Association between increased antenatal vaginal pH and preterm birth rate : a systematic review

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)Background: Worldwide, 14.9 million infants (11%) are born preterm each year. Up to 40% of preterm births (PTBs) are associated with genital tract infections. The vaginal pH can reflect changes in the vaginal milieu and, if elevated, indicates an abnormal flora or infection. Objective: The aim of the study was to investigate whether an increased antenatal vaginal pH >4.5 in pre-labour pregnant women is associated with an increased PTB rate <37 completed weeks gestation. Search strategy Key databases included SCOPUS, EMBASE, MEDLINE, PsycInfo and the Cochrane Central Register of Controlled Trials, complemented by hand search, up to January 2017. Selection criteria Primary research reporting vaginal pH assessment in pre-labour pregnant women and PTB rate. Data collection and analysis: Data extraction and appraisal were carried out in a pre-defined standardised manner, applying the Newcastle-Ottawa scale (NOS) and Cochrane risk of bias tool. Analysis included calculation of risk difference (RD) and narrative synthesis. It was decided to abstain from pooling of the studies due to missing information in important moderators. Main results: Of 986 identified records, 30 were included in the systematic review. The risk of bias was considered mostly high (40%) or moderate (37%). Fifteen studies permitted a calculation of RD. Of these, 14 (93%) indicated a positive association between increased antenatal vaginal pH and PTB (RD range: 0.02-0.75). Conclusions: An increased antenatal vaginal pH >4.5 may be associated with a higher risk for PTB. It is recommended to conduct a randomised controlled trial (RCT) to investigate the effectiveness of antenatal pH screening to prevent PTB. Tweetable abstract Pregnant women with an increased vaginal pH >4.5 may be at higher risk to experience preterm birth

Comparative efficacy of materials used in patients undergoing pulpotomy or direct pulp capping in carious teeth: A systematic review and meta-analysis.

OBJECTIVES Different materials have been used for capping the pulp after exposure during caries removal in permanent teeth. The purpose of this study was to collate and analyze all pertinent evidence from randomized controlled trials (RCTs) on different materials used in patients undergoing pulpotomy or direct pulp capping in carious teeth. MATERIALS AND METHODS Trials comparing two or more capping agents used for direct pulp capping (DPC) or pulpotomy were considered eligible. An electronic search of four databases and two clinical trial registries was carried out up to February 28, 2021 using a search strategy properly adapted to the PICO framework. Screening, data extraction, and risk of bias (RoB) assessment of primary studies were performed in duplicate and independently. The primary outcome was clinical and radiological success; secondary outcomes included continued root formation, tooth discoloration, and dentin bridge formation. RESULTS 21 RCTs were included in the study. The RoB assessment indicated a moderate risk among the studies. Due to significant clinical and statistical heterogeneity among the studies, performing network meta-analysis (NMA) was not possible. An ad hoc subgroup analysis revealed strong evidence of a higher success of DPC with Mineral Trioxide Aggregate (MTA) compared to calcium hydroxide (CH) (odds ratio [OR] = 3.10, 95% confidence interval [CI]: 1.66-5.79). MTA performed better than CH in pulp capping (both DPC and pulpotomy) of mature compared to immature teeth (OR = 3.34, 95% CI: 1.81-6.17). The GRADE assessment revealed moderate strength of evidence for DPC and mature teeth, and low to very low strength of evidence for the remaining subgroups. CONCLUSIONS Considerable clinical and statistical heterogeneity among the trials did not allow NMA. The ad hoc subgroup analysis indicated that the clinical and radiographic success of MTA was higher than that of CH but only in mature teeth and DPC cases where the strength of evidence was moderate. PROSPERO Registration: number CRD42020127239

Applicability and added value of novel methods to improve drug development in rare diseases

The ASTERIX project developed a number of novel methods suited to study small populations. The objective of this exercise was to evaluate the applicability and added value of novel methods to improve drug development in small populations, using real world drug development programmes as reported in European Public Assessment Reports. The applicability and added value of thirteen novel methods developed within ASTERIX were evaluated using data from 26 European Public Assessment Reports (EPARs) for orphan medicinal products, representative of rare medical conditions as predefined through six clusters. The novel methods included were 'innovative trial designs' (six methods), 'level of evidence' (one method), 'study endpoints and statistical analysis' (four methods), and 'meta-analysis' (two methods) and they were selected from the methods developed within ASTERIX based on their novelty; methods that discussed already available and applied strategies were not included for the purpose of this validation exercise. Pre-requisites for application in a study were systematized for each method, and for each main study in the selected EPARs it was assessed if all pre-requisites were met. This direct applicability using the actual study design was firstly assessed. Secondary, applicability and added value were explored allowing changes to study objectives and design, but without deviating from the context of the drug development plan. We evaluated whether differences in applicability and added value could be observed between the six predefined condition clusters. Direct applicability of novel methods appeared to be limited to specific selected cases. The applicability and added value of novel methods increased substantially when changes to the study setting within the context of drug development were allowed. In this setting, novel methods for extrapolation, sample size re-assessment, multi-armed trials, optimal sequential design for small sample sizes, Bayesian sample size re-estimation, dynamic borrowing through power priors and fall-back tests for co-primary endpoints showed most promise - applicable in more than 40% of evaluated EPARs in all clusters. Most of the novel methods were applicable to conditions in the cluster of chronic and progressive conditions, involving multiple systems/organs. Relatively fewer methods were applicable to acute conditions with single episodes. For the chronic clusters, Goal Attainment Scaling was found to be particularly applicable as opposed to other (non-chronic) clusters. Novel methods as developed in ASTERIX can improve drug development programs. Achieving optimal added value of these novel methods often requires consideration of the entire drug development program, rather than reconsideration of methods for a specific trial. The novel methods tested were mostly applicable in chronic conditions, and acute conditions with recurrent episodes. The online version of this article (10.1186/s13023-018-0925-0) contains supplementary material, which is available to authorized users

An empirical comparison of Bayesian modelling strategies for missing binary outcome data in network meta-analysis

Abstract Background A number of strategies have been proposed to handle missing binary outcome data (MOD) in systematic reviews. However, none of these have been evaluated empirically in a series of published systematic reviews. Methods Using published systematic reviews with network meta-analysis (NMA) from a wide range of health-related fields, we evaluated comparatively the most frequently described Bayesian modelling strategies for MOD in terms of log odds ratio (log OR), between-trial variance, inconsistency factor (i.e. difference between direct and indirect estimates for a comparison), surface under the cumulative ranking (SUCRA) and rankings. We extended the Bayesian random-effects NMA model to incorporate the informative missingness odds ratio (IMOR) parameter, and applied the node-splitting approach to investigate inconsistency locally. We considered both pattern-mixture and selection models, different structures for prior distribution of log IMOR, and different scenarios for MOD. To illustrate level of agreement between different strategies and scenarios, we used Bland-Altman plots. Results Addressing MOD using extreme scenarios and ignoring the uncertainty about the scenarios led to systematically different and more precise log ORs compared to modelling MOD under the missing at random (MAR) assumption. Hierarchical structure of log IMORs led to lower between-trial variance, especially in the case of substantial MOD. Assuming common-within-network or trial-specific log IMORs yielded similar posterior results for all NMA estimates, whereas intervention-specific structure systematically inflated uncertainty around log ORs and SUCRAs. Pattern-mixture model agreed with selection model, particularly under the trial-specific structure; however, selection model systematically reduced precision around log IMORs. Overall, different strategies and scenarios mostly had good agreement in the case of low MOD. Conclusions Addressing MOD using extreme scenarios and/or ignoring the uncertainty about the scenarios may negatively affect NMA estimates. Modelling MOD via the IMOR parameter can ensure bias-adjusted estimates and offer valuable insights into missingness mechanisms. The researcher should seek an expert opinion in order to decide on the structure of log IMOR that best aligns to the condition and interventions studied and to define a proper prior distribution for log IMOR. Our findings also apply to pairwise meta-analyses

Μελέτες με ελλιπούσες τιμές στη μετα-ανάλυση δικτύων

According to systematic reviews in psychiatry, the dropout rate isconsiderable high in the studies of this field. Despite the high dropoutout, the reporting on the extent of missing outcome data, the analyticalstrategies undertaken and the acknowledgment of the implication ofmissing outcome data on the results have been particularly insufficient.190 systematic reviews in the mental health field published in theCochrane Database of Systematic Reviews after 1/1/2009 by threeCochrane Review Groups were considered in order to provide empiricalevidence on addressing missing outcome data. The findings of thisreview indicated that the description of the amount and the extend ofmissing outcome data and the inclusion of their implications in thefindings of the systematic reviews seem to be particularly suboptimal.Another importance consideration in the missing outcome data issueis the minimization of their occurrence. In order to prevent as much aspossible the missing outcome data, it is important to investigate and tounderstand the reasons that the patients drop out early from trials.Therefore, an extensive research was initiated to identify trialcharacteristics that have an impact on premature discontinuation inantipsychotic trials for schizophrenia. The findings of this researchrevealed that high dropout rates in antipsychotic trials can be associatedwith various characteristics, particularly with the use of placebo ascontrol and the size of the study.A conventional meta-analysis model has been suggested to betterquantify uncertainty in the imputations of the missing outcome data byincorporating in the model an unknown parameter that refers to binarymissing outcome data and it has been called the ‘informativemissingness odds ratio’ (IMOR). This parameter describes therelationship between the unknown occurrence of the outcome amongmissing participants and the known outcome in observed participants.The present research extended the idea of IMOR into a network metaanalysissetting. Two dataset comparing anti-manic treatments andantidepressants were used. In the original meta-analyses, missingparticipants were assumed to have failed regardless the treatment theywere randomized to. This assumption was evaluated by consideringseveral IMOR models that reflected different assumption on the missingoutcome data. In both datasets, the relative effectiveness of thetreatments was affected only by the worst- and best-case analyses.Moreover, heterogeneity increased in both datasets under these twoextreme scenarios. Overall, there were small changes on the ranking ofthe anti-manic and antidepressant treatments. The posterior and priorIMOR distributions were very similar implying that there was littleinformation about the true outcome in missing participants.Σύμφωνα με τις συστηματικές ανασκοπήσεις στην ψυχιατρική, τοποσοστό ελλιπουσών τιμών είναι ιδιαίτερα υψηλό στις μελέτες αυτούτου πεδίου. Παρά το υψηλό ποσοστό ελλιπουσών τιμών, η αναφοράστην έκταση των ελλιπουσών τιμών αποτελέσματος, οι αναλυτικέςστρατηγικές αντιμετώπισης αυτών και η αναγνώριση των συνεπειώντων ελλιπουσών τιμών αποτελέσματος στα ευρήματα της μελέτης είναιιδιαίτερα ανεπαρκή. 190 συστηματικές ανασκοπήσεις στο πεδίο τηςψυχικής υγείας δημοσιευμένες στην βάση δεδομένων της Cochraneμετά το 1/1/2009 σε τρεις ομάδες ανασκόπησης συλλέχθηκαν με σκοπόνα παρέχουν εμπειρική ένδειξη για την αντιμετώπιση των ελλιπουσώντιμών αποτελέσματος. Τα αποτελέσματα της παρούσας μελέτης έδειξανότι η περιγραφή του μεγέθους και της έκτασης των ελλιπουσών τιμών αποτελέσματος και η συμπερίληψη των συνεπειών αυτών στα ευρήματατων συστηματικών ανασκοπήσεων είναι ιδιαίτερα ανεπαρκής.Η ελαχιστοποίηση της εμφάνισης των ελλιπουσών τιμώναποτελέσματος είναι επίσης ένα σημαντικό θέμα. Για την πρόληψη όσοτο δυνατόν περισσότερων ελλιπουσών τιμών αποτελέσματος, είναισημαντική η εξερεύνηση και η κατανόηση των λόγων που οι ασθενείςεγκαταλείπουν νωρίς τις μελέτες. Επομένως, η παρούσα μελέτηξεκίνησε τον εντοπισμό των χαρακτηριστικών μελετών που έχουναντίκτυπο στην πρόωρη εγκατάλειψη των αντιψυχωτικών μελετών γιατη σχιζοφρένεια. Τα ευρήματα της παρούσας μελέτης αποκάλυψαν ότιυψηλά ποσοστά των ελλιπουσών τιμών αποτελέσματος στιςαντιψυχωτικές μελέτες μπορεί να σχετίζονται με διάφοραχαρακτηριστικά μελετών και κυρίως με την χρήση εικονικής θεραπείαςως θεραπεία ελέγχου και το μέγεθος της μελέτης.Ένα μοντέλο απλής μετα-ανάλυσης έχει προταθεί με σκοπό ναποσοτικοποιήσει την αβεβαιότητα στις αντικαταστάσεις των ελλιπουσώντιμών αποτελέσματος εισάγοντας στο μοντέλο μία άγνωστη παράμετροπου αφορά δίτιμα δεδομένα και ονομάζεται ‘αναλογία πιθανοτήτωνελλιπουσών τιμών αποτελέσματος με πληροφορία’ (informativemissingness odds ratio, IMOR). Αυτή η παράμετρος περιγράφει τηνσχέση μεταξύ του άγνωστου αποτελέσματος στους ελλιπόντες συμμετέχοντες και του γνωστού αποτελέσματος στουςπαρατηρούμενους συμμετέχοντες. Η παρούσα μελέτη επέκτεινε την ιδέαγια την IMOR παράμετρο στο δίκτυο μετα-ανάλυσης. Ένα σετδεδομένων για αντιμανιακές θεραπείες και ένα σετ δεδομένων γιααντικαταθλιπτικές θεραπείες χρησιμοποιήθηκαν. Στις αρχικές μετα-αναλύσεις, οι ελλιπόντες συμμετέχοντες θεωρήθηκαν ότι απέτυχαν νααποκριθούν στη θεραπεία τους. Η υπόθεση αυτή αξιολογήθηκεεφαρμόζοντας διάφορα IMOR μοντέλα που αντανακλούν διαφορετικήυπόθεση για τα ελλιπόντα δεδομένα αποτελέσματος. Στα δύο σετδεδομένων, η σχετική αποτελεσματικότητα των θεραπειών επηρεάστηκεμόνο από την χειρότερη και την καλύτερη υπόθεση για τα ελλιπόνταδεδομένα αποτελέσματος. Επιπλέον, η ετερογένεια αυξήθηκε και σταδύο σετ δεδομένων σε αυτές τις δύο ακραίες υποθέσεις. Συνολικά,υπήρχαν μικρές διαφορές στην ιεράρχιση των αντιμανιακών και τωναντικαταθλιπτικών θεραπειών. Η τελική κατανομή των IMORπαραμέτρων ήταν πολύ όμοια με την αρχική κατανομή αυτών και αυτόδείχνει ότι υπήρχε πολύ λίγη πληροφορία για το πραγματικόαποτέλεσμα των ελλιπόντων συμμετέχοντων

Meta-analysis: Fixed-effect model

A meta-analysis, a statistical combination of data from selected studies, is implemented by choosing a priori between 2 popular statistical models: fixed-effect and random-effects models.1 The choice of the appropriate model for the analysis is critical to ensure the credibility of the results and depends on both the goals of the analysis and the assumptions of the models.1 In this section, we introduce these 2 models, describe how to perform a meta-analysis under these models, and apply a real-data example from a published systematic review

Publication bias: Graphical and statistical methods.

Various statistical approaches and visual tools have been developed to detect, estimate, and evaluate the impact of publication bias in meta-analysis results. In this article, we present the most popular statistical methods and graphic tools to address publication bias using an example