Test accuracy of rapid diagnostic tests and reverse-transcription polymerase chain reaction against virus isolation in cell culture for assessing SARS-CoV-2 infectivity: Systematic review and meta-analysis.

We aimed to assess the performance of Ag-RDT and RT-qPCR with regard to detecting infectious SARS-CoV-2 in cell cultures, as their diagnostic test accuracy (DTA) compared to virus isolation remains largely unknown. We searched three databases up to 15 December 2021 for DTA studies. The bivariate model was used to synthesise the estimates. Risk of bias was assessed using QUADAS-2/C. Twenty studies (2605 respiratory samples) using cell culture and at least one molecular test were identified. All studies were at high or unclear risk of bias in at least one domain. Three comparative DTA studies reported results on Ag-RDT and RT-qPCR against cell culture. Two studies evaluated RT-qPCR against cell culture only. Fifteen studies evaluated Ag-RDT against cell culture as reference standard in RT-qPCR-positive samples. For Ag-RDT, summary sensitivity was 93% (95% CI 78; 98%) and specificity 87% (95% CI 70; 95%). For RT-qPCR, summary sensitivity (continuity-corrected) was 98% (95% CI 95; 99%) and specificity 45% (95% CI 28; 63%). In studies relying on RT-qPCR-positive subsamples (n = 15), the summary sensitivity of Ag-RDT was 93% (95% CI 92; 93%) and specificity 63% (95% CI 63; 63%). Ag-RDT show moderately high sensitivity, detecting most but not all samples demonstrated to be infectious based on virus isolation. Although RT-qPCR exhibits high sensitivity across studies, its low specificity to indicate infectivity raises the question of its general superiority in all clinical settings. Study findings should be interpreted with caution due to the risk of bias, heterogeneity and the imperfect reference standard for infectivity.

Efficacy and safety of corticosteroids and ACTH in epileptic syndromes beyond Infantile Epileptic Spasms Syndrome (IESS): A systematic review and meta-analysis.

We conducted a systematic review investigating the efficacy and tolerability of adrenocorticotropic hormone (ACTH) and corticosteroids in children with epilepsies other than infantile epileptic spasm syndrome (IESS) that are resistant to anti-seizure medication (ASM). We included retrospective and prospective studies reporting on more than five patients and with clear case definitions and descriptions of treatment and outcome measures. We searched multiple databases and registries, and we assessed the risk of bias in the selected studies using a questionnaire based on published templates. Results were summarized with meta-analyses that pooled logit-transformed proportions or rates. Subgroup analyses and univariable and multivariable meta-regressions were performed to examine the influence of covariates. We included 38 studies (2 controlled and 5 uncontrolled prospective; 31 retrospective) involving 1152 patients. Meta-analysis of aggregate data for the primary outcomes of seizure response and reduction of electroencephalography (EEG) spikes at the end of treatment yielded pooled proportions (PPs) of 0.60 (95% confidence interval [CI] 0.52-0.67) and 0.56 (95% CI 0.43-0.68). The relapse rate was high (PP 0.33, 95% CI 0.27-0.40). Group analyses and meta-regression showed a small benefit of ACTH and no difference between all other corticosteroids, a slightly better effect in electric status epilepticus in slow sleep (ESES) and a weaker effect in patients with cognitive impairment and "symptomatic" etiology. Obesity and Cushing's syndrome were the most common adverse effects, occurring more frequently in trials addressing continuous ACTH (PP 0.73, 95% CI 0.48-0.89) or corticosteroids (PP 0.72, 95% CI 0.54-0.85) than intermittent intravenous or oral corticosteroid administration (PP 0.05, 95% CI 0.02-0.10). The validity of these results is limited by the high risk of bias in most included studies and large heterogeneity among study results. This report was registered under International Prospective Register of Systematic Reviews (PROSPERO) number CRD42022313846. We received no financial support.

Shortest path or random walks? A framework for path weights in network meta-analysis.

Quantifying the contributions, or weights, of comparisons or single studies to the estimates in a network meta-analysis (NMA) is an active area of research. We extend this work to include the contributions of paths of evidence. We present a general framework, based on the path-design matrix, that describes the problem of finding path contributions as a linear equation. The resulting solutions may have negative coefficients. We show that two known approaches, called shortestpath and randomwalk, are special solutions of this equation, and both meet an optimization criterion, as they minimize the sum of absolute path contributions. In general, there is an infinite set of solutions, which can be identified using the generalized inverse (Moore-Penrose pseudoinverse). We consider two further special approaches. For large networks we find that shortestpath is superior with respect to run time and variability, compared to the other approaches, and is thus recommended in practice. The path-weights framework also has the potential to answer more general research questions in NMA.

Meta-Analysis of Diagnostic Accuracy Studies With Multiple Thresholds: Comparison of Approaches in a Simulation Study.

The development of methods for the meta-analysis of diagnostic test accuracy (DTA) studies is still an active area of research. While methods for the standard case where each study reports a single pair of sensitivity and specificity are nearly routinely applied nowadays, methods to meta-analyze receiver operating characteristic (ROC) curves are not widely used. This situation is more complex, as each primary DTA study may report on several pairs of sensitivity and specificity, each corresponding to a different threshold. In a case study published earlier, we applied a number of methods for meta-analyzing DTA studies with multiple thresholds to a real-world data example (Zapf et al., Biometrical Journal. 2021; 63(4): 699-711). To date, no simulation study exists that systematically compares different approaches with respect to their performance in various scenarios when the truth is known. In this article, we aim to fill this gap and present the results of a simulation study that compares three frequentist approaches for the meta-analysis of ROC curves. We performed a systematic simulation study, motivated by an example from medical research. In the simulations, all three approaches worked partially well. The approach by Hoyer and colleagues was slightly superior in most scenarios and is recommended in practice.

Model selection for component network meta-analysis in connected and disconnected networks: a simulation study.

BACKGROUND: Network meta-analysis (NMA) allows estimating and ranking the effects of several interventions for a clinical condition. Component network meta-analysis (CNMA) is an extension of NMA which considers the individual components of multicomponent interventions. CNMA allows to "reconnect" a disconnected network with common components in subnetworks. An additive CNMA assumes that component effects are additive. This assumption can be relaxed by including interaction terms in the CNMA. METHODS: We evaluate a forward model selection strategy for component network meta-analysis to relax the additivity assumption that can be used in connected or disconnected networks. In addition, we describe a procedure to create disconnected networks in order to evaluate the properties of the model selection in connected and disconnected networks. We apply the methods to simulated data and a Cochrane review on interventions for postoperative nausea and vomiting in adults after general anaesthesia. Model performance is compared using average mean squared errors and coverage probabilities. RESULTS: CNMA models provide good performance for connected networks and can be an alternative to standard NMA if additivity holds. For disconnected networks, we recommend to use additive CNMA only if strong clinical arguments for additivity exist. CONCLUSIONS: CNMA methods are feasible for connected networks but questionable for disconnected networks.

Assessing severe acute respiratory syndrome coronavirus 2 infectivity by reverse-transcription polymerase chain reaction: A systematic review and meta-analysis.

The cornerstone of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection is reverse-transcription polymerase chain reaction (RT-PCR) of viral RNA. As a surrogate assay SARS-CoV-2 RNA detection does not necessarily imply infectivity. Only virus isolation in permissive cell culture systems can indicate infectivity. Here, we review the evidence on RT-PCR performance in detecting infectious SARS-CoV-2. We searched for any studies that used RT-PCR and cell culture to determine infectious SARS-CoV-2 in respiratory samples. We assessed (i) diagnostic accuracy of RT-PCR compared to cell culture as reference test, (ii) performed meta-analysis of positive predictive values (PPV) and (iii) determined the virus isolation probabilities depending on cycle threshold (Ct) or log10 genome copies/ml using logistic regression. We included 55 studies. There is substantial statistical and clinical heterogeneity. Seven studies were included for diagnostic accuracy. Sensitivity ranged from 90% to 99% and specificity from 29% to 92%. In meta-analysis, the PPVs varied across subgroups with different sampling times after symptom onset, with 1% (95% confidence interval [CI], 0%-7%) in sampling beyond 10 days and 27% (CI, 19%-36%) to 46% (CI, 33%-60%) in subgroups that also included earlier samples. Estimates of virus isolation probability varied between 6% (CI, 0%-100%) and 50% (CI, 0%-100%) at a Ct value of 30 and between 0% (CI, 0%-22%) and 63% (CI, 0%-100%) at 5 log10 genome copies/ml. Evidence on RT-PCR performance in detecting infectious SARS-CoV-2 in respiratory samples was limited. Major limitations were heterogeneity and poor reporting. RT-PCR and cell culture protocols need further standardisation.

Network meta-analysis and random walks.

Network meta-analysis (NMA) is a central tool for evidence synthesis in clinical research. The results of an NMA depend critically on the quality of evidence being pooled. In assessing the validity of an NMA, it is therefore important to know the proportion contributions of each direct treatment comparison to each network treatment effect. The construction of proportion contributions is based on the observation that each row of the hat matrix represents a so-called "evidence flow network" for each treatment comparison. However, the existing algorithm used to calculate these values is associated with ambiguity according to the selection of paths. In this article, we present a novel analogy between NMA and random walks. We use this analogy to derive closed-form expressions for the proportion contributions. A random walk on a graph is a stochastic process that describes a succession of random "hops" between vertices which are connected by an edge. The weight of an edge relates to the probability that the walker moves along that edge. We use the graph representation of NMA to construct the transition matrix for a random walk on the network of evidence. We show that the net number of times a walker crosses each edge of the network is related to the evidence flow network. By then defining a random walk on the directed evidence flow network, we derive analytically the matrix of proportion contributions. The random-walk approach has none of the associated ambiguity of the existing algorithm.

Bayesian models for aggregate and individual patient data component network meta-analysis.

Network meta-analysis can synthesize evidence from studies comparing multiple treatments for the same disease. Sometimes the treatments of a network are complex interventions, comprising several independent components in different combinations. A component network meta-analysis (CNMA) can be used to analyze such data and can in principle disentangle the individual effect of each component. However, components may interact with each other, either synergistically or antagonistically. Deciding which interactions, if any, to include in a CNMA model may be difficult, especially for large networks with many components. In this article, we present two Bayesian CNMA models that can be used to identify prominent interactions between components. Our models utilize Bayesian variable selection methods, namely the stochastic search variable selection and the Bayesian LASSO, and can benefit from the inclusion of prior information about important interactions. Moreover, we extend these models to combine data from studies providing aggregate information and studies providing individual patient data (IPD). We illustrate our models in practice using three real datasets, from studies in panic disorder, depression, and multiple myeloma. Finally, we describe methods for developing web-applications that can utilize results from an IPD-CNMA, to allow for personalized estimates of relative treatment effects given a patient's characteristics.

Diagnostic accuracy of cerebrospinal fluid biomarkers for the differential diagnosis of sporadic Creutzfeldt-Jakob disease: a (network) meta-analysis.

BACKGROUND: There are no systematic reviews of cerebrospinal fluid and blood biomarkers for sporadic Creutzfeldt-Jakob disease (sCJD) in specialized care settings that compare diagnostic accuracies in a network meta-analysis (NMA). METHODS: We searched Medline, Embase, and Cochrane Library for diagnostic studies of sCJD biomarkers. Studies had to use established diagnostic criteria for sCJD and for diseases in the non-CJD groups, which had to represent a consecutive population of patients suspected as a CJD case, as reference standard. Risk of bias was assessed with QUADAS-2. We conducted individual biomarker meta-analyses with generalized bivariate models. To investigate heterogeneity, we performed subgroup analyses based on QUADAS-2 quality and clinical criteria. For the NMA, we applied a Bayesian beta-binomial ANOVA model. The study protocol was registered at PROSPERO (CRD42019118830). RESULTS: Of 2976 publications screened, we included 16 studies, which investigated 14-3-3ß (n = 13), 14-3-3γ (n = 3), neurofilament light chain (NfL, n = 1), neuron-specific enolase (n = 1), p-tau181/t-tau ratio (n = 2), RT-QuIC (n = 7), S100B (n = 3), t-tau (n = 12), and t-tau/Aß42 ratio (n = 1). Excluded diagnostic studies had strong limitations in study design. In the NMA, RT-QuIC (0.91; 95% CI [0.83, 0.95]) and NfL (0.93 [0.78, 0.99]) were the most sensitive biomarkers for the diagnosis of definite, probable, and possible sCJD cases. RT-QuIC was the most specific biomarker (0.97 [0.89, 1.00]). Heterogeneity in accuracy estimates was high between studies. CONCLUSIONS: We identified RT-QuIC as the most accurate biomarker, partially confirming currently applied diagnostic criteria. The shortcomings identified in many diagnostic studies for sCJD biomarkers need to be addressed in future studies.

Answering complex hierarchy questions in network meta-analysis.

BACKGROUND: Network meta-analysis estimates all relative effects between competing treatments and can produce a treatment hierarchy from the most to the least desirable option according to a health outcome. While about half of the published network meta-analyses present such a hierarchy, it is rarely the case that it is related to a clinically relevant decision question. METHODS: We first define treatment hierarchy and treatment ranking in a network meta-analysis and suggest a simulation method to estimate the probability of each possible hierarchy to occur. We then propose a stepwise approach to express clinically relevant decision questions as hierarchy questions and quantify the uncertainty of the criteria that constitute them. The steps of the approach are summarized as follows: a) a question of clinical relevance is defined, b) the hierarchies that satisfy the defined question are collected and c) the frequencies of the respective hierarchies are added; the resulted sum expresses the certainty of the defined set of criteria to hold. We then show how the frequencies of all possible hierarchies relate to common ranking metrics. RESULTS: We exemplify the method and its implementation using two networks. The first is a network of four treatments for chronic obstructive pulmonary disease where the most probable hierarchy has a frequency of 28%. The second is a network of 18 antidepressants, among which Vortioxetine, Bupropion and Escitalopram occupy the first three ranks with frequency 19%. CONCLUSIONS: The developed method offers a generalised approach of producing treatment hierarchies in network meta-analysis, which moves towards attaching treatment ranking to a clear decision question, relevant to all or a subset of competing treatments.

Evaluation of circulating microRNAs as non-invasive biomarkers in the diagnosis of ovarian cancer: a case-control study.

PURPOSE: Ovarian cancer is the seventh most frequent form of malignant diseases in women worldwide and over 150,000 women die from it every year. More than 70 percent of all ovarian cancer patients are diagnosed at a late-stage disease with poor prognosis necessitating the development of sufficient screening biomarkers. MicroRNAs displayed promising potential as early diagnostics in various malignant diseases including ovarian cancer. The presented study aimed at identifying single microRNAs and microRNA combinations detecting ovarian cancer in vitro and in vivo. METHODS: Intracellular, extracellular and urinary microRNA expression levels of twelve microRNAs (let-7a, let-7d, miR-10a, miR-15a, miR-15b, miR-19b, miR-20a, miR-21, miR-100, miR-125b, miR-155, miR-222) were quantified performing quantitative real-time-PCR. Therefore, the three ovarian cancer cell lines SK-OV-3, OAW-42, EFO-27 as well as urine samples of ovarian cancer patients and healthy controls were analyzed. RESULTS: MiR-15a, miR-20a and miR-222 showed expression level alterations extracellularly, whereas miR-125b did intracellularly across the analyzed cell lines. MicroRNA expression alterations in single cell lines suggest subtype specificity in both compartments. Hypoxia and acidosis showed scarce effects on single miRNA expression levels only. Furthermore, we were able to demonstrate the feasibility to clearly detect the 12 miRNAs in urine samples. In urine, miR-15a was upregulated whereas let-7a was down-regulated in ovarian cancer patients. CONCLUSION: Intracellular, extracellular and urinary microRNA expression alterations emphasize their great potential as biomarkers in liquid biopsies. Especially, miR-15a and let-7a qualify for possible circulating biomarkers in liquid biopsies of ovarian cancer patients.

A forward search algorithm for detecting extreme study effects in network meta-analysis.

In a quantitative synthesis of studies via meta-analysis, it is possible that some studies provide a markedly different relative treatment effect or have a large impact on the summary estimate and/or heterogeneity. Extreme study effects (outliers) can be detected visually with forest/funnel plots and by using statistical outlying detection methods. A forward search (FS) algorithm is a common outlying diagnostic tool recently extended to meta-analysis. FS starts by fitting the assumed model to a subset of the data which is gradually incremented by adding the remaining studies according to their closeness to the postulated data-generating model. At each step of the algorithm, parameter estimates, measures of fit (residuals, likelihood contributions), and test statistics are being monitored and their sharp changes are used as an indication for outliers. In this article, we extend the FS algorithm to network meta-analysis (NMA). In NMA, visualization of outliers is more challenging due to the multivariate nature of the data and the fact that studies contribute both directly and indirectly to the network estimates. Outliers are expected to contribute not only to heterogeneity but also to inconsistency, compromising the NMA results. The FS algorithm was applied to real and artificial networks of interventions that include outliers. We developed an R package (NMAoutlier) to allow replication and dissemination of the proposed method. We conclude that the FS algorithm is a visual diagnostic tool that helps to identify studies that are a potential source of heterogeneity and inconsistency.

Prevalence of evidence of inconsistency and its association with network structural characteristics in 201 published networks of interventions.

BACKGROUND: Network meta-analysis (NMA) has attracted growing interest in evidence-based medicine. Consistency between different sources of evidence is fundamental to the reliability of the NMA results. The purpose of the present study was to estimate the prevalence of evidence of inconsistency and describe its association with different NMA characteristics. METHODS: We updated our collection of NMAs with articles published up to July 2018. We included networks with randomised clinical trials, at least four treatment nodes, at least one closed loop, a dichotomous primary outcome, and available arm-level data. We assessed consistency using the design-by-treatment interaction (DBT) model and testing all the inconsistency parameters globally through the Wald-type chi-squared test statistic. We estimated the prevalence of evidence of inconsistency and its association with different network characteristics (e.g., number of studies, interventions, intervention comparisons, loops). We evaluated the influence of the network characteristics on the DBT p-value via a multivariable regression analysis and the estimated Pearson correlation coefficients. We also evaluated heterogeneity in NMA (consistency) and DBT (inconsistency) random-effects models. RESULTS: We included 201 published NMAs. The p-value of the design-by-treatment interaction (DBT) model was lower than 0.05 in 14% of the networks and lower than 0.10 in 20% of the networks. Networks including many studies and comparing few interventions were more likely to have small DBT p-values (less than 0.10), which is probably because they yielded more precise estimates and power to detect differences between designs was higher. In the presence of inconsistency (DBT p-value lower than 0.10), the consistency model displayed higher heterogeneity than the DBT model. CONCLUSIONS: Our findings show that inconsistency was more frequent than what would be expected by chance, suggesting that researchers should devote more resources to exploring how to mitigate inconsistency. The results of this study highlight the need to develop strategies to detect inconsistency (because of the relatively high prevalence of evidence of inconsistency in published networks), and particularly in cases where the existing tests have low power.

Investigator initiated trials versus industry sponsored trials - translation of randomized controlled trials into clinical practice (IMPACT).

BACKGROUND: Healthcare decisions are ideally based on clinical trial results, published in study registries, as journal articles or summarized in secondary research articles. In this research project, we investigated the impact of academically and commercially sponsored clinical trials on medical practice by measuring the proportion of trials published and cited by systematic reviews and clinical guidelines. METHODS: We examined 691 multicenter, randomized controlled trials that started in 2005 or later and were completed by the end of 2016. To determine whether sponsorship/funding and place of conduct influence a trial's impact, we created four sub-cohorts of investigator initiated trials (IITs) and industry sponsored trials (ISTs): 120 IITs and 171 ISTs with German contribution compared to 200 IITs and 200 ISTs without German contribution. We balanced the groups for study phase and place of conduct. German IITs were funded by the German Research Foundation (DFG), the Federal Ministry of Education and Research (BMBF), or by another non-commercial research organization. All other trials were drawn from the German Clinical Trials Register or ClinicalTrials.gov. We investigated, to what extent study characteristics were associated with publication and impact using multivariable logistic regressions. RESULTS: For 80% of the 691 trials, results were published as result articles in a medical journal and/or study registry, 52% were cited by a systematic review, and 26% reached impact in a clinical guideline. Drug trials and larger trials were associated with a higher probability to be published and to have an impact than non-drug trials and smaller trials. Results of IITs were more often published as a journal article while results of ISTs were more often published in study registries. International ISTs less often gained impact by inclusion in systematic reviews or guidelines than IITs. CONCLUSION: An encouraging high proportion of the clinical trials were published, and a considerable proportion gained impact on clinical practice. However, there is still room for improvement. For publishing study results, study registries have become an alternative or complement to journal articles, especially for ISTs. IITs funded by governmental bodies in Germany reached an impact that is comparable to international IITs and ISTs.

Component network meta-analysis compared to a matching method in a disconnected network: A case study.

Network meta-analysis is a method to combine evidence from randomized controlled trials (RCTs) that compare a number of different interventions for a given clinical condition. Usually, this requires a connected network. A possible approach to link a disconnected network is to add evidence from nonrandomized comparisons, using propensity score or matching-adjusted indirect comparisons methods. However, nonrandomized comparisons may be associated with an unclear risk of bias. Schmitz et al. used single-arm observational studies for bridging the gap between two disconnected networks of treatments for multiple myeloma. We present a reanalysis of these data using component network meta-analysis (CNMA) models entirely based on RCTs, utilizing the fact that many of the treatments consisted of common treatment components occurring in both networks. We discuss forward and backward strategies for selecting appropriate CNMA models and compare the results to those obtained by Schmitz et al. using their matching method. CNMA models provided a good fit to the data and led to treatment rankings that were similar, though not fully equal to that obtained by Schmitz et al. We conclude that researchers encountering a disconnected network with treatments in different subnets having common components should consider a CNMA model. Such models, exclusively based on evidence from RCTs, are a promising alternative to matching approaches that require additional evidence from observational studies. CNMA models are implemented in the R package netmeta.

Meta-analysis of diagnostic accuracy studies with multiple thresholds: Comparison of different approaches.

Methods for standard meta-analysis of diagnostic test accuracy studies are well established and understood. For the more complex case in which studies report test accuracy across multiple thresholds, several approaches have recently been proposed. These are based on similar ideas, but make different assumptions. In this article, we apply four different approaches to data from a recent systematic review in the area of nephrology and compare the results. The four approaches use: a linear mixed effects model, a Bayesian multinomial random effects model, a time-to-event model and a nonparametric model, respectively. In the case study data, the accuracy of neutrophil gelatinase-associated lipocalin for the diagnosis of acute kidney injury was assessed in different scenarios, with sensitivity and specificity estimates available for three thresholds in each primary study. All approaches led to plausible and mostly similar summary results. However, we found considerable differences in results for some scenarios, for example, differences in the area under the receiver operating characteristic curve (AUC) of up to 0.13. The Bayesian approach tended to lead to the highest values of the AUC, and the nonparametric approach tended to produce the lowest values across the different scenarios. Though we recommend using these approaches, our findings motivate the need for a simulation study to explore optimal choice of method in various scenarios.

Meta-analyses identify differentially expressed micrornas in Parkinson's disease.

OBJECTIVE: MicroRNA (miRNA)-mediated (dys)regulation of gene expression has been implicated in Parkinson's disease (PD), although results of miRNA expression studies remain inconclusive. We aimed to identify miRNAs that show consistent differential expression across all published expression studies in PD. METHODS: We performed a systematic literature search on miRNA expression studies in PD and extracted data from eligible publications. After stratification for brain, blood, and cerebrospinal fluid (CSF)-derived specimen, we performed meta-analyses across miRNAs assessed in three or more independent data sets. Meta-analyses were performed using effect-size- and p-value-based methods, as applicable. RESULTS: After screening 599 publications, we identified 47 data sets eligible for meta-analysis. On these, we performed 160 meta-analyses on miRNAs quantified in brain (n = 125), blood (n = 31), or CSF (n = 4). Twenty-one meta-analyses were performed using effect sizes. We identified 13 significantly (Bonferroni-adjusted α = 3.13 × 10-4 ) differentially expressed miRNAs in brain (n = 3) and blood (n = 10) with consistent effect directions across studies. The most compelling findings were with hsa-miR-132-3p (p = 6.37 × 10-5 ), hsa-miR-497-5p (p = 1.35 × 10-4 ), and hsa-miR-133b (p = 1.90 × 10-4 ) in brain and with hsa-miR-221-3p (p = 4.49 × 10-35 ), hsa-miR-214-3p (p = 2.00 × 10-34 ), and hsa-miR-29c-3p (p = 3.00 × 10-12 ) in blood. No significant signals were found in CSF. Analyses of genome-wide association study data for target genes of brain miRNAs showed significant association (α = 9.40 × 10-5 ) of genetic variants in nine loci. INTERPRETATION: We identified several miRNAs that showed highly significant differential expression in PD. Future studies may assess the possible role of the identified brain miRNAs in pathogenesis and disease progression as well as the potential of the top blood miRNAs as biomarkers for diagnosis, progression, or prediction of PD. ANN NEUROL 2019;85:835-851.

The statistical importance of a study for a network meta-analysis estimate.

BACKGROUND: In pairwise meta-analysis, the contribution of each study to the pooled estimate is given by its weight, which is based on the inverse variance of the estimate from that study. For network meta-analysis (NMA), the contribution of direct (and indirect) evidence is easily obtained from the diagonal elements of a hat matrix. It is, however, not fully clear how to generalize this to the percentage contribution of each study to a NMA estimate. METHODS: We define the importance of each study for a NMA estimate by the reduction of the estimate's variance when adding the given study to the others. An equivalent interpretation is the relative loss in precision when the study is left out. Importances are values between 0 and 1. An importance of 1 means that the study is an essential link of the pathway in the network connecting one of the treatments with another. RESULTS: Importances can be defined for two-stage and one-stage NMA. These numbers in general do not add to one and thus cannot be interpreted as 'percentage contributions'. After briefly discussing other available approaches, we question whether it is possible to obtain unique percentage contributions for NMA. CONCLUSIONS: Importances generalize the concept of weights in pairwise meta-analysis in a natural way. Moreover, they are uniquely defined, easily calculated, and have an intuitive interpretation. We give some real examples for illustration.

Drugs for preventing postoperative nausea and vomiting in adults after general anaesthesia: a network meta-analysis.

BACKGROUND: Postoperative nausea and vomiting (PONV) is a common adverse effect of anaesthesia and surgery. Up to 80% of patients may be affected. These outcomes are a major cause of patient dissatisfaction and may lead to prolonged hospital stay and higher costs of care along with more severe complications. Many antiemetic drugs are available for prophylaxis. They have various mechanisms of action and side effects, but there is still uncertainty about which drugs are most effective with the fewest side effects. OBJECTIVES: • To compare the efficacy and safety of different prophylactic pharmacologic interventions (antiemetic drugs) against no treatment, against placebo, or against each other (as monotherapy or combination prophylaxis) for prevention of postoperative nausea and vomiting in adults undergoing any type of surgery under general anaesthesia • To generate a clinically useful ranking of antiemetic drugs (monotherapy and combination prophylaxis) based on efficacy and safety • To identify the best dose or dose range of antiemetic drugs in terms of efficacy and safety SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the World Health Organization International Clinical Trials Registry Platform (WHO ICTRP), ClinicalTrials.gov, and reference lists of relevant systematic reviews. The first search was performed in November 2017 and was updated in April 2020. In the update of the search, 39 eligible studies were found that were not included in the analysis (listed as awaiting classification). SELECTION CRITERIA: Randomized controlled trials (RCTs) comparing effectiveness or side effects of single antiemetic drugs in any dose or combination against each other or against an inactive control in adults undergoing any type of surgery under general anaesthesia. All antiemetic drugs belonged to one of the following substance classes: 5-HT3 receptor antagonists, D2 receptor antagonists, NK1 receptor antagonists, corticosteroids, antihistamines, and anticholinergics. No language restrictions were applied. Abstract publications were excluded. DATA COLLECTION AND ANALYSIS: A review team of 11 authors independently assessed trials for inclusion and risk of bias and subsequently extracted data. We performed pair-wise meta-analyses for drugs of direct interest (amisulpride, aprepitant, casopitant, dexamethasone, dimenhydrinate, dolasetron, droperidol, fosaprepitant, granisetron, haloperidol, meclizine, methylprednisolone, metoclopramide, ondansetron, palonosetron, perphenazine, promethazine, ramosetron, rolapitant, scopolamine, and tropisetron) compared to placebo (inactive control). We performed network meta-analyses (NMAs) to estimate the relative effects and ranking (with placebo as reference) of all available single drugs and combinations. Primary outcomes were vomiting within 24 hours postoperatively, serious adverse events (SAEs), and any adverse event (AE). Secondary outcomes were drug class-specific side effects (e.g. headache), mortality, early and late vomiting, nausea, and complete response. We performed subgroup network meta-analysis with dose of drugs as a moderator variable using dose ranges based on previous consensus recommendations. We assessed certainty of evidence of NMA treatment effects for all primary outcomes and drug class-specific side effects according to GRADE (CINeMA, Confidence in Network Meta-Analysis). We restricted GRADE assessment to single drugs of direct interest compared to placebo. MAIN RESULTS: We included 585 studies (97,516 randomized participants). Most of these studies were small (median sample size of 100); they were published between 1965 and 2017 and were primarily conducted in Asia (51%), Europe (25%), and North America (16%). Mean age of the overall population was 42 years. Most participants were women (83%), had American Society of Anesthesiologists (ASA) physical status I and II (70%), received perioperative opioids (88%), and underwent gynaecologic (32%) or gastrointestinal surgery (19%) under general anaesthesia using volatile anaesthetics (88%). In this review, 44 single drugs and 51 drug combinations were compared. Most studies investigated only single drugs (72%) and included an inactive control arm (66%). The three most investigated single drugs in this review were ondansetron (246 studies), dexamethasone (120 studies), and droperidol (97 studies). Almost all studies (89%) reported at least one efficacy outcome relevant for this review. However, only 56% reported at least one relevant safety outcome. Altogether, 157 studies (27%) were assessed as having overall low risk of bias, 101 studies (17%) overall high risk of bias, and 327 studies (56%) overall unclear risk of bias. Vomiting within 24 hours postoperatively Relative effects from NMA for vomiting within 24 hours (282 RCTs, 50,812 participants, 28 single drugs, and 36 drug combinations) suggest that 29 out of 36 drug combinations and 10 out of 28 single drugs showed a clinically important benefit (defined as the upper end of the 95% confidence interval (CI) below a risk ratio (RR) of 0.8) compared to placebo. Combinations of drugs were generally more effective than single drugs in preventing vomiting. However, single NK1 receptor antagonists showed treatment effects similar to most of the drug combinations. High-certainty evidence suggests that the following single drugs reduce vomiting (ordered by decreasing efficacy): aprepitant (RR 0.26, 95% CI 0.18 to 0.38, high certainty, rank 3/28 of single drugs); ramosetron (RR 0.44, 95% CI 0.32 to 0.59, high certainty, rank 5/28); granisetron (RR 0.45, 95% CI 0.38 to 0.54, high certainty, rank 6/28); dexamethasone (RR 0.51, 95% CI 0.44 to 0.57, high certainty, rank 8/28); and ondansetron (RR 0.55, 95% CI 0.51 to 0.60, high certainty, rank 13/28). Moderate-certainty evidence suggests that the following single drugs probably reduce vomiting: fosaprepitant (RR 0.06, 95% CI 0.02 to 0.21, moderate certainty, rank 1/28) and droperidol (RR 0.61, 95% CI 0.54 to 0.69, moderate certainty, rank 20/28). Recommended and high doses of granisetron, dexamethasone, ondansetron, and droperidol showed clinically important benefit, but low doses showed no clinically important benefit. Aprepitant was used mainly at high doses, ramosetron at recommended doses, and fosaprepitant at doses of 150 mg (with no dose recommendation available). Frequency of SAEs Twenty-eight RCTs were included in the NMA for SAEs (10,766 participants, 13 single drugs, and eight drug combinations). The certainty of evidence for SAEs when using one of the best and most reliable anti-vomiting drugs (aprepitant, ramosetron, granisetron, dexamethasone, ondansetron, and droperidol compared to placebo) ranged from very low to low. Droperidol (RR 0.88, 95% CI 0.08 to 9.71, low certainty, rank 6/13) may reduce SAEs. We are uncertain about the effects of aprepitant (RR 1.39, 95% CI 0.26 to 7.36, very low certainty, rank 11/13), ramosetron (RR 0.89, 95% CI 0.05 to 15.74, very low certainty, rank 7/13), granisetron (RR 1.21, 95% CI 0.11 to 13.15, very low certainty, rank 10/13), dexamethasone (RR 1.16, 95% CI 0.28 to 4.85, very low certainty, rank 9/13), and ondansetron (RR 1.62, 95% CI 0.32 to 8.10, very low certainty, rank 12/13). No studies reporting SAEs were available for fosaprepitant. Frequency of any AE Sixty-one RCTs were included in the NMA for any AE (19,423 participants, 15 single drugs, and 11 drug combinations). The certainty of evidence for any AE when using one of the best and most reliable anti-vomiting drugs (aprepitant, ramosetron, granisetron, dexamethasone, ondansetron, and droperidol compared to placebo) ranged from very low to moderate. Granisetron (RR 0.92, 95% CI 0.80 to 1.05, moderate certainty, rank 7/15) probably has no or little effect on any AE. Dexamethasone (RR 0.77, 95% CI 0.55 to 1.08, low certainty, rank 2/15) and droperidol (RR 0.89, 95% CI 0.81 to 0.98, low certainty, rank 6/15) may reduce any AE. Ondansetron (RR 0.95, 95% CI 0.88 to 1.01, low certainty, rank 9/15) may have little or no effect on any AE. We are uncertain about the effects of aprepitant (RR 0.87, 95% CI 0.78 to 0.97, very low certainty, rank 3/15) and ramosetron (RR 1.00, 95% CI 0.65 to 1.54, very low certainty, rank 11/15) on any AE. No studies reporting any AE were available for fosaprepitant. Class-specific side effects For class-specific side effects (headache, constipation, wound infection, extrapyramidal symptoms, sedation, arrhythmia, and QT prolongation) of relevant substances, the certainty of evidence for the best and most reliable anti-vomiting drugs mostly ranged from very low to low. Exceptions were that ondansetron probably increases headache (RR 1.16, 95% CI 1.06 to 1.28, moderate certainty, rank 18/23) and probably reduces sedation (RR 0.87, 95% CI 0.79 to 0.96, moderate certainty, rank 5/24) compared to placebo. The latter effect is limited to recommended and high doses of ondansetron. Droperidol probably reduces headache (RR 0.76, 95% CI 0.67 to 0.86, moderate certainty, rank 5/23) compared to placebo. We have high-certainty evidence that dexamethasone (RR 1.00, 95% CI 0.91 to 1.09, high certainty, rank 16/24) has no effect on sedation compared to placebo. No studies assessed substance class-specific side effects for fosaprepitant. Direction and magnitude of network effect estimates together with level of evidence certainty are graphically summarized for all pre-defined GRADE-relevant outcomes and all drugs of direct interest compared to placebo in http://doi.org/10.5281/zenodo.4066353. AUTHORS' CONCLUSIONS: We found high-certainty evidence that five single drugs (aprepitant, ramosetron, granisetron, dexamethasone, and ondansetron) reduce vomiting, and moderate-certainty evidence that two other single drugs (fosaprepitant and droperidol) probably reduce vomiting, compared to placebo. Four of the six substance classes (5-HT3 receptor antagonists, D2 receptor antagonists, NK1 receptor antagonists, and corticosteroids) were thus represented by at least one drug with important benefit for prevention of vomiting. Combinations of drugs were generally more effective than the corresponding single drugs in preventing vomiting. NK1 receptor antagonists were the most effective drug class and had comparable efficacy to most of the drug combinations. 5-HT3 receptor antagonists were the best studied substance class. For most of the single drugs of direct interest, we found only very low to low certainty evidence for safety outcomes such as occurrence of SAEs, any AE, and substance class-specific side effects. Recommended and high doses of granisetron, dexamethasone, ondansetron, and droperidol were more effective than low doses for prevention of vomiting. Dose dependency of side effects was rarely found due to the limited number of studies, except for the less sedating effect of recommended and high doses of ondansetron. The results of the review are transferable mainly to patients at higher risk of nausea and vomiting (i.e. healthy women undergoing inhalational anaesthesia and receiving perioperative opioids). Overall study quality was limited, but certainty assessments of effect estimates consider this limitation. No further efficacy studies are needed as there is evidence of moderate to high certainty for seven single drugs with relevant benefit for prevention of vomiting. However, additional studies are needed to investigate potential side effects of these drugs and to examine higher-risk patient populations (e.g. individuals with diabetes and heart disease).

Network meta-analysis of multicomponent interventions.

In network meta-analysis (NMA), treatments can be complex interventions, for example, some treatments may be combinations of others or of common components. In standard NMA, all existing (single or combined) treatments are different nodes in the network. However, sometimes an alternative model is of interest that utilizes the information that some treatments are combinations of common components, called component network meta-analysis (CNMA) model. The additive CNMA model assumes that the effect of a treatment combined of two components A and B is the sum of the effects of A and B, which is easily extended to treatments composed of more than two components. This implies that in comparisons equal components cancel out. Interaction CNMA models also allow interactions between the components. Bayesian analyses have been suggested. We report an implementation of CNMA models in the frequentist R package netmeta. All parameters are estimated using weighted least squares regression. We illustrate the application of CNMA models using an NMA of treatments for depression in primary care. Moreover, we show that these models can even be applied to disconnected networks, if the composite treatments in the subnetworks contain common components.