Bayesian random-effects meta-analysis with empirical heterogeneity priors for application in health technology assessment with very few studies.

In Bayesian random-effects meta-analysis, the use of weakly informative prior distributions is of particular benefit in cases where only a few studies are included, a situation often encountered in health technology assessment (HTA). Suggestions for empirical prior distributions are available in the literature but it is unknown whether these are adequate in the context of HTA. Therefore, a database of all relevant meta-analyses conducted by the Institute for Quality and Efficiency in Health Care (IQWiG, Germany) was constructed to derive empirical prior distributions for the heterogeneity parameter suitable for HTA. Previously, an extension to the normal-normal hierarchical model had been suggested for this purpose. For different effect measures, this extended model was applied on the database to conservatively derive a prior distribution for the heterogeneity parameter. Comparison of a Bayesian approach using the derived priors with IQWiG's current standard approach for evidence synthesis shows favorable properties. Therefore, these prior distributions are recommended for future meta-analyses in HTA settings and could be embedded into the IQWiG evidence synthesis approach in the case of very few studies.

How trace plots help interpret meta-analysis results.

The trace plot is seldom used in meta-analysis, yet it is a very informative plot. In this article, we define and illustrate what the trace plot is, and discuss why it is important. The Bayesian version of the plot combines the posterior density of τ , the between-study standard deviation, and the shrunken estimates of the study effects as a function of τ . With a small or moderate number of studies, τ is not estimated with much precision, and parameter estimates and shrunken study effect estimates can vary widely depending on the correct value of τ . The trace plot allows visualization of the sensitivity to τ along with a plot that shows which values of τ are plausible and which are implausible. A comparable frequentist or empirical Bayes version provides similar results. The concepts are illustrated using examples in meta-analysis and meta-regression; implementation in R is facilitated in a Bayesian or frequentist framework using the bayesmeta and metafor packages, respectively.

Summarizing empirical information on between-study heterogeneity for Bayesian random-effects meta-analysis.

In Bayesian meta-analysis, the specification of prior probabilities for the between-study heterogeneity is commonly required, and is of particular benefit in situations where only few studies are included. Among the considerations in the set-up of such prior distributions, the consultation of available empirical data on a set of relevant past analyses sometimes plays a role. How exactly to summarize historical data sensibly is not immediately obvious; in particular, the investigation of an empirical collection of heterogeneity estimates will not target the actual problem and will usually only be of limited use. The commonly used normal-normal hierarchical model for random-effects meta-analysis is extended to infer a heterogeneity prior. Using an example data set, we demonstrate how to fit a distribution to empirically observed heterogeneity data from a set of meta-analyses. Considerations also include the choice of a parametric distribution family. Here, we focus on simple and readily applicable approaches to then translate these into (prior) probability distributions.

Using the bayesmeta R package for Bayesian random-effects meta-regression.

BACKGROUND: Random-effects meta-analysis within a hierarchical normal modeling framework is commonly implemented in a wide range of evidence synthesis applications. More general problems may even be tackled when considering meta-regression approaches that in addition allow for the inclusion of study-level covariables. METHODS: We describe the Bayesian meta-regression implementation provided in the bayesmetaR package including the choice of priors, and we illustrate its practical use. RESULTS: A wide range of example applications are given, such as binary and continuous covariables, subgroup analysis, indirect comparisons, and model selection. Example R code is provided. CONCLUSIONS: The bayesmeta package provides a flexible implementation. Due to the avoidance of MCMC methods, computations are fast and reproducible, facilitating quick sensitivity checks or large-scale simulation studies.

Reducing antibiotic use in uncomplicated urinary tract infections in adult women: a systematic review and individual participant data meta-analysis.

BACKGROUND: Randomised controlled trials (RCTs) investigated analgesics, herbal formulations, delayed prescription of antibiotics, and placebo to prevent overprescription of antibiotics in women with uncomplicated urinary tract infections (uUTI). OBJECTIVES: To estimate the effect of these strategies and to identify symptoms, signs, or other factors that indicate a benefit from these strategies. DATA SOURCES: MEDLINE, EMBASE, Web of Science, LILACS, Cochrane Database of Systematic Reviews and of Controlled Trials, and ClinicalTrials. STUDY ELIGIBILITY CRITERIA, PARTICIPANTS AND INTERVENTIONS: RCTs investigating any strategies to reduce antibiotics vs. immediate antibiotics in adult women with uUTI in primary care. METHODS: We extracted individual participant data (IPD) if available, otherwise aggregate data (AD). Bayesian random-effects meta-analysis of the AD was used for pairwise comparisons. Candidate moderators and prognostic indicators of treatment effects were investigated using generalised linear mixed models based on IPD. RESULTS: We analysed IPD of 3524 patients from eight RCTs and AD of 78 patients. Non-antibiotic strategies increased the rates of incomplete recovery (OR 3.0; 95% credible interval (CrI), 1.7-5.5; Bayesian p-value (pB) = 0.0017; τ = 0.6), subsequent antibiotic treatment (OR 3.5; 95% CrI, 2.1-5.8; pB = 0.0003) and pyelonephritis (OR 5.6; 95% CrI, 2.3-13.9; pB = 0.0003). Conversely, they decreased overall antibiotic use by 63%. Patients positive for urinary erythrocytes and urine culture were at increased risk for incomplete recovery (OR 4.7; 95% CrI, 2.1-10.8; pB = 0.0010), but no difference was apparent where both were negative (OR 0.8; 95% CrI, 0.3-2.0; pB = 0.667). In patients treated using non-antibiotic strategies, urinary erythrocytes and positive urine culture were independent prognostic indicators for subsequent antibiotic treatment and pyelonephritis. CONCLUSIONS: Compared to immediate antibiotics, non-antibiotic strategies reduce overall antibiotic use but result in poorer clinical outcomes. The presence of erythrocytes and tests to confirm bacteria in urine could be used to target antibiotic prescribing.

Double arcsine transform not appropriate for meta-analysis.

The variance-stabilizing Freeman-Tukey double arcsine transform was originally proposed for inference on single proportions. Subsequently, its use has been suggested in the context of meta-analysis of proportions. While some erratic behavior has been observed previously, here we point out and illustrate general issues of monotonicity and invertibility that make this transform unsuitable for meta-analysis purposes.

Immunotherapy for people with clinically isolated syndrome or relapsing-remitting multiple sclerosis: treatment response by demographic, clinical, and biomarker subgroups (PROMISE)-a systematic review protocol.

BACKGROUND: Multiple sclerosis (MS) is an inflammatory and degenerative disease of the central nervous system with an increasing worldwide prevalence. Since 1993, more than 15 disease-modifying immunotherapies (DMTs) have been licenced and have shown moderate efficacy in clinical trials. Based on the heterogeneity of the disease and the partial effectiveness of therapies, a personalised medicine approach would be valuable taking individual prognosis and suitability of a chosen therapy into account to gain the best possible treatment effect. The primary objective of this review is to assess the differential treatment effects of all approved DMTs in subgroups of adults with clinically isolated syndrome or relapsing forms of MS. We will analyse possible treatment effect modifiers (TEM) defined by baseline demographic characteristics (gender, age), and diagnostic (i.e. MRI measures) and clinical (i.e. relapses, disability level) measures of MS disease activity. METHODS: We will include all published and accessible unpublished primary and secondary analyses of randomised controlled trials (RCTs) with a follow-up of at least 12 months investigating the efficacy of at least one approved DMT, with placebo or other approved DMTs as control intervention(s) in subgroups of trial participants. As the primary outcome, we will address disability as defined by the Expanded Disability Status Scale or multiple sclerosis functional composite scores followed by relapse frequency, quality of life measures, and side effects. MRI data will be analysed as secondary outcomes. MEDLINE, EMBASE, CINAHL, LILACS, CENTRAL and major trial registers will be searched for suitable studies. Titles and abstracts and full texts will be screened by two persons independently using Covidence. The risk of bias will be analysed based on the Cochrane "Risk of Bias 2" tool, and the certainty of evidence will be assessed using GRADE. Treatment effects will be reported as rate ratio or odds ratio. Primary analyses will follow the intention-to-treat principle. Meta-analyses will be carried out using random-effects models. DISCUSSION: Given that individual patient data from clinical studies are often not available, the review will allow to analyse the evidence on TEM in MS immunotherapy and thus support clinical decision making in individual cases. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42021279665 .

A straightforward meta-analysis approach for oncology phase I dose-finding studies.

Phase I early-phase clinical studies aim at investigating the safety and the underlying dose-toxicity relationship of a drug or combination. While little may still be known about the compound's properties, it is crucial to consider quantitative information available from any studies that may have been conducted previously on the same drug. A meta-analytic approach has the advantages of being able to properly account for between-study heterogeneity, and it may be readily extended to prediction or shrinkage applications. Here we propose a simple and robust two-stage approach for the estimation of maximum tolerated dose(s) utilizing penalized logistic regression and Bayesian random-effects meta-analysis methodology. Implementation is facilitated using standard R packages. The properties of the proposed methods are investigated in Monte Carlo simulations. The investigations are motivated and illustrated by two examples from oncology.

COPD mortality and exacerbations in the placebo group of clinical trials over two decades: a systematic review and meta-regression.

A decreasing trend in exacerbation rates has been observed in COPD. Because mortality is linked to exacerbations, it is of interest to investigate whether a similar time trend is also present in mortality rates. We performed a systematic review of placebo groups in published randomised controlled trials. Mortality rate was modelled based on a Poisson distribution for the event counts. Adding information on mortality as well as on newly published studies on a previous database, we performed a meta-regression. Among the 56 included studies representing 14 166 patients, an annual decrease in mortality rates of 6.1% (-0.6%, 12.6%) (p=0.073) was observed. Consistent results were obtained in subgroups as well as when adjusting for potential confounders. The correlation between exacerbation rate and mortality rate was positive but weak as well as insignificant. In summary, analysis of randomised controlled trials in COPD patients showed a decrease in mortality in the placebo arms over the last two decades. This effect is comparable to the previously observed decrease in annual exacerbation rate. Albeit insignificant, our results suggest that care is needed in the design of new trials or when comparing results from trials published many years apart.

Bounds for the weight of external data in shrinkage estimation.

Shrinkage estimation in a meta-analysis framework may be used to facilitate dynamical borrowing of information. This framework might be used to analyze a new study in the light of previous data, which might differ in their design (e.g., a randomized controlled trial and a clinical registry). We show how the common study weights arise in effect and shrinkage estimation, and how these may be generalized to the case of Bayesian meta-analysis. Next we develop simple ways to compute bounds on the weights, so that the contribution of the external evidence may be assessed a priori. These considerations are illustrated and discussed using numerical examples, including applications in the treatment of Creutzfeldt-Jakob disease and in fetal monitoring to prevent the occurrence of metabolic acidosis. The target study's contribution to the resulting estimate is shown to be bounded below. Therefore, concerns of evidence being easily overwhelmed by external data are largely unwarranted.

Cytoplasmic synthesis of endogenous Alu complementary DNA via reverse transcription and implications in age-related macular degeneration.

Alu retroelements propagate via retrotransposition by hijacking long interspersed nuclear element-1 (L1) reverse transcriptase (RT) and endonuclease activities. Reverse transcription of Alu RNA into complementary DNA (cDNA) is presumed to occur exclusively in the nucleus at the genomic integration site. Whether Alu cDNA is synthesized independently of genomic integration is unknown. Alu RNA promotes retinal pigmented epithelium (RPE) death in geographic atrophy, an untreatable type of age-related macular degeneration. We report that Alu RNA-induced RPE degeneration is mediated via cytoplasmic L1-reverse-transcribed Alu cDNA independently of retrotransposition. Alu RNA did not induce cDNA production or RPE degeneration in L1-inhibited animals or human cells. Alu reverse transcription can be initiated in the cytoplasm via self-priming of Alu RNA. In four health insurance databases, use of nucleoside RT inhibitors was associated with reduced risk of developing atrophic macular degeneration (pooled adjusted hazard ratio, 0.616; 95% confidence interval, 0.493-0.770), thus identifying inhibitors of this Alu replication cycle shunt as potential therapies for a major cause of blindness.

On weakly informative prior distributions for the heterogeneity parameter in Bayesian random-effects meta-analysis.

The normal-normal hierarchical model (NNHM) constitutes a simple and widely used framework for meta-analysis. In the common case of only few studies contributing to the meta-analysis, standard approaches to inference tend to perform poorly, and Bayesian meta-analysis has been suggested as a potential solution. The Bayesian approach, however, requires the sensible specification of prior distributions. While noninformative priors are commonly used for the overall mean effect, the use of weakly informative priors has been suggested for the heterogeneity parameter, in particular in the setting of (very) few studies. To date, however, a consensus on how to generally specify a weakly informative heterogeneity prior is lacking. Here we investigate the problem more closely and provide some guidance on prior specification.

Strategies to reduce antibiotic use in women with uncomplicated urinary tract infection in primary care: protocol of a systematic review and meta-analysis including individual patient data.

INTRODUCTION: Uncomplicated urinary tract infection (UTI) in women is a common reason to present in general practice and is usually treated with antibiotics to reduce symptom severity and duration. Results of recent clinical trials indicate that non-antibiotic treatment approaches can also be effective. However, it remains unclear which patients would benefit from antibiotic treatment and which can effectively and safely be treated without antibiotics. This systematic review and meta-analysis aims to estimate the effect of treatment strategies to reduce antibiotic use in comparison with immediate antibiotic treatment and to identify prognostic factors and moderators of treatment effects. A further aim is to identify subgroups of patients benefiting from a specific therapy. METHODS AND ANALYSIS: A systematic literature search will be performed to identify randomised controlled trials which investigated the effect of treatment strategies to reduce antibiotic use in female adults with uncomplicated UTI compared with immediate antibiotic treatment. Therefore, the primary outcome of the meta-analysis is incomplete recovery. Anonymised individual patient data (IPD) will be collected. Aggregate data will be used for pairwise comparisons of treatment strategies using meta-analysis models with random effects accounting for potential between-study heterogeneity. Potential effect moderators will be explored in meta-regressions. For IPD, generalised linear mixed models will be used, which may be adjusted for baseline characteristics. Interactions of baseline variables with treatment effects will be explored. These models will be used to assess direct comparisons of treatment, but might be extended to networks. ETHICS AND DISSEMINATION: The local institutional review and ethics board judged the project a secondary analysis of existing anonymous data which meet the criteria for waiver of ethics review. Dissemination of the results will be via published scientific papers and presentations. Key messages will be promoted for example, via social media or press releases. PROSPERO REGISTRATION NUMBER: CRD42019125804.

Repurposing anti-inflammasome NRTIs for improving insulin sensitivity and reducing type 2 diabetes development.

Innate immune signaling through the NLRP3 inflammasome is activated by multiple diabetes-related stressors, but whether targeting the inflammasome is beneficial for diabetes is still unclear. Nucleoside reverse-transcriptase inhibitors (NRTI), drugs approved to treat HIV-1 and hepatitis B infections, also block inflammasome activation. Here, we show, by analyzing five health insurance databases, that the adjusted risk of incident diabetes is 33% lower in patients with NRTI exposure among 128,861 patients with HIV-1 or hepatitis B (adjusted hazard ratio for NRTI exposure, 0.673; 95% confidence interval, 0.638 to 0.710; P < 0.0001; 95% prediction interval, 0.618 to 0.734). Meanwhile, an NRTI, lamivudine, improves insulin sensitivity and reduces inflammasome activation in diabetic and insulin resistance-induced human cells, as well as in mice fed with high-fat chow; mechanistically, inflammasome-activating short interspersed nuclear element (SINE) transcripts are elevated, whereas SINE-catabolizing DICER1 is reduced, in diabetic cells and mice. These data suggest the possibility of repurposing an approved class of drugs for prevention of diabetes.

Repeating noninvasive risk stratification improves prediction of outcome in ICD patients.

BACKGROUND: Noninvasive risk stratification aims to detect abnormalities in the pathophysiological mechanisms underlying ventricular arrhythmias. We studied the predictive value of repeating risk stratification in patients with an implantable cardioverter-defibrillator (ICD). METHODS: The EUTrigTreat clinical study was a prospective multicenter trial including ischemic and nonischemic cardiomyopathies and arrhythmogenic heart disease. Left ventricular ejection fraction ≤40% (LVEF), premature ventricular complexes >400/24 hr (PVC), non-negative microvolt T-wave alternans (MTWA), and abnormal heart rate turbulence (HRT) were considered high risk. Tests were repeated within 12 months after inclusion. Adjusted Cox regression analysis was performed for mortality and appropriate ICD shocks. RESULTS: In total, 635 patients had analyzable baseline data with a median follow-up of 4.4 years. Worsening of LVEF was associated with increased mortality (HR 3.59, 95% CI 1.17-11.04), as was consistent abnormal HRT (HR 8.34, 95%CI 1.06-65.54). HRT improvement was associated with improved survival when compared to consistent abnormal HRT (HR 0.10, 95%CI 0.01-0.82). For appropriate ICD shocks, a non-negative MTWA test or high PVC count at any moment was associated with increased arrhythmic risk independent of the evolution of test results (worsening: HR 3.76 (95%CI 1.43-9.88) and HR 2.50 (95%CI 1.15-5.46); improvement: HR 2.80 (95%CI 1.03-7.61) and HR 2.45 (95%CI 1.07-5.62); consistent: HR 2.47 (95%CI 0.95-6.45) and HR 2.40 (95%CI 1.33-4.33), respectively). LVEF improvement was associated with a lower arrhythmic risk (HR 0.34, 95%CI 0.12-0.94). CONCLUSIONS: Repeating LVEF and HRT improved the prediction of mortality, whereas stratification of ventricular arrhythmias may be improved by repeating LVEF measurements, MTWA and ECG Holter monitoring.

Neutrophil Gelatinase-Associated Lipocalin Measured on Clinical Laboratory Platforms for the Prediction of Acute Kidney Injury and the Associated Need for Dialysis Therapy: A Systematic Review and Meta-analysis.

RATIONALE & OBJECTIVE: The usefulness of measures of neutrophil gelatinase-associated lipocalin (NGAL) in urine or plasma obtained on clinical laboratory platforms for predicting acute kidney injury (AKI) and AKI requiring dialysis (AKI-D) has not been fully evaluated. We sought to quantitatively summarize published data to evaluate the value of urinary and plasma NGAL for kidney risk prediction. STUDY DESIGN: Literature-based meta-analysis and individual-study-data meta-analysis of diagnostic studies following PRISMA-IPD guidelines. SETTING & STUDY POPULATIONS: Studies of adults investigating AKI, severe AKI, and AKI-D in the setting of cardiac surgery, intensive care, or emergency department care using either urinary or plasma NGAL measured on clinical laboratory platforms. SELECTION CRITERIA FOR STUDIES: PubMed, Web of Science, Cochrane Library, Scopus, and congress abstracts ever published through February 2020 reporting diagnostic test studies of NGAL measured on clinical laboratory platforms to predict AKI. DATA EXTRACTION: Individual-study-data meta-analysis was accomplished by giving authors data specifications tailored to their studies and requesting standardized patient-level data analysis. ANALYTICAL APPROACH: Individual-study-data meta-analysis used a bivariate time-to-event model for interval-censored data from which discriminative ability (AUC) was characterized. NGAL cutoff concentrations at 95% sensitivity, 95% specificity, and optimal sensitivity and specificity were also estimated. Models incorporated as confounders the clinical setting and use versus nonuse of urine output as a criterion for AKI. A literature-based meta-analysis was also performed for all published studies including those for which the authors were unable to provide individual-study data analyses. RESULTS: We included 52 observational studies involving 13,040 patients. We analyzed 30 data sets for the individual-study-data meta-analysis. For AKI, severe AKI, and AKI-D, numbers of events were 837, 304, and 103 for analyses of urinary NGAL, respectively; these values were 705, 271, and 178 for analyses of plasma NGAL. Discriminative performance was similar in both meta-analyses. Individual-study-data meta-analysis AUCs for urinary NGAL were 0.75 (95% CI, 0.73-0.76) and 0.80 (95% CI, 0.79-0.81) for severe AKI and AKI-D, respectively; for plasma NGAL, the corresponding AUCs were 0.80 (95% CI, 0.79-0.81) and 0.86 (95% CI, 0.84-0.86). Cutoff concentrations at 95% specificity for urinary NGAL were>580ng/mL with 27% sensitivity for severe AKI and>589ng/mL with 24% sensitivity for AKI-D. Corresponding cutoffs for plasma NGAL were>364ng/mL with 44% sensitivity and>546ng/mL with 26% sensitivity, respectively. LIMITATIONS: Practice variability in initiation of dialysis. Imperfect harmonization of data across studies. CONCLUSIONS: Urinary and plasma NGAL concentrations may identify patients at high risk for AKI in clinical research and practice. The cutoff concentrations reported in this study require prospective evaluation.

Random-effects meta-analysis of few studies involving rare events.

Meta-analyses of clinical trials targeting rare events face particular challenges when the data lack adequate numbers of events for all treatment arms. Especially when the number of studies is low, standard random-effects meta-analysis methods can lead to serious distortions because of such data sparsity. To overcome this, we suggest the use of weakly informative priors (WIPs) for the treatment effect parameter of a Bayesian meta-analysis model, which may also be seen as a form of penalization. As a data model, we use a binomial-normal hierarchical model (BNHM) that does not require continuity corrections in case of zero counts in one or both arms. We suggest a normal prior for the log-odds ratio with mean 0 and standard deviation 2.82, which is motivated (a) as a symmetric prior centered around unity and constraining the odds ratio within a range from 1/250 to 250 with 95% probability and (b) as consistent with empirically observed effect estimates from a set of 37 773 meta-analyses from the Cochrane Database of Systematic Reviews. In a simulation study with rare events and few studies, our BNHM with a WIP outperformed a Bayesian method without a WIP and a maximum likelihood estimator in terms of smaller bias and shorter interval estimates with similar coverage. Furthermore, the methods are illustrated by a systematic review in immunosuppression of rare safety events following pediatric transplantation. A publicly available R package, MetaStan, is developed to automate a Bayesian implementation of meta-analysis models using WIPs.

Dynamically borrowing strength from another study through shrinkage estimation.

Meta-analytic methods may be used to combine evidence from different sources of information. Quite commonly, the normal-normal hierarchical model (NNHM) including a random-effect to account for between-study heterogeneity is utilized for such analyses. The same modeling framework may also be used to not only derive a combined estimate, but also to borrow strength for a particular study from another by deriving a shrinkage estimate. For instance, a small-scale randomized controlled trial could be supported by a non-randomized study, e.g. a clinical registry. This would be particularly attractive in the context of rare diseases. We demonstrate that a meta-analysis still makes sense in this extreme case, effectively based on a synthesis of only two studies, as illustrated using a recent trial and a clinical registry in Creutzfeld-Jakob disease. Derivation of a shrinkage estimate within a Bayesian random-effects meta-analysis may substantially improve a given estimate even based on only a single additional estimate while accounting for potential effect heterogeneity between the studies. Alternatively, inference may equivalently be motivated via a model specification that does not require a common overall mean parameter but considers the treatment effect in one study, and the difference in effects between the studies. The proposed approach is quite generally applicable to combine different types of evidence originating, e.g. from meta-analyses or individual studies. An application of this more general setup is provided in immunosuppression following liver transplantation in children.

Present criteria for prophylactic ICD implantation: Insights from the EU-CERT-ICD (Comparative Effectiveness Research to Assess the Use of Primary ProphylacTic Implantable Cardioverter Defibrillators in EUrope) project.

BACKGROUND: The clinical effectiveness of primary prevention implantable cardioverter defibrillator (ICD) therapy is under debate. It is urgently needed to better identify patients who benefit from prophylactic ICD therapy. The EUropean Comparative Effectiveness Research to Assess the Use of Primary ProphylacTic Implantable Cardioverter Defibrillators (EU-CERT-ICD) completed in 2019 will assess this issue. SUMMARY: The EU-CERT-ICD is a prospective investigator-initiated non-randomized, controlled, multicenter observational cohort study done in 44 centers across 15 European countries. A total of 2327 patients with heart failure due to ischemic heart disease or dilated cardiomyopathy indicated for primary prophylactic ICD implantation were recruited between 2014 and 2018 (>1500 patients at first ICD implantation, >750 patients non-randomized non-ICD control group). The primary endpoint was all-cause mortality, and first appropriate shock was co-primary endpoint. At baseline, all patients underwent 12­lead ECG and Holter-ECG analysis using multiple advanced methods for risk stratification as well as documentation of clinical characteristics and laboratory values. The EU-CERT-ICD data will provide much needed information on the survival benefit of preventive ICD therapy and expand on previous prospective risk stratification studies which showed very good applicability of clinical parameters and advanced risk stratifiers in order to define patient subgroups with above or below average ICD benefit. CONCLUSION: The EU-CERT-ICD study will provide new and current data about effectiveness of primary prophylactic ICD implantation. The study also aims for improved risk stratification and patient selection using clinical risk markers in general, and advanced ECG risk markers in particular.

Sarcopenia as Prognostic Factor in Lung Cancer Patients: A Systematic Review and Meta-analysis.

BACKGROUND/AIM: Sarcopenia describes the loss of skeletal muscle mass. While this condition is associated with a high mortality in cancer patients, its influence on survival is still underestimated. PATIENTS AND METHODS: A systematic review for articles was performed using the PubMed database, Cochrane Library, Biomed Central, Science Direct and by manual search. We used data of overall survival in sarcopenic patients for assessing the death risk. We extracted hazard ratio estimates from univariate and multivariate Cox proportional hazards models for meta-analysis. RESULTS: A total of 15 studies were eligible for meta-analysis including a total of 2,521 lung cancer patients. Univariate meta-analysis revealed a two-fold increased death risk in sarcopenic patients; multivariate meta-analysis yielded a significant, three-fold elevated risk of death. This higher mortality is independent of tumour stage. CONCLUSION: Muscle loss is an independent risk factor for increased death risk in lung cancer patients independent of cancer stage. This argues for implementing screening for sarcopenia into cancer care.