. First, the number of patients included in the study  was rather small, and therefore our data should be considered as explorative and confirmed in larger populations. Second, we measured hs-TnT after 4 hours from exercise only; thus, we cannot exclude that a higher proportion of patients might have displayed increased hs-TnT levels if other blood samples had been obtained later ; unfortunately, this was not possible as patients not admitted to hospital for further assessment had to be discharged as soon as possible. Moreover, for the same reason, we could not obtain multiple samples from patients after EST; thus, whether the curve of the changes in hs-TnT concentrations over time, derived from multiple blood samples, might be helpful to identify patients with obstructive CAD remains to be established. Third, the diabetic status of patients was only assessed on admission by antidiabetic therapy and measurement of glycated hemoglobin; a careful assessment of the glycemic status could not be performed in all patients, indeed, due to the early discharge of most of them; on the other hand, the classification of smoking status we applied was rather arbitrary; accordingly, further studies are required to better clarify the effects of diabetes and smoking status on the troponin changes after EST in patients admitted with acute chest pain. Finally, at the time of admission some patients were taking anti-ischemic drugs that could not be withdrawn; thus, also whether some form of treatment may influence the response to EST of troponin(s) needs to be clarified in appropriate studies.


Smoking is a major risk factor for COPD and may impact the efficacy of COPD treatments; however, a large proportion of COPD patients continue to smoke following diagnosis.1) area under the curve from 0 to 12 h, trough FEV1, forced vital capacity, St George’s Respiratory Questionnaire (SGRQ) total score, COPD assessment test (CAT) score, transition dyspnea index (TDI) focal score, daily symptom scores, and rescue medication use were assessed in current smokers and ex-smokers. Incidences of adverse events (AEs) and serious AEs (SAEs) were also assessed.This post-hoc analysis of pooled data from the replicate 12-week, placebo-controlled GEM1 and GEM2 studies assessed the impact of smoking status on the efficacy and safety of glycopyrrolate 15.6 μg twice daily vs placebo in patients with moderate-to-severe COPD. Data from 867 patients enrolled in GEM1 and GEM2 were pooled for analysis and grouped by smoking status . Forced expiratory volume in 1 s  than those not receiving ICS. Changes from baseline in SGRQ total score and rescue medication use were significantly greater with glycopyrrolate compared with placebo, regardless of smoking status. Changes in the CAT score, TDI focal score, and daily symptom scores significantly improved versus placebo, but only in current smokers. Improvements in patient-reported outcomes (PROs) with glycopyrrolate relative to placebo were numerically greater in current smokers than ex-smokers. The incidences of AEs and SAEs were similar regardless of smoking status.Treatment with glycopyrrolate resulted in significant improvements in all lung function measures, independent of smoking status. In both current and ex-smokers, changes from baseline in trough FEVIn this post-hoc analysis of GEM1 and GEM2, glycopyrrolate use led to significant improvements in lung function, independent of baseline smoking status; improvements were less marked among patients receiving background ICS, regardless of baseline smoking status. Improvements in PROs were greater with glycopyrrolate than placebo, and the magnitude of changes was numerically greater among current smokers. The safety profile of glycopyrrolate was comparable between current smokers and ex-smokers.The online version of this article (10.1186/s12931-019-1112-0) contains supplementary material, which is available to authorized users. Tobacco smoking is well established as a major risk factor for the development of chronic obstructive pulmonary disease (COPD) –4. A SweGlycopyrrolate Effect on syMptoms and lung function 1 and 2 (GEM1 and GEM2), demonstrated the efficacy and safety of GLY vs placebo in patients with moderate-to-severe COPD and a smoking history of ≥10 pack-years  vs 1 [0.5%]) and MACE (3 [1.6%] vs 1 [0.5%]) were greater for GLY than for placebo. Among ex-smokers, MACE included three cases of non-fatal MI in the GLY arm, and one coronary revascularization in a patient taking placebo.Smoking status is known to impact the efficacy of ICS treatment in asthma , 9 and l1 AUC0-12h, trough FEV1, and trough FVC at 12 weeks was greater in ex-smokers than current smokers, but none of the differences between current and ex-smokers were significant. The effects of GLY were not impacted by baseline ICS use, while the impact on PROs was numerically but not significantly greater in current than ex-smokers. Conversely, in patients receiving tiotropium for 12 weeks there were numerical but not statistical improvements in FEV1 in current vs ex-smokers [1 criteria [The magnitude of the effect of GLY (relative to placebo) on FEV-smokers . Howevercriteria . In contcriteria . Importa1 with GLY compared with placebo, whereas ex-smokers had a significant improvement, regardless of background ICS use; however, this was, at least in part, due to differences in lung function improvement in the placebo arm between current and ex-smokers. Provided they were not receiving background ICS, both current and ex-smokers had significant improvements in trough FEV1.The GOLD 2019 report recommended the addition of ICS to long-acting bronchodilator therapies in patients with more severe disease experiencing dyspnea or exacerbations and having high eosinophil counts , 23–25, The most notable differences between current and ex-smokers were related to changes in PROs. While SGRQ total scores and rescue medication use were significantly improved with GLY compared with placebo in both current and ex-smokers, numerically greater improvements in placebo-adjusted SGRQ total score among current smokers compared to ex-smokers receiving GLY may be attributed, in part, to the greater improvements among ex-smokers receiving placebo. In contrast, placebo-adjusted changes from baseline in CAT and symptoms scores, as well as changes in TDI focal scores at Week 12, were significantly improved only in current smokers, but not in ex-smokers. While the differences in CAT scores between current and ex-smokers may be due to greater improvements among ex-smokers compared with smokers receiving placebo, similar differences between current and ex-smokers in placebo effects were not observed in TDI focal scores. Previous studies of tiotropium showed similar significant placebo-adjusted changes from baseline in SGRQ total score irrespective of smoking status, although numerically greater improvements were observed among current smokers compared with ex-smokers , 21.The differences observed in PROs between current and ex-smokers in this analysis may have been influenced by disease severity at baseline, with patients with more severe disease having possibly ceased smoking due to worse baseline lung function. This is supported by baseline characteristics showing that more ex-smokers than current smokers had longer disease duration and were classified as GOLD 3 and GOLD group D. Another potential explanation for the greater improvements in PROs in current smokers than ex-smokers is improvements in symptoms directly driven by active smoking, such as increased mucus secretion; such improvements in symptoms directly caused by active smoking may have led to greater positive outcomes among current smokers. In addition, the central actions of nicotine in the brain, which include release of neurotransmitters such as dopamine , may havThe safety profile of GLY was not substantially impacted by smoking status at baseline, although there were differences in the incidences of COPD worsening, cough, and upper respiratory tract infections between current and ex-smokers and treatment arms; these may be due to differences in baseline characteristics (e.g. disease severity) between subgroups. AEs of special interest associated with anticholinergic therapies (e.g. dry mouth and dizziness) were infrequent and were similar between treatments. Overall, the AE profile of GLY was not affected by smoking status at baseline. Although CCV AEs and MACE were most frequent in ex-smokers, the incidences were low and similar to those reported for other LAMAs , 28.The main limitation of this analysis is that post-hoc comparisons were not adjusted for multiplicity. Only a small number of patients stopped or started smoking during the GEM1 and GEM2 studies, which did not impact the observed outcomes. Additional prospective studies are needed to better understand the impact of smoking status on clinical outcomes with different bronchodilators available for the treatment of patients with COPD.1 compared with placebo; this is consistent with previous studies that demonstrated effects of smoking on ICS efficacy. GLY treatment resulted in clinically important improvements in CAT scores, TDI focal scores, and daily symptom scores in both current and ex-smokers, with significant improvements over placebo only among current smokers. These data support the use of GLY 15.6 μg BID in patients with moderate-to-severe COPD regardless of their baseline smoking status, although the magnitude of benefit may differ between current and ex-smokers.In this post-hoc analysis of pooled data from the GEM1 and GEM2 studies, GLY treatment led to significant improvements in lung function, SGRQ total score, and rescue medication use vs placebo in both smokers and ex-smokers. Current smokers receiving background ICS therapy on top of GLY had non-significant improvements in trough FEVAdditional file 1:Figure S1. Design of the GEM1 and GEM2 studies [*Screening period was flexible, ranging between 1 to 7 days. BID, twice daily (PDF 397 kb) studies , 15. *Sc


PGRP-LA, -LB, -LD, -LE, and -S1 during infection with two different fungal entomopathogenic strains. Furthermore, our data shows temporal differences in PGRP elicitation, with most PGRPs displaying significant upregulation at 60 h post-infection. Depletion of certain PGRPs via RNAi silencing resulted in a significant increase in fungal proliferation and a reduction in mosquito survival that was fungal strain-specific. Our data indicates that PGRPs play an important role in the antifungal response and expands our understanding of the factors that determine host susceptibility to fungal entomopathogens.Fungal entomopathogens are potential tools for the control of mosquito vectors that transmit infectious agents that cause disease in humans and animals. During the infection process, effective recognition of the invading fungi by the mosquito, is a crucial step in mounting an appropriate anti-fungal response. In this study, we investigated the role of peptidoglycan recognition receptors (PGRPs) in host resistance to fungal entomopathogens at the early stages of infection. Our study identified the induction of  Aedes aegypti, is one of the most important arboviral vectors, responsible for transmitting dengue, Zika and yellow fever virus , recent research has found that these recognition proteins play important roles against other invading microbes such as protozoan parasites Meister, . PGRPs cDrosophila and mosquitoes have shown that PGRP-LC plays a critical role in bacterial clearance and anti-Plasmodium defense  , are thought to function as negative regulators of immune responses given that its knock-down increased the production of several AMPs  colony was reared at 27°C and 80% humidity with a 12 h:12 h light/dark cycle. The colony was maintained by feeding adult females via an artificial feeding system using citrated bovine blood (HemoStat Laboratories Inc.). Adults were provided with a 10% sucrose solution and larvae were reared on a mixture of rabbit food and tropical fish food. Mosquitoes used in all experimental assays were conducted using 3- to 5-day old adult females.Beauveria bassiana (MBC 076) and Isaria javanica (ARSEF 5874) were grown on ¼ strength Sabouraud dextrose agar and yeast extract (SDAY) medium at 26°C for 15-days. Spore oil formulations were prepared as previously reported . The LT50 and LT95 values were calculated by probit analysis using SAS 9.4 statistical package.Fungal infection assays were conducted as previously described , six GNBPs (GNPS-1 through GNBP-6) and two molecules known to be elicited in response to fungal infection (CLSP2 and TEP22) was conducted in a 10 μl reaction using one microliter of the generated cDNA. The PowerUp SYBR green Master mix qPCR kit (Qiagen) was used in all reactions with gene specific primers .For gene expression analyses, pools of five challenged mosquitoes were collected at 48, 60, and 72 h post-infection (PI). These time points were selected to assess the progression of early immune elicitation when infected by these two different entomopathogenic fungal species. RNA was extracted from samples using TRIzol (Invitrogen) according to the manufacturer's instructions. Concentration and quality of the extracted RNA were evaluated via NanoDrop (Thermo Scientific). Synthesis of cDNA was conducted on normalized amounts of RNA using the QuantiTec reverse transcription kit with DNA Wipeout (Qiagen). Quantitative real-time PCR to amplify seven PGRPs ( primers . RT-qPCR primers . Rps17 hAe. aegypti. The HiScribe T7 Quick High Yield RNA synthesis kit (New England Biolabs) was used to generate dsRNA products from each target gene and each product was then diluted with molecular-grade water to a 3 μg/μl solution. Cold anesthetized 2- to 3-day old Ae. aegypti females were injected with 69 nl of a 3 μg/μl dsRNA solution into the thorax using a nano-injector . For infection assays evaluating the effect of silencing on mosquito survival, mosquitoes were challenged with entomopathogenic fungi at 1-day post-dsRNA injection. Silencing efficiency relative to dsFluc controls was evaluated for each silenced gene in mosquito whole-bodies at 3–4-days post-injection. We conducted a triple silencing of the three most highly expressed PGRPs , to overcome a potential redundancy in PGRP-derived protection. For the triple-knockdown, dsRNA products for each target gene were combined to a final concentration of 3 μg/μl each and with a final concentration of 9 μg/μl dsRNA injected per mosquito. To account for the total dsRNA product injected into dsPGRP-treated mosquitoes, control mosquitoes were also injected with 69 nl of a 9 μg/μl of dsFluc solution. Primers designed for gene silencing are reported in The transient depletion of PGRP genes was conducted via RNA interference (RNAi)-directed silencing as previously described . The error bars indicate the standard error of the mean and the type of test used is described in the respective figure legend.Graphs and statistical analysis for survival curves and gene expression were conducted using the software Prism 8 (GraphPad). Significance of survival curves was assessed using the Kaplan-Meier estimator with Log-rank test, while gene expression values were compared via ANOVA with Dunnett's post-test. LTAe. aegypti PGRPs can respond to entomopathogenic fungal infection, mosquitoes were challenged with either soybean oil, B. bassiana or I. javanica. Samples were collected at 48, 60, and 72 h post-infection (PI) and the expression of seven PGRPs    were evajavanica . In contPGRP-LC indicated absence of elicitation at 48 and 60 h PI but displayed significant increase in expression at 72 h PI only in mosquitoes infected with I. javanica  during the early time of infection. Our data shows that PGRP-S1 was the only PGRP that displayed consistently higher levels of gene expression throughout the time points of infection  . Silencing of the remaining PGRPs displayed similar phenotypes to that of dsPGRP-LA, with no significant effect on the survival of B. bassiana-infected mosquitoes in which PGRP-LD      . Interes = 0.39) , PGRP-LE = 0.45) , PGRP-S1 = 0.49) , or PGRP = 0.86) , had beeI. javanica-challenged mosquitoes showed a significant reduction in survival compared to dsFluc controls, with the depletion of either PGRP-LA        , PGRP-LD= 0.035) , or PGRP= 0.042) . No sign= 0.197) , PGRP-LE= 0.087) , or PGRP= 0.114) , Table 1PGRP-LA, PGRP-LD and PGRP-S1) and assessed its effect on mosquito survival. The silencing efficiency observed in tripled-silenced mosquitoes was the same as the single-silenced targets . Triple silencing prior to fungal infection led to a slim, albeit significant decrease in mosquito survival compared to the control groups in mosquitos infected with B. bassiana    , Table 1= 0.036) , Table 1B. bassiana-infected mosquitoes  are a family of proteins whose diverse functions, covering these three areas, are used to maintain the homeostasis between resistance and tolerance to microorganisms  and a later induction of GNBP-2 (at 60 h PI). These two GNBPs have been associated with the response to fungal infection , another family of proteins associated with fungal recognition. Here, our results indicate an earlier triggering of PGRP-LA, PGRP-LD, and PGRP-S1, had a robust and significant increase in gene expression in most time points evaluated. This was observed independent of the strain of fungal entomopathogen infecting the mosquito. In general, most of these PGRPs have been associated with the Imd pathway, and its triggering is thought to occur via the detection of peptidoglycan (PGN) during bacterial infections  at the same time of infection, providing further support that the activation of mosquito PGRPs is triggered by fungal-derived molecules.The PGRP ability to detect multiple pathogen-derived molecules might be inherent to their function and structural composition. Indeed, structural analysis of PGRP domains in Meister, . For insI. javanica but with no effect in those infected with B. bassiana. This detrimental effect of PGRP depletion was observed in three out of six PGRPs tested. This included PGRP-LA and PGRP-LD, which displayed strong gene elicitation during the infection process.We next corroborated the role of PGRPs in the antifungal defense by depleting PGRP expression via RNAi prior to fungal infection. Our results suggest that the strength of the PGRP anti-fungal response varies according to the invading fungal strain, with PGRP-silencing significantly affecting survival in mosquitoes infected with PGRP-SC2 depletion despite the absence of a significant induction in gene expression. This phenotype could be due to its predicted function as a PGRP with amidase activity, acting as a negative regulator of the immune response. Thus, while constitutively expressed, at basal levels it might be involved in catalyzing fungal-derived molecules that could potentially lead to a deleterious overstimulation of the immune response. In fact, a previous study in Drosophila, demonstrated that removal of PGRPs with amidase activity led to the uncontrolled activation of the Imd pathway and reduced fly survival due to an excessive immune response  hint to this possibility, with a slight but significant reduction in survival in mosquitoes infected with B. bassiana. Furthermore, PGRP expression analysis in single PGRP knockdowns indicated the significant elicitation of PGRP-LD in mosquitoes in which PGRP-LA or PGRP-LC were silenced. This suggests partial redundancy in these PGRPs and agrees with what is known with respect to PGRP-LA, PGRP-LD, and PGRP-LC, as interacting partners of the Imd pathway.The absence of any detrimental effect of PGRP silencing on survival of PGRP-LC-depleted mosquitoes challenged with B. bassiana but absent in I. javanica infection is puzzling. It could indicate that elicitation of PGRP-LC, and thus Imd pathway activation, is somewhat counterproductive to mosquito survival under the context of a fungal infection. However, our previous study knocking down the Imd pathway transcription factor Rel2 depicted a reduction in survival, indicating a protective effect of Imd pathway activation (Ramirez et al., B. bassiana at the level of PGRP-LC rather than a general mosquito antifungal response. The degree and specificity of this potential interaction between B. bassiana and PGRP-LC remains to be elucidated.The slight but significant increase in survival of I. javanica than those infected with B. bassiana. This is in agreement with our survival phenotypes and reiterates that while fungal detection by PGRP, and its subsequent downstream signaling through the immune pathways, has a stronger protective effect on mosquitoes infected with I. javanica, it has a very limited effect on infection with B. bassiana. This likely suggests a stronger ability of B. bassiana to circumvent the mosquito antifungal immune response. Whether this is due to active immune-suppressive activity via secondary metabolites or due to the inherent resistance of B. bassiana to the immune-derived effectors produced via PGRP elicitation, remains to be elucidated. Furthermore, our study only evaluated two fungal entomopathogenic species and it is quite possible that different fungal strains of B. bassiana will present different infection phenotypes. Thus, further work evaluating the role of PGRPs in infections with different strains is needed to confirm whether this phenotypic response is solely a characteristic of B. bassiana.Further assays evaluating the fungal load in PGRP-depleted mosquitoes confirmed the participation of PGRPs in the antifungal host response, whose dynamics are in turn dependent on the strain of invading fungal entomopathogen. For instance, our study showed a much greater and significant increase in fungal load in mosquitoes infected with In summary, our study reveals that in the complex interaction that exists between the mosquito immune system and the invading entomopathogenic fungi, PGRPs play a critical role in antifungal defense. Our data further indicates that the effectiveness of PGPR-based fungal detection/response varies according to the infecting fungal species and displays a temporal elicitation of PGRP genes during the early stages of infection. Information derived from our understanding of how mosquitoes detect and respond to microbes, and the mechanisms underlying host resistance to fungal entomopathogens, can improve mosquito control strategies using fungal entomopathogens by facilitating the selection of strains with different modes of action.All datasets generated for this study are included in the article/JR conceived and conducted the study, analyzed the data, and wrote the manuscript. EM assisted on the determination, analysis of time-dependent mortality data, and manuscript writing. LF-W and KV assisted with electron microscopy imaging and manuscript writing. AR assisted with data analysis and interpretation. All authors edited and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


These mutations may disrupt the gene regulation by affecting protein-DNA interaction. A study of these disruptions is important in understanding tumorigenesis. However, current computational tools process DNA sequence variants individually, when predicting the effect on protein-DNA binding. Thus, it is a daunting task to identify functional regulatory disturbances among thousands of mutations in a patient. Previously, we have reported and validated a pipeline for identifying functional non-coding somatic mutations in cancer patient cohorts, by integrating diverse information such as gene expression, spatial distribution of the mutations, and a biophysical model for estimating protein binding affinity. Here, we present a new user-friendly Python package BayesPI-BAR2 based on the proposed pipeline for integrative whole-genome sequence analysis. This may be the first prediction package that considers information from both multiple mutations and multiple patients. It is evaluated in follicular lymphoma and skin cancer patients, by focusing on sequence variants in gene promoter regions. BayesPI-BAR2 is a useful tool for predicting functional non-coding mutations in whole genome sequencing data: it allows identification of novel transcription factors (TFs) whose binding is altered by non-coding mutations in cancer. BayesPI-BAR2 program can analyze multiple datasets of genome-wide mutations at once and generate concise, easily interpretable reports for potentially affected gene regulatory sites. The package is freely available at  Somatic mutations are the primary cause of cancer. Although most studies of cancer genomes to date have focused on mutations occurring within exons, recent efforts have made whole genome sequences of paired tumor and normal samples widely available, facilitating the analysis of non-coding variants in cancer. In many cases, such variants have been shown to affect gene expression and to promote tumorigenesis . One mecThe BayesPI-BAR2 package presented here aims to solve these problems. It ranks TFs affected by SNV through a new BayesPI-BAR algorithm , augmentThe operation of the BayesPI-BAR2 pipeline is illustrated in BayesPI-BAR2 Python package first finds DNA regions with high mutation density and close to differentially expressed genes, then predicts TF affinity changes in these regions using the new BayesPI-BAR, and finally tests the significance of these predicted changes against a background model. All analysis is carried out by a set of command line tools written in Python 2. The package also includes binary files of the new BayesPI program  which careads per kilobase of exon model per million mapped reads (RPKM) values of RNA-seq data of patients are compared to that of the normal samples . Since RPKM-based differential expression tests may be affected by experimental biases  regions are identified by a method described in l biases  and resuSubsequently, MuSSD (Mutation filtering based on the Space and Sample Distribution) algorithm  is appliThe basic biophysical model for computing TF-DNA binding affinity, named BayesPI, was first reported in Si,a = 1 if the DNA sequence has nucleotide a  at position i and Si,a = 1 otherwise, N is the sequence length, M is the length of the binding motif, μ is the chemical potential of the TF or its concentration in the nucleus. The selection of μ  is based on a previous study  is the TF binding energy to a short DNA fragment with length M bp. This model assumes that nucleotides at each binding position contribute to the binding energy independently. The matrix w ∈ R(M×4), called position-specific affinity matrix (PSAM), where wj,a is the binding energy of nucleotide a at position j of the DNA fragment. In BayesPI-BAR2 Python package, a collection of PSAMs derived from a previous published work , for each sequence variant and TF:Bayesian modeling of Protein-DNA Interaction and Binding Affinity Ranking  method iSref, Salt represent the reference and alternate sequences, respectively. δdbA is the measure of the affinity change used by BayesPI-BAR. More details about the BayesPI-BAR approach are available in the supplementary and  both the size and the mutation counts of the background mutation blocks are kept same as that of patient ones. (b) DNA sequence is selected randomly from the same regions as the patient mutation block. (c) distributions of the mutation positions and the nucleotide changes are based on specific mutation signature such as tumor-derived mutations. To evaluate the relationship between the number of background blocks and the precision of background δdbA model, a few simulations are displayed in (1)Background mutation blocks are extracted randomly from regions of interest, with the same sequence length as patient block. Reference sequence of a background mutation block is taken from the reference genome. The alternate sequence is generated by random alteration of nucleotides in reference sequence, using either the tumor-derived mutations or the given k-mer mutation probability distribution (the mutation signature).(2)For each given TF, BayesPI-BAR computes δdbA of a patient regulatory mutation block. Then, it computes δdbA values for about 2000 background blocks that represent the background distribution of δdbA scores.(3)P-values is applied.Wilcoxon rank-sum test is used to compare the distribution of δdbA values between the patients’ and the background mutation blocks. Bonferroni correction of P-value correction procedure may not be suitable here. For example, Benjamini–Hochberg (BH) false discovery rate requires the P-values to be independent (or have limited dependencies)  , but there large .Computation of scores is the most time-consuming task that is needed for both the patient and the background mutation blocks. The old R program  was desij,aw and e-μ in the right side of the formula are precomputed and stored in order to avoid computing the exponent term in every sliding window. The new implementation reduces the computational time by about 90%. In addition, in BayesPI-BAR2 Python package, all calculations are parallelized across either multiple local CPUs or multiple nodes on a cluster using the SLURM workload manager. For instance, it takes about 5 h to process all mutation blocks in the skin cancer dataset , by using 8 nodes of 8 CPUs in each. The overall waiting time can be further reduced if more parallel processes are used or few mutation blocks are selected for testing. User guide and package architecture of BayesPI-BAR2 are available in the Where the terms eThe precision of the new BayesPI-BAR Python program, which is the basis of BayesPI-BAR2 package, was first assessed by a benchmark dataset of 67 SNVs with experimentally verified effects of TF binding. The results match the previous study .BCL6, BCL2, and HIST1H2BM genes are detected automatically, where containing 34, 40, and 2 SNVs, respectively. The results match with the earlier report (BCL6 and BCL2) (FOXD2 and FOXD3) on BCL6 and BCL2 were confirmed previously by knockdown experiments in SUDHL4 lymphoma cell  as were investigated before . Putativr report . Also, tnd BCL2)  were recoma cell . The newoma cell  and is rThe somatic mutations and RNA-Seq counts for the skin cancer evaluation were downloaded from the public DCC data release 23 at the International Cancer Genome Consortium (ICGC) data portal, from the MELA-AU, SKCA-BR, and SKCM-US projects. The dataset contains 23 million mutations called from whole genome sequence analysis of 263 patients. Melanoma or skin cancer has the highest prevalence of somatic mutations across human cancer types, which is more than ten times higher than that in Lymphoma cancer . There aTERT gene has the most patients affected, 58 in number, closely followed by blocks near several housekeeping genes . This is in agreement with the previous studies . A list of the 15 most highly mutated blocks is shown in a  studies . It has  studies . In the melanoma . When te ranking , 2.Additionally, BayesPI-BAR2 discovers novel regulatory mutations which affect gene expression in skin cancer. For instance, binding of TFs from Sp/KLF family and ETS family were found to be disrupted , but there are 15 mutation blocks from 263 skin cancer samples that pass a more stringent criteria . Therefore, a large sample size is preferred when using BayesPI-BAR2 to predict putative functional non-coding mutations.The new BayesPI-BAR2 Python package has been evaluated in both small  and large  cancer patient cohorts, based on whole genome sequencing experiments. It achieves good prediction accuracy and automatically reproduces the published results. The new package can be used to investigate previously unknown regulatory effects, even if the sample size is small and the recurrent mutation frequency is low. Nevertheless, the robustness of significance test in BayesPI-BAR2 is dependent on the sample size , a smallTERT promoter mutations: C > T at chr5:1,295,228 and chr5:1,295,250. Both of these mutations create ETS binging sites. Though six of fifty-eight patients did not have these two mutations, some ETS factors are positively affected in five of them  . AlternaThe new BayesPI-BAR2 Python package allows analysis of non-coding mutations in cancer patient cohorts, discovering mutation hotspots, and predicting effects of these mutations on TF-DNA binding. Unlike previously available tools, it considers the frequency of mutations, their recurrence across patients, and integrates this information with the predicted affinity changes employing a simple and statistically sound approach. Although in principle, it is applicable to any mutation dataset, BayesPI-BAR2 is designed for the typical cancer use case, with the goal to find few non-random effects among many somatic mutations. The package can be a useful tool for in-depth analysis of non-coding mutations detected in whole genome sequencing experiments, as well as for predicting their effects on genome regulation in cancer. All in all, it provides a reasonable number of predictions for further experimental validation.http://folk.uio.no/junbaiw/BayesPI-BAR2/; Project name: BayesPI-BAR2 Package; Operating system(s): Linux and OS X; Programming language: Python; License: General Public License (GNU GPLv3); Any restrictions to use by non-academics: None; The datasets analyzed during the current study are available in the public DCC data release 23 at the ICGC data portal: https://dcc.icgc.org/releases/release_23/Projects.The package source code, binaries for Linux and OS X, and demo datasets are available at KB implemented the BayesPI-BAR2 pipeline in Python. JD validated study. JW conceived project, designed BayesPI-BAR2 pipeline, and contributed in developing package. KB and JW drafted manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.