Defining the exit meta-analysis.

INTRODUCTION: In recent decades, clinical research has seen significant advancements, both in the generation and synthesis of evidence through meta-analyses. Despite these methodological advancements, there is a growing concern about the accumulation of repetitive and redundant literature, potentially contributing to research waste. This highlights the necessity for a mechanism to determine when a meta-analysis has conclusively addressed a research question, signaling no further need for additional studies-a concept we term an "exit" meta-analysis. METHODS: We introduced a convergence index, the Doi-Abdulmajeed Trial Stability (DAts) index, and a convergence plot to determine the exit status of a meta-analysis. The performance of DAts was examined through simulation and applied to two real-world meta-analyses. RESULTS: The DAts index and convergence plot demonstrate highly effective discriminative ability across varying study scenarios. This represents the first attempt to define an exit meta-analysis using a quantitative measurement of stability (as opposed to sufficiency) and its corresponding plot. The application to real-world scenarios further validated the utility of DAts and the convergence plot in identifying a conclusive (exit) meta-analyses. CONCLUSION: The new development of DAts and the convergence plot provide a promising tool for investigating the conclusiveness of meta-analyses. By identifying an exit status for meta-analysis, the scientific community may be equipped to make better-informed decisions on the continuation of research on a specific topic, thereby preventing research waste and focusing efforts on areas with unresolved questions.

Influence of lack of blinding on the estimation of medication-related harms: a retrospective cohort study of randomized controlled trials.

BACKGROUND: Empirical evidence suggests that lack of blinding may be associated with biased estimates of treatment benefit in randomized controlled trials, but the influence on medication-related harms is not well-recognized. We aimed to investigate the association between blinding and clinical trial estimates of medication-related harms. METHODS: We searched PubMed from January 1, 2015, till January 1, 2020, for systematic reviews with meta-analyses of medication-related harms. Eligible meta-analyses must have contained trials both with and without blinding. Potential covariates that may confound effect estimates were addressed by restricting trials within the comparison or by hierarchical analysis of harmonized groups of meta-analyses (therefore harmonizing drug type, control, dosage, and registration status) across eligible meta-analyses. The weighted hierarchical linear regression was then used to estimate the differences in harm estimates (odds ratio, OR) between trials that lacked blinding and those that were blinded. The results were reported as the ratio of OR (ROR) with its 95% confidence interval (CI). RESULTS: We identified 629 meta-analyses of harms with 10,069 trials. We estimated a weighted average ROR of 0.68 (95% CI: 0.53 to 0.88, P < 0.01) among 82 trials in 20 meta-analyses where blinding of participants was lacking. With regard to lack of blinding of healthcare providers or outcomes assessors, the RORs were 0.68 (95% CI: 0.53 to 0.87, P < 0.01 from 81 trials in 22 meta-analyses) and 1.00 (95% CI: 0.94 to 1.07, P = 0.94 from 858 trials among 155 meta-analyses) respectively. Sensitivity analyses indicate that these findings are applicable to both objective and subjective outcomes. CONCLUSIONS: Lack of blinding of participants and health care providers in randomized controlled trials may underestimate medication-related harms. Adequate blinding in randomized trials, when feasible, may help safeguard against potential bias in estimating the effects of harms.

Determining the conclusiveness of a meta-analysis.

The pursuit of conclusive evidence related to an unanswered foreground (decision-making) question has been the driving factor behind multiple ongoing and planned randomized controlled trials as well as meta-analyses. However, a fundamental challenge lies in establishing robust methods for ascertaining whether a collection of synthesized trials has yielded a definitive answer to that foreground question through the process of meta-analysis. This article explores the evolution of methods that attempt to address this challenge. These methods have primarily focused on defining and measuring the sufficiency and stability of evidence within a meta-analytic context. Cumulative meta-analysis and trial sequential analysis are the tools currently used, but they both come with limitations and challenges. We further discuss methods aimed at evaluating the evolution of effects over time more directly, such as the recursive cumulative meta-analysis. The latter method can be considered a better alternative, as it serves to demonstrate whether there is a true underlying treatment effect to which the meta-analysis is converging. However, recursive cumulative meta-analysis falls short of a specific indicator that establishes whether convergence has been reached. We coin the term exit for a meta-analysis where convergence can be demonstrated. Developing methods to determine the exit status of a meta-analysis is the next priority in research synthesis methods, as it will indicate that the research journey has concluded on a particular foreground question with no expectation of a different result with the addition of future trials.

Bioelectrical Impedance Analysis Detects Body Fat Changes After Surgical Subcutaneous Fat Removal.

Background: The risk and metabolic effects of obesity are determined by the distribution of fat throughout the body. It has been proposed that the distribution of abdominal fat is more closely related to the metabolic risks of obesity. High prevalence of overweight and obesity has thereby contributed to an increased uptake of surgical subcutaneous fat removal (SSFR) procedures. The goal of this study was to determine whether bioelectrical impedance analysis (Tanita system) can be used to detect the removal of excess abdominal subcutaneous fat tissue during SSFR when studying the metabolic effects of such procedures. Methods: Study population comprised patients who received body contouring procedures at the Hamad General Hospital's plastic surgery department between November 2020 and December 2022. To evaluate the factors of interest, subjects were prospectively followed up at two time points: within 1 week before the surgery and within 1-2 weeks thereafter. The following factors were measured: body weight, body fat percentage, body fat mass, body mass index (BMI), fat-free mass, estimated muscle mass, total body water, visceral fat score, and basal metabolic rate. Results: In total, 22 patients were included in the study. The two visits' medians for height, weight, BMI, fat percent (fat%), fat mass, visceral fat rating, and Doi's weighted average glucose (dwAG) were compared. Only in the case of Tanita fat% and fat mass, were the preoperative and postoperative medians significantly different. Furthermore, there was no association between these Tanita measures and dwAG or homeostatic model assessment (HOMA; insulin resistance [IR]) changes (before and after surgery). Tanita measures overestimated fat loss, as seen by the mountain plot and Bland-Altman plot agreement methods. Conclusions: Our findings indicated that the only two Tanita measures exhibited meaningful early associations with the amount of tissue excised which were fat mass and fat% differences. Although dwAG and HOMA-IR are not impacted immediately postsurgery, a trend was seen that suggested improvements in those parameters, even though the changes are not clinically significant.

Time to Move Beyond a Binary Criterion for Gestational Diabetes?

Over the years, several international guidelines have been developed by specialist organizations for the diagnosis of gestational diabetes mellitus (GDM). However, these guidelines vary and lack consensus on what level of glycemia defines GDM and worryingly, there is now evidence of over- or- under-diagnosis of women with GDM by current criteria. Towards this end, the National Priorities Research Program (NPRP) funded a program of research aimed at elucidating the problem with GDM diagnosis. It was determined, on completion of the project, that the solution required diagnosis of graded levels of dysglycemia in pregnancy and not just a diagnosis of presence or absence of GDM. A new diagnostic criterion (called the NPRP criterion) was created based on a single numerical summary of the three readings from the oral glucose tolerance test (GTT) that diagnosed women in pregnancy into four levels: normal, impaired, GDM and high risk GDM. This paper now examines existing GDM criteria vis-à-vis the NPRP criterion. It is noted that no significant change has happened over the years for existing criteria except for a gradual reduction in the threshold values of individual time-points or the number of time points, bringing us towards over-diagnosis of GDM in pregnancy. The new criterion unifies all readings from the GTT into one numerical value and, because it results in four levels of glycemia, represents a new way forwards for GDM diagnosis and can potentially reduce the rates of under diagnosis and over diagnosis of GDM.

Comparison of bias adjustment in meta-analysis using data-based and opinion-based methods.

INTRODUCTION: Several methods exist for bias adjustment of meta-analysis results, but there has been no comprehensive comparison with unadjusted methods. We compare 6 bias-adjustment methods with 2 unadjusted methods to examine how these different methods perform. METHODS: We re-analyzed a meta-analysis that included 10 randomized controlled trials. Two data-based methods (Welton's data-based approach and Doi's quality effects model) and 4 opinion-informed methods (opinion-based approach, opinion-based distributions combined statistically with data-based distributions, numerical opinions informed by data-based distributions, and opinions obtained by selecting areas from data-based distributions) were used to incorporate methodological quality information into the meta-analytical estimates. The results of these 6 methods were compared with 2 unadjusted models: the DerSimonian-Laird random effects model and Doi's inverse variance heterogeneity model. RESULTS: The 4 opinion-based methods returned the random effects model estimates with wider uncertainty. The data-based and quality effects methods returned different results and aligned with the inverse variance heterogeneity method with some minor downward bias adjustment. CONCLUSION: Opinion-based methods seem to only add uncertainty rather than bias adjust.

Correction: Assessing the consistency of iPSC and animal models in cystic fibrosis modelling: A meta-analysis.

[This corrects the article DOI: 10.1371/journal.pone.0272091.].

Tranexamic Acid for Traumatic Injury in the Emergency Setting: A Systematic Review and Bias-Adjusted Meta-Analysis of Randomized Controlled Trials.

STUDY OBJECTIVE: Traumatic injury causes a significant number of deaths due to bleeding. Tranexamic acid (TXA), an antifibrinolytic agent, can reduce bleeding in traumatic injuries and potentially enhance outcomes. Previous reviews suggested potential TXA benefits but did not consider the latest trials. METHODS: A systematic review and bias-adjusted meta-analysis were performed to assess TXA's effectiveness in emergency traumatic injury settings by pooling estimates from randomized controlled trials. Researchers searched Medline, Embase, and Cochrane Central for randomized controlled trials comparing TXA's effects to a placebo in emergency trauma cases. The primary endpoint was 1-month mortality. The methodological quality of the trials underwent assessment using the MASTER scale, and the meta-analysis applied the quality-effects method to adjust for methodological quality. RESULTS: Seven randomized controlled trials met the set criteria. This meta-analysis indicated an 11% decrease in the death risk at 1 month after TXA use (odds ratio [OR] 0.89, 95% confidence interval [CI] 0.84 to 0.95) with a number needed to treat of 61 to avoid 1 additional death. The meta-analysis also revealed reduced 24-hour mortality (OR 0.76, 95% CI 0.65 to 0.88) for TXA. No compelling evidence of increased vascular occlusive events emerged (OR 0.96, 95% CI 0.73 to 1.27). Subgroup analyses highlighted TXA's effectiveness in general trauma versus traumatic brain injury and survival advantages when administered out-of-hospital versus inhospital. CONCLUSIONS: This synthesis demonstrates that TXA use for trauma in emergencies leads to a reduction in 1-month mortality, with no significant evidence of problematic vascular occlusive events. Administering TXA in the out-of-hospital setting is associated with reduced mortality compared to inhospital administration, and less mortality with TXA in systemic trauma is noted compared with traumatic brain injury specifically.

Use of conditional estimates of effect in cancer epidemiology: An application to lung cancer treatment.

BACKGROUND: In oncology clinical trials, there is the assumption that randomization sufficiently balances confounding covariates and therefore average treatment effects are usually reported. This paper explores the wider benefits provided by conditioning on covariates for reasons other than mitigation of confounding. METHODS: We reanalyzed the data from primary randomized controlled trials listed in two meta-analyses to explore the significance of conditioning on smoking status in terms of the effect magnitude of treatment on progression free survival in non-small cell lung cancer. RESULTS: The reanalysis revealed that conditioning on smoking status using sub-group analyses provided the closest empiric estimate of individual treatment effect based on smoking status and significantly reduced the heterogeneity of treatment effect observed across studies. In addition, smoking status was determined to be a modifier of the effect of treatment. CONCLUSION: Conditioning on prognostic covariates in randomized trials in oncology helps generate the closest empiric estimates of individual treatment benefit, addresses heterogeneity due to varying covariate distributions across trials and facilitates future decision making as well as evidence synthesis. Conditioning using sub-group analyses also allows examination for effect modification in meta-analysis.

Pre-prandial plasma liver-expressed antimicrobial peptide 2 (LEAP2) concentration in humans is inversely associated with hunger sensation in a ghrelin independent manner.

PURPOSE: The liver-expressed antimicrobial peptide 2 (LEAP2) is a newly recognized peptide hormone that acts via the growth hormone secretagogue receptor (GHSR) blunting the effects of ghrelin and displaying ghrelin-independent actions. Since the implications of LEAP2 are beginning to be elucidated, we investigated if plasma LEAP2 concentration varies with feeding status or sex and whether it is associated with glucose metabolism and appetite sensations. METHODS: We performed a single test meal study, in which plasma concentrations of LEAP2, ghrelin, insulin and glucose as well as visual analogue scales for hunger, desire to eat, prospective food consumption, fullness were assessed before and 60 min after breakfast in 44 participants (n = 21 females) with normal weight (NW) or overweight/obesity (OW/OB). RESULTS: Pre-prandial plasma LEAP2 concentration was ~ 1.6-fold higher whereas ghrelin was ~ 2.0-fold lower in individuals with OW/OB (p < 0.001) independently of sex. After adjusting for body mass index (BMI) and sex, pre-prandial plasma LEAP2 concentration displayed a direct relationship with BMI (ß: 0.09; 95%CI: 0.05, 0.13; p < 0.001), fat mass (ß: 0.05; 95%CI: 0.01, 0.09; p = 0.010) and glycemia (ß: 0.24; 95%CI: 0.05, 0.43; p = 0.021), whereas plasma ghrelin concentration displayed an inverse relationship with BMI and fat mass but not with glycemia. Postprandial plasma LEAP2 concentration increased ~ 58% in females with OW/OB (p = 0.045) but not in females with NW or in males. Pre-prandial plasma LEAP2 concentration displayed an inverse relationship with hunger score (ß: - 11.16; 95% CI: - 18.52, - 3.79; p = 0.004), in a BMI-, sex- and ghrelin-independent manner. CONCLUSIONS: LEAP2 emerges as a key hormone implicated in the regulation of metabolism and appetite in humans. TRIAL REGISTRATION: The study was retrospectively registered in clinicaltrials.gov (April 2023). CLINICALTRIALS: gov Identifier: NCT05815641.

No Association Between Ct Value and COVID-19 Severity and Mortality in Qatar.

Background: The association between the cycle threshold (Ct) which reflects the SARS-CoV-2 viral load and the severity of COVID-19 is still not clear. We investigated the association between Ct values, symptoms and the risk of ICU admission and mortality from COVID-19 in Qatar. Methods: This case-control study used data of hospitalized individuals with confirmed COVID-19 during the period March to September 2020. Cases were defined as individuals with confirmed COVID-19 who were admitted to the intensive care unit (ICU) or died and controls as those who were not admitted to the ICU. The association between Ct value, symptoms, ICU admission and mortality was investigated using Ct value as a categorical variable (below and above 25) in multivariable regression models and adjusted for relevant confounders. Results: A total of 622 participants with median age 53 (IQR: 53-63), of which 69% were males, were included. There were 236 ICU admissions and 111 deaths. When categorized, Ct value (<25 vs ≥25) had no association with the odds of ICU admission (OR 0.85, 95% CI 0.56 to 1.29) or odds of mortality (OR 1.21, 95% CI 0.71 to 2.08). Respiratory (OR 2.95, 95% CI 1.57 to 5.56) and gastrointestinal symptoms (OR 1.99, 95% CI 1.18 to 3.35) were associated with higher odds of ICU admission. Similarly, respiratory (OR 4.96, 95% CI 1.10 to 22.43) and gastrointestinal symptoms (OR 3.17, 95% CI 1.29 to 7.84) were associated with higher odds of mortality. Conclusion: Although RT-PCR Ct has good diagnostic value, its prognostic value appears to be unreliable. Respiratory and gastrointestinal symptoms are associated with COVID-19 criticality and mortality in this setting.

Development of an evidence-based model for predicting patient, provider, and appointment factors that influence no-shows in a rural healthcare system.

BACKGROUND: No-show appointments pose a significant challenge for healthcare providers, particularly in rural areas. In this study, we developed an evidence-based predictive model for patient no-shows at the Marshfield Clinic Health System (MCHS) rural provider network in Wisconsin, with the aim of improving overbooking approaches in outpatient settings and reducing the negative impact of no-shows in our underserved rural patient populations. METHODS: Retrospective data (2021) were obtained from the MCHS scheduling system, which included 1,260,083 total appointments from 263,464 patients, as well as their demographic, appointment, and insurance information. We used descriptive statistics to associate variables with show or no-show status, logistic regression, and random forests utilized, and eXtreme Gradient Boosting (XGBoost) was chosen to develop the final model, determine cut-offs, and evaluate performance. We also used the model to predict future no-shows for appointments from 2022 and onwards. RESULTS: The no-show rate was 6.0% in both the train and test datasets. The train and test datasets both yielded 5.98. Appointments scheduled further in advance (> 60 days of lead time) had a higher (7.7%) no-show rate. Appointments for patients aged 21-30 had the highest no-show rate (11.8%), and those for patients over 60 years of age had the lowest (2.9%). The model predictions yielded an Area Under Curve (AUC) of 0.84 for the train set and 0.83 for the test set. With the cut-off set to 0.4, the sensitivity was 0.71 and the positive predictive value was 0.18. Model results were used to recommend 1 overbook for every 6 at-risk appointments per provider per day. CONCLUSIONS: Our findings demonstrate the feasibility of developing a predictive model based on administrative data from a predominantly rural healthcare system. Our new model distinguished between show and no-show appointments with high performance, and 1 overbook was advised for every 6 at-risk appointments. This data-driven approach to mitigating the impact of no-shows increases treatment availability in rural areas by overbooking appointment slots on days with an elevated risk of no-shows.

Data extraction error in pharmaceutical versus non-pharmaceutical interventions for evidence synthesis: Study protocol for a crossover trial.

Background: Data extraction is the foundation for research synthesis evidence, while data extraction errors frequently occur in the literature. An interesting phenomenon was observed that data extraction error tend to be more common in trials of pharmaceutical interventions compared to non-pharmaceutical ones. The elucidation of which would have implications for guidelines, practice, and policy. Methods and analyses: We propose a crossover, multicenter, investigator-blinded trial to elucidate the potential variants on the data extraction error rates. Eligible 90 participants would be 2nd year or above post-graduate students (e.g., masters, doctoral program). Participants will be randomized to one of the two groups to complete pre-defined data extraction tasks: 1) group A will contain 10 randomized controlled trials (RCTs) of pharmaceutical interventions; 2) group B will contain 10 RCTs of non-pharmaceutical interventions. Participants who finish the data extraction would then be assigned to the alternative group for another round of data extraction after a 30 min washout period. Finally, those participants assigned to A or B group will be further 1:1 randomly matched based on a random-sequenced number for the double-checking process on the extracted data. The primary outcome will be the data extract error rates of the pharmaceutical intervention group and non-pharmaceutical group, before the double-checking process, in terms of the cell level, study level, and participant level. The secondary outcome will be the data error rates of the pharmaceutical intervention group and non-pharmaceutical group after the double-checking process, again, in terms of the cell level, study level, and participant level. A generalized linear mixed effects model (based on the above three levels) will be used to estimate the potential differences in the error rates, with a log link function for binomial data. Subgroup analyses will account for the experience of individuals on systematic reviews and the time used for the data extraction. Discussion: This trial will provide useful evidence for further systematic review of data extraction practices, improved data extraction strategies, and better guidelines. Trial registration: Chinese Clinical Trial Register Center (Identifier: ChiCTR2200062206).

Clinical Phenotypes of PCOS: a Cross-Sectional Study.

This cross-sectional study examines the Doi-Alshoumer PCOS clinical phenotype classification in relation to measured clinical and biochemical characteristics of women with polycystic ovary syndrome (PCOS). Two cohorts of women (Kuwait and Rotterdam) diagnosed with PCOS (FAI > 4.5%) were examined. These phenotypes were created using neuroendocrine dysfunction (IRMA LH/FSH ratio > 1 or LH > 6 IU/L) and menstrual cycle status (oligo/amenorrhea) to create three phenotypes: (A) neuroendocrine dysfunction and oligo/amenorrhea, (B) without neuroendocrine dysfunction but with oligo/amenorrhea, and (C) without neuroendocrine dysfunction and with regular cycles. These phenotypes were compared in terms of hormonal, biochemical, and anthropometric measures. The three suggested phenotypes (A, B, and C) were shown to be sufficiently distinct in terms of hormonal, biochemical, and anthropometric measures. Patients who were classified as phenotype A had neuroendocrine dysfunction, excess LH (and LH/FSH ratio), irregular cycles, excess A4, infertility, excess T, highest FAI and E2, and excess 17αOHPG when compared to the other phenotypes. Patients classified as phenotype B had irregular cycles, no neuroendocrine dysfunction, obesity, acanthosis nigricans, and insulin resistance. Lastly, patients classified as phenotype C had regular cycles, acne, hirsutism, excess P4, and the highest P4 to E2 molar ratio. The differences across phenotypes suggested distinct phenotypic expression of this syndrome, and the biochemical and clinical correlates of each phenotype are likely to be useful in the management of women with PCOS. These phenotypic criteria are distinct from criteria used for diagnosis.