Random-effects Meta-analysis Methods Research Articles

Meta-analyses of phase I dose-finding studies: Application for the development of protein kinase inhibitors in oncology.

This study aimed to assess the feasibility of applying two recent phase I meta-analyses methods to protein kinase inhibitors (PKIs) developed in oncology and to identify situations where these methods could be both feasible and useful. This ancillary study used data from a systematic review conducted to identify dose-finding studies for PKIs. PKIs selected for meta-analyses were required to have at least five completed dose-finding studies involving cancer patients, with available results, and dose escalation guided by toxicity assessment. To account for heterogeneity caused by various administration schedules, some studies were divided into study parts, considered as separate entities in the meta-analyses. For each PKI, two Bayesian random-effects meta-analysis methods were applied to model the toxicity probability distribution of the recommended dose and to estimate the maximum tolerated dose (MTD). Meta-analyses were performed for 20 PKIs including 96 studies corresponding to 115 study parts. The median posterior probability of toxicity probability was below the toxicity thresholds of 0.20 for 70% of the PKIs, even if the resulting credible intervals were very wide. All approved doses were below the MTD estimated for the minimum toxicity threshold, except for one, for which the approved dose was above the MTD estimated for the maximal threshold. The application of phase I meta-analysis methods has been feasible for the majority of PKI; nevertheless, their implementation requires multiple conditions. However, meta-analyses resulted in estimates with large uncertainty, probably due to limited patient numbers and/or between-study variability. This calls into question the reliability of the recommended doses.

The effectiveness of faculty development programs for training university professors in the health area: a systematic review and meta-analysis

SummaryBackgroundThe growing discussion on teacher development focuses on diversified educational skills that promote knowledge and innovation in the teaching, learning and assessment process. With the Covid-19 scenario, this picture of necessary changes has become more evident, demonstrating the need for professional preparation to work in teacher development. The aim of the study was to analyze the effectiveness of teacher development programs for the training of university teachers in the health area, through a systematic review and meta-analysis.MethodsThe systematic review and meta-analysis were carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and involved searching five databases - PubMed-Medline, Education Resource Information Center (ERIC), SCOPUS, Embase and Web of Science. The review included randomized clinical trials and cohort studies that addressed the effectiveness of teaching professionalization in the health area for university professors. The quality of the selected studies was assessed based on the evaluation criteria of the Joanna Briggs Institute tool. The random effects meta-analysis method was used to explain the distribution of effects between the studies, using Stata® software (version 11.0) and publication bias was examined by visual inspection of the graphs and Egger's test.ResultsWe included 12 studies in the systematic review and 8 in the meta-analysis. These studies were published between 1984 and 2022 in 14 countries. Significant changes were reported in teachers' behavior to stimulate and encourage students, improvement in the quality of teaching and teaching staff, as well as improvement in skills such as leadership and self-evaluation. Furthermore, the result of the meta-analysis showed that there is evidence of the effectiveness of the positive effects of teacher development programs after their implementation, with this effect being 1.70% and an increase of 4.75 in the effect of these teacher development programs.ConclusionOur study shows that development programs have been implemented in different countries and contexts, all of which have proven to be effective in the short, medium and long term. We recommend that future research focus specifically on the different competencies that have been acquired following the implementation of these programs.

Meta-analysis of breast cancer risk for individuals with PALB2 pathogenic variants.

Multigene panel testing now allows efficient testing of many cancer susceptibility genes leading to a larger number of mutation carriers being identified. They need to be counseled about their cancer risk conferred by the specific gene mutation. An important cancer susceptibility gene is PALB2. Multiple studies reported risk estimates for breast cancer (BC) conferred by pathogenic variants in PALB2. Due to the diverse modalities of reported risk estimates (age-specific risk, odds ratio, relative risk, and standardized incidence ratio) and effect sizes, a meta-analysis combining these estimates is necessary to accurately counsel patients with this mutation. However, this is not trivial due to heterogeneity of studies in terms of study design and risk measure. We utilized a recently proposed Bayesian random-effects meta-analysis method that can synthesize estimates from such heterogeneous studies. We applied this method to combine estimates from 12 studies on BC risk for carriers of pathogenic PALB2 mutations. The estimated overall (meta-analysis-based) risk of BC is 12.80% (6.11%-22.59%) by age 50 and 48.47% (36.05%-61.74%) by age 80. Pathogenic mutations in PALB2 makes women more susceptible to BC. Our risk estimates can help clinically manage patients carrying pathogenic variants in PALB2.

Cardiac imaging correlates and predictors of stroke in patients with atrial fibrillation: a meta-analysis.

New nonclinical parameters are needed to improve the current stroke risk stratification schemes for patients with atrial fibrillation. This study aimed to summarize data on potential cardiac imaging correlates and predictors of stroke or systemic embolism in patients with atrial fibrillation. MEDLINE, EMBASE, and Web of Science were searched to identify all published studies providing relevant data through 16 November 2022. Random effects meta-analysis method was used to pool estimates. We included 64 studies reporting data from a pooled population of 56 639 patients. Left atrial spontaneous echo-contrast [adjusted odds ratio (aOR) 3.32, 95% confidence interval (CI) 1.98-5.49], nonchicken wing left atrial appendage (LAA) morphology (aOR 2.15, 95% CI 1.11-4.18), left atrial enlargement (aOR 2.12, 95% CI 1.45-3.08), and higher LAA orifice diameter (aOR 1.56, 95% CI 1.18-2.05) were highly associated with stroke. Other parameters associated with stroke included higher left atrial sphericity (aOR 1.14, 95% CI 1.01-1.29), higher left atrial volume (aOR 1.03, 95% CI 1.01-1.04), higher left atrial volume index (aOR 1.014, 95% CI 1.004-1.023), lower left atrial reservoir strain [adjusted hazard ratio (aHR) 0.86, 95% CI 0.76-0.98], higher left ventricular mass index (aOR 1.010, 95% CI 1.005-1.015) and E / e' ratio (aOR 1.12, 95% CI 1.07-1.16). There was no association between LAA volume (aOR 1.37, 95% CI 0.85-2.21) and stroke. These cardiac imaging parameters identified as potential predictors of thromboembolism may improve the accuracy of stroke risk stratification schemes in patients with atrial fibrillation. Further studies should evaluate the performance of holistic risk scores including clinical factors, biomarkers, and cardiac imaging.

Comparison of statistical methods used to meta-analyse results from interrupted time series studies: an empirical study

BackgroundThe Interrupted Time Series (ITS) is a robust design for evaluating public health and policy interventions or exposures when randomisation may be infeasible. Several statistical methods are available for the analysis and meta-analysis of ITS studies. We sought to empirically compare available methods when applied to real-world ITS data.MethodsWe sourced ITS data from published meta-analyses to create an online data repository. Each dataset was re-analysed using two ITS estimation methods. The level- and slope-change effect estimates (and standard errors) were calculated and combined using fixed-effect and four random-effects meta-analysis methods. We examined differences in meta-analytic level- and slope-change estimates, their 95% confidence intervals, p-values, and estimates of heterogeneity across the statistical methods.ResultsOf 40 eligible meta-analyses, data from 17 meta-analyses including 282 ITS studies were obtained (predominantly investigating the effects of public health interruptions (88%)) and analysed. We found that on average, the meta-analytic effect estimates, their standard errors and between-study variances were not sensitive to meta-analysis method choice, irrespective of the ITS analysis method. However, across ITS analysis methods, for any given meta-analysis, there could be small to moderate differences in meta-analytic effect estimates, and important differences in the meta-analytic standard errors. Furthermore, the confidence interval widths and p-values for the meta-analytic effect estimates varied depending on the choice of confidence interval method and ITS analysis method.ConclusionsOur empirical study showed that meta-analysis effect estimates, their standard errors, confidence interval widths and p-values can be affected by statistical method choice. These differences may importantly impact interpretations and conclusions of a meta-analysis and suggest that the statistical methods are not interchangeable in practice.

Evaluation of statistical methods used to meta-analyse results from interrupted time series studies: A simulation study.

Interrupted time series (ITS) are often meta-analysed to inform public health and policy decisions but examination of the statistical methods for ITS analysis and meta-analysis in this context is limited. We simulated meta-analyses of ITS studies with continuous outcome data, analysed the studies using segmented linear regression with two estimation methods [ordinary least squares (OLS) and restricted maximum likelihood (REML)], and meta-analysed the immediate level- and slope-change effect estimates using fixed-effect and (multiple) random-effects meta-analysis methods. Simulation design parameters included varying series length; magnitude of lag-1 autocorrelation; magnitude of level- and slope-changes; number of included studies; and, effect size heterogeneity. All meta-analysis methods yielded unbiased estimates of the interruption effects. All random effects meta-analysis methods yielded coverage close to the nominal level, irrespective of the ITS analysis method used and other design parameters. However, heterogeneity was frequently overestimated in scenarios where the ITS study standard errors were underestimated, which occurred for short series or when the ITS analysis method did not appropriately account for autocorrelation. The performance of meta-analysis methods depends on the design and analysis of the included ITS studies. Although all random effects methods performed well in terms of coverage, irrespective of the ITS analysis method, we recommend the use of effect estimates calculated from ITS methods that adjust for autocorrelation when possible. Doing so will likely to lead to more accurate estimates of the heterogeneity variance.

Risk factors for admission and length of stay in hospital for children with and without congenital anomalies: A EUROlinkCAT study

ObjectiveTo explore risk factors for hospital admission, and length of stay (LOS) in hospital, among children with congenital anomalies (CAs) and reference children without CAs in Europe.
 MethodA European population-based data-linkage cohort study was conducted including children with CAs (born 1995-2014) registered in seven EUROCAT CA registries and children without CAs (reference children) living in the same geographical areas. Data on hospitalisation and LOS (1995-2015) for all children aged <1 year and 1-4 years were obtained by linkage to hospital discharge databases.
 The effects of birth cohort, sex, gestational age, maternal age, multiple birth and socioeconomic status on risk of admission and LOS were estimated using Cox’s Proportional Hazards and negative binomial regression models. Random effects meta-analysis and quantile estimation methods were used to pool the estimates.
 ResultsA total of 79,036 children with CAs and 2,016,042 reference children were linked to hospital records. Children with CAs born pre-term (<32 weeks) were more than twice as likely to be admitted (adj. HR 2.35, 95% CI 1.45-3.80) and had almost 8 times longer stays (adj. IRR 7.95, 95% CI 6.12-10.33) compared to children with CAs born at term. Reference children were almost six times as likely to be admitted (adj. HR 5.87, 95% CI 3.10-11.09), and had almost 50 times longer stays (adj. IRR 49.49, 30.92-79.21) compared to reference children born at term. Children with CAs and reference children born preterm were also at increased risk of admission at 1-4 years of age, although the effect was less than for children aged <1 year.
 ConclusionThe impact of risk factors for admission to hospital and LOS were similar between children with CAs and reference children but the impact was often greater in reference children. This study highlights the value of linking to hospital discharge records to obtain population-based information on morbidity for counselling parents.

The effectiveness of mobile application for monitoring diabetes mellitus and hypertension in the adult and elderly population: systematic review and meta-analysis

ContextArterial Hypertension (AH) and Diabetes Mellitus (DM) are diseases that are getting worse all over the world. Linked to this advance, is the growing digital health market with numerous mobile health applications, which aim to help patients and professionals in the proper management of chronic diseases. The aim of this study was to analyze, through a systematic review and meta-analysis, the effectiveness of using mobile health applications in monitoring AH and/or DM in the adult and elderly population.MethodsThe systematic review and meta-analysis was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Metanalyses guidelines and involved searching five databases – Medline/PubMed, Embase, CINAHL, Virtual Library in Health and Cochrane Library. The review included randomized and cohort clinical trials testing the effects of the intervention on changing biochemical parameters and clinical efficacy in people treated for AH and/or DM. The quality of the selected studies was assessed based on the evaluation criteria of the Joanna Briggs Institute tool. The random effects meta-analysis method was used to explain effect distribution between studies, by Stata® software (version 11.0) and publication bias was examined by visual inspection of graphs and Egger test.ResultsWe included 26 studies in the systematic review and 17 in the meta-analysis. These studies were published between 2014 to 2022 in 14 countries. Were reported improvement in knowledge and self-management of AH and DM, social motivation with treatment and behavioral change, reduction in glycated hemoglobin values, fasting glucose and blood pressure, improvement in adherence to drug treatment, among others. The result of the meta-analysis showed that there is evidence that the use of mobile applications can help reduce glycated hemoglobin by 0.39% compared to the usual care group.ConclusionsMonitoring and self-monitoring of behaviors and health care related to AH and DM in adults and the elderly through mobile applications, has clinically significant effectiveness in reducing glycated hemoglobin levels. Future studies should provide more evidence and recommendations for best practices and development of digital health interventions.Trial registrationPROSPERO. International Prospective Registry of Systematic Reviews. CRD42022361928.

Prevalence and predictors of data and code sharing in the medical and health sciences: systematic review with meta-analysis of individual participant data

ObjectivesTo synthesise research investigating data and code sharing in medicine and health to establish an accurate representation of the prevalence of sharing, how this frequency has changed over time, and...

Ultra-Processed Food Intake Is Associated with Non-Alcoholic Fatty Liver Disease in Adults: A Systematic Review and Meta-Analysis.

Non-alcoholic fatty liver disease (NAFLD) is associated with overweight/obesity, metabolic syndrome and type 2 diabetes (T2D) due to chronic caloric excess and physical inactivity. Previous meta-analyses have confirmed associations between ultra-processed food (UPF) intake and obesity and T2D. We aim to ascertain the contribution of UPF consumption to the risk of developing NAFLD. We performed a systematic review and meta-analysis (PROSPERO (CRD42022368763)). All records registered on Ovid Medline and Web of Science were searched from inception until December 2022. Studies that assessed UPF consumption in adults, determined according to the NOVA food classification system, and that reported NAFLD determined by surrogate (steatosis) scores, imaging or liver biopsy were included. The association between UPF consumption and NAFLD was assessed using random-effects meta-analysis methods. Study quality was assessed, and evidence credibility evaluated, using the Newcastle Ottawa Scale and NutriGrade systems, respectively. A total of 5454 records were screened, and 112 records underwent full text review. From these, 9 studies (3 cross-sectional, 3 case-control and 3 cohort), analysing 60,961 individuals, were included in the current review. Both moderate (vs. low) (pooled relative risk 1.03 (1.00-1.07) (p = 0.04) (I2 = 0%)) and high (vs. low) (1.42 (1.16-1.75) (<0.01) (I2 = 89%)) intake of UPF significantly increased the risk of NAFLD. Funnel plots demonstrate low risk of publication bias. Consumption of UPF is associated with NAFLD with a dose-response effect. Public health measures to reduce overconsumption of UPF are imperative to reduce the burden of NAFLD, and the related conditions, obesity and T2D.

Bariatric Surgery Before Spine Surgery is Associated With Fewer Postsurgical Complications: A Systematic Review and Meta-Analysis.

Systematic review and meta-analysis. To perform a systematic review and meta-analysis investigating the rate of adverse events after spine surgery in patients who underwent bariatric surgery (BS). Obesity is an established risk factor for postoperative complications after spine surgery. BS has been associated with improvements in health in patients with severe obesity. However, it is not known whether undergoing BS before spine surgery is associated with reduced adverse outcomes. PubMed, EMBASE, Scopus, and Web-of-Science were systematically searched according to "Preferred Reporting Items for Systematic Reviews and Meta-Analyses" guidelines. The search included indexed terms and text words from database inception to the date of the search (May 27, 2022). Data and estimates were pooled using the Mantel-Haenszel method for random-effects meta-analysis. Risk of bias was assessed using the Joanna Briggs Institute risk of bias tool. The primary outcome was an all-cause complication rate after surgery. Relative risks for surgical and medical complications were assessed. A total of 4 studies comprising 177,273 patients were included. The pooled analysis demonstrated that the all-cause medical complication rate after spine surgery was lower in patients undergoing BS (relative risk: 0.54, 95% CI: 0.39, 0.74, P < 0.01). There was no difference in rates of surgical complications and 30-day hospital readmission rates between the cohort undergoing BS before spine surgery and the cohort that did not. These analyses suggest that obese patients undergoing BS before spine surgery have significantly lower adverse event rates. Future prospective studies are needed to corroborate these findings. 4.

Efficacy of clozapine compared with other second-generation antipsychotic drugs in patients with treatment-resistant schizophrenia: protocol for a systematic review and individual patient data meta-analysis of randomised controlled trials

IntroductionGuidelines recommend clozapine for treatment-resistant schizophrenia. However, meta-analysis of aggregate data (AD) did not demonstrate higher efficacy of clozapine compared with other second-generation antipsychotics but found substantial heterogeneity between trials...

BAYESIAN HIERARCHICAL RANDOM-EFFECTS META-ANALYSIS AND DESIGN OF PHASE I CLINICAL TRIALS.

We propose a curve-free random-effects meta-analysis approach to combining data from multiple phase I clinical trials to identify an optimal dose. Our method accounts for between-study heterogeneity that may stem from different study designs, patient populations, or tumor types. We also develop a meta-analytic-predictive (MAP) method based on a power prior that incorporates data from multiple historical studies into the design and conduct of a new phase I trial. Performances of the proposed methods for data analysis and trial design are evaluated by extensive simulation studies. The proposed random-effects meta-analysis method provides more reliable dose selection than comparators that rely on parametric assumptions. The MAP-based dose-finding designs are generally more efficient than those that do not borrow information, especially when the current and historical studies are similar. The proposed methodologies are illustrated by a meta-analysis of five historical phase I studies of Sorafenib, and design of a new phase I trial.

Absolute treatment effects for the primary outcome and all-cause mortality in the cardiovascular outcome trials in type 2 diabetes: a meta-analysis of individual patient data

Abstract Background and purpose Treatment effects from the large cardiovascular outcome trials (CVOTs) in diabetes are almost exclusively communicated as hazard ratios, although reporting guidelines recommend to report treatment effects also on an absolute scale, e.g. as numbers needed to treat (NNT). We aimed to analyze NNT in CVOTs of novel oral antidiabetic drugs comparing dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, or sodium-glucose cotransporter-2 (SGLT2) inhibitors to placebo. Methods and results We extracted individual time-to-event information for the primary outcome and all-cause mortality from 19 CVOTs that compared DPP-4 inhibitors, GLP-1 receptor agonists, or SGLT2 inhibitors to placebo. We estimated Weibull models for each trial and outcome and derived monthly NNTs. NNTs were summarized across all trials and within drug-classes by random effects meta-analysis methods. Treatment effects in the CVOTs appear smaller if they are reported as NNTs: Overall, 60 (95%-CI: 40–124) patients have to be treated for 29 months (the median follow-up time across all trials) to avoid a single event of the primary outcome, and 101 (95%-CI: 69–191) patients have to be treated for 39 months to avoid a single death. Conclusion We found that the respective treatment effects of novel oral antidiabetic drugs look less impressive when communicated on an absolute scale, as numbers needed to treat. For a valid overall picture of the benefit of these drugs, trial authors should thus also report treatment effects on an absolute scale. Funding Acknowledgement Type of funding sources: None.

The Association Between Smartphone App-Based Self-monitoring of Hypertension-Related Behaviors and Reductions in High Blood Pressure: Systematic Review and Meta-analysis.

BackgroundSelf-monitoring of behavior can support lifestyle modifications; however, we do not know whether such interventions are effective in supporting positive changes in hypertension-related health behaviors and thus in reducing blood pressure in patients treated for hypertension.ObjectiveThis systematic literature review evaluates the extent to which smartphone app–based self-monitoring of health behavior supports reductions in blood pressure and changes in hypertension-related behaviors. It also explores the behavioral components that might explain intervention effectiveness.MethodsA systematic search of 7 databases was conducted in August 2021. Article screening, study and intervention coding, and data extraction were completed independently by reviewers. The search strategy was developed using keywords from previous reviews and relevant literature. Trials involving adults, published after the year 2000, and in the English language were considered for inclusion. The random-effects meta-analysis method was used to account for the distribution of the effect across the studies.ResultsWe identified 4638 articles, of which 227 were included for full-text screening. A total of 15 randomized controlled trials were included in the review. In total, 7415 patients with hypertension were included in the meta-analysis. The results indicate that app-based behavioral self-monitoring interventions had a small but significant effect in reducing systolic blood pressure (SBP), on average, by 1.64 mmHg (95% CI 2.73-0.55, n=7301; odds ratio [OR] 1.60, 95% CI 0.74-3.42, n=114) and in improving changes in medication adherence behavior (standardized mean difference [SMD] 0.78, 95% CI 0.22-1.34) compared to usual care or minimal intervention. The review found the intervention had a small effect on supporting improvements in healthy diet by changing habits related to high sodium food (SMD –0.44, 95% CI –0.79 to –0.08) and a trend, although insignificant, toward supporting smoking cessation, low alcohol consumption, and better physical activity behaviors. A subgroup analysis found that behavioral self-monitoring interventions combined with tailored advice resulted in higher and significant changes in both SBP and diastolic blood pressure (DBP) in comparison to those not providing tailored advice (SBP: –2.92 mmHg, 95% CI –3.94 to –1.90, n=3102 vs –0.72 mmHg, 95% CI –1.67 to 0.23, n=4199, χ2=9.65, P=.002; DBP: –2.05 mmHg, 95% CI –3.10 to –1.01, n=968 vs 1.54 mmHg, 95% CI –0.53 to 3.61, n=400, χ2=9.19, P=.002).ConclusionsSelf-monitoring of hypertension-related behaviors via smartphone apps combined with tailored advice has a modest but potentially clinically significant effect on blood pressure reduction. Future studies could use rigorous methods to explore its effects on supporting changes in both blood pressure and hypertension-related health behaviors to inform recommendations for policy making and service provision.Trial RegistrationPROSPERO CRD42019136158; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=136158

Frequency and prognostic significance of atrial fibrillation in acute pulmonary embolism: A pooled analysis

ObjectiveTo summarize data on the prevalence/incidence, risk factors and prognosis of atrial fibrillation (AF) in patients with acute pulmonary embolism (aPE). MethodsMEDLINE, Embase, and Web of Science were searched to identify all published studies providing relevant data through December 12, 2021. Random-effects meta-analysis method was used to pool estimates. ResultsWe included 27 studies reporting data from a pooled population of 819,380 patients. The prevalence rates were 11.3% for pre-existing AF, 4.7% for newly diagnosed AF, and 13.2% for prevalent (total) AF. Predictors of newly diagnosed AF (from one study) included congestive heart failure (adjusted odds ratio [aOR] 3.33, 95% CI: 1.81–6.12), ischemic heart disease (aOR 3.25, 95% CI: 1.65–6.39), massive PE (aOR 2.67, 95% CI: 1.19–5.99). Overall, AF was associated with increased risk of short-term (aOR 1.54, 95% CI: 1.44–1.64) and long-term mortality (aOR 1.58, 95% CI: 1.26–1.97). In subgroup analyses, all types of AF were associated with increased risk of short-term mortality: pre-existing AF (aOR 1.90, 95% CI: 1.59–2.27), newly diagnosed AF (aOR 1.51, 95% CI: 1.18–1.93), and prevalent AF (aOR 1.50, 95% CI: 1.42–1.60). Pre-existing AF (aOR 2.08, 95% CI: 1.27–3.42) and prevalent AF (aOR 1.29, 95% CI: 1.02–1.63) were also associated with higher long-term mortality. ConclusionAF is present in about one in eight patients with aPE, and is associated with increased short- and long-term mortality. AF might improve risk stratification in patients with aPE.

Effect of preoperative renin-angiotensin system blockade on vasoplegia after cardiac surgery: A systematic review with meta-analysis.

BACKGROUNDVasoplegia is a common complication of cardiac surgery but its causal relationship with preoperative use of renin angiotensin system (RAS) blockers [angiotensin converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARB)] is still debated.AIMTo update and summarize data on the effect of preoperative use of RAS blockers on incident vasoplegia.METHODSAll published studies from MEDLINE, EMBASE, and Web of Science providing relevant data through January 13, 2021 were identified. A random-effects meta-analysis method was used to pool estimates, and post-cardiac surgery shock was differentiated from vasoplegia.RESULTSTen studies reporting on a pooled population of 15672 patients (none looking at ARBs exclusively) were included in the meta-analysis. All were case-control studies. Use of ACEIs was associated with an increased risk of vasoplegia [pooled adjusted odds ratio (Aor) of 2.06, 95%CI: 1.45-2.93] and increased inotropic/vasopressor support requirement (pooled aOR 1.19, 95%CI: 1.10-1.29). Post-cardiac surgery shock was increased in the presence of left ventricular dysfunction (pooled aOR 2.32, 95%CI: 1.60-3.36; I2 49%) but not increased by the use of beta blockers (pooled aOR 0.78, 95%CI: 0.36-1.69; I2 77%). Two randomized control trials (RCTs), not eligible for the meta-analysis, did not show an association between continuation of RAS blockers and vasoplegia. CONCLUSIONPreoperative continuation of ACEIs is associated with an increased need for inotropic support postoperatively and with an increased risk of vasoplegia in observational studies but not in RCTs. The absence of a consensus definition of vasoplegia should lead to the use of perioperative cardiovascular monitoring when designing RCTs to better understand this discrepancy.

SYSTEMATIC REVIEW: EVALUATION OF CYTOKINE STORM TREATMENT FROM COVID 19 PATIENT BASE ON CLINICAL TRIAL

These cytokine storms are extremely dangerous. Cytokine storm is considered the reason for the high mortality rate of COVID 19 patients. An undetected reason causes a Cytokine storm in a patient, but this is associated with the characteristics of a person's immune system. Most COVID-19 patients recover with mild and moderate symptoms within one week; some develop severe pneumonia in the second week, followed by cytokine storm, ARDS, multiorgan failure, and disseminated intravascular coagulation (DIC) within the 3rd week. The high mortality rate in COVID-19 patients is most likely due to a cytokine storm in the patient's body. Cytokines are also immune system proteins that regulate interactions between cells and trigger immune reactivity, both in innate and adaptive immunity. To evaluate all treatments that can be used during the treatment of cytokine storm in COVID-19 patients based on clinical trials. Systematic Literature Review (SLR) about studies researching the treatment of cytokine strom in COVID-19 patients. Accumulated treatment was calculated using the confidence ratio for the random effects meta-analysis method of medium and high-quality data.&#x0D; Based on the literature on clinical trials, we can use Tocilizumab, Ruxolitinib. Baricitinib, Itolizumab, Zilucoplan, Stem Cells (MSC transplantation, Umbilical cord mesenchymal stromal cells, and Placenta‐derived decidua stromal cells), Anakinra, Beta-glucans (AFO-202 and N-163 of a black yeast Aureobasidium pullulans), LDRT (Low-dose radiation therapy), ALS (Artificial-liver blood-purification system), and CP (Convalescent plasma) medication for treating COVID-19 patient with cytokine storm syndrome. The use of each treatment has its advantages and disadvantages. However. All of the above therapies have shown effectiveness in treating cytokine storms in clinical trials.

A Bayesian hierarchical model for individual participant data meta-analysis of demand curves.

Individual participant data meta-analysis is a frequently used method to combine and contrast data from multiple independent studies. Bayesian hierarchical models are increasingly used to appropriately take into account potential heterogeneity between studies. In this paper, we propose a Bayesian hierarchical model for individual participant data generated from the Cigarette Purchase Task (CPT). Data from the CPT details how demand for cigarettes varies as a function of price, which is usually described as an exponential demand curve. As opposed to the conventional random-effects meta-analysis methods, Bayesian hierarchical models are able to estimate both the study-specific and population-level parameters simultaneously without relying on the normality assumptions. We applied the proposed model to a meta-analysis with baseline CPT data from six studies and compared the results from the proposed model and a two-step conventional random-effects meta-analysis approach. We conducted extensive simulation studies to investigate the performance of the proposed approach and discussed the benefits of using the Bayesian hierarchical model for individual participant data meta-analysis of demand curves.

Atrial fibrillation incidence, prevalence, predictors, and adverse outcomes in acute coronary syndromes: A pooled analysis of data from 8 million patients.

To summarize data on the prevalence/incidence, risk factors and prognosis of atrial fibrillation (AF) in patients with acute coronary syndromes (ACS). MEDLINE, Embase, and Web of Science were searched to identify all published studies providing relevant data through August 23, 2020. Random-effects meta-analysis method was used to pool estimates. We included 109 studies reporting data from a pooled population of 8 239 364 patients. The prevalence rates were 5.8% for pre-existing AF, 7.3% for newly diagnosed AF, and 11.3% for prevalent (total) AF, in patients with ACS. Predictors of newly diagnosed AF included age (per year increase) (adjusted odds ratio [aOR]: 1.05), C-reactive protein (aOR: 1.49), left atrial (LA) diameter (aOR: 1.08), LA dilatation (aOR:2.32), left ventricular ejection fraction <40% (aOR: 1.82), hypertension (aOR: 1.87), and Killip ˃ 1 (aOR: 1.85), p < .01 in all analyzes. Newly diagnosed AF was associated with an increased risk of acute heart failure (adjusted hazard ratio [aHR]:3.20), acute kidney injury (aHR: 3.09), re-infarction (aHR: 1.96), stroke (aHR: 2.15), major bleeding (aHR: 2.93), and mortality (aHR: 1.80) in the short term; and with an increased risk of heart failure (aHR: 2.21), stroke (aHR: 1.75), mortality (aHR: 1.67), CV mortality (aHR: 2.09), sudden cardiac death (aHR: 1.53), and a composite of major adverse cardiovascular events (aHR: 1.54) in the long term (beyond 1 month), p < .05 in all analyzes. One in nine patients with ACS has AF, with a high proportion of newly diagnosed AF. AF, in particular newly diagnosed AF, is associated with poor short-term and long-term outcomes in patients with ACS.