Does High-Velocity Resistance Exercise Elicit Greater Physical Function Benefits Than Traditional Resistance Exercise in Older Adults? A Systematic Review and Network Meta-Analysis of 79 Trials

BACKGROUND

A systematic review and network meta-analysis was undertaken to examine the effectiveness of different modes of resistance exercise velocity in fast walking speed, timed-up and go, 5-times sit-to-stand, 30-second sit-to-stand, and 6-minute walking tests in older adults.

METHODS

CINAHL, Embase, LILACS, PubMed, Scielo, SPORTDiscus, and Web of Science databases were searched up to February 2022. Eligible randomized trials examined the effects of supervised high-velocity or traditional resistance exercise in older adults (ie, ≥60 years). The primary outcome for this review was physical function measured by fast walking speed, timed-up and go, 5-times sit-to-stand, 30-second sit-to-stand, and 6-minute walking tests, while maximal muscle power and muscle strength were secondary. A random-effects network meta-analysis was undertaken to examine the effects of different resistance exercise interventions.

RESULTS

Eighty articles describing 79 trials (n = 3 575) were included. High-velocity resistance exercise was the most effective for improving fast walking speed (standardized mean difference [SMD] -0.44, 95% confidence interval [CI]: 0.00 to 0.87), timed-up and go (SMD -0.76, 95% CI: -1.05 to -0.47), and 5-times sit-to-stand (SMD -0.74, 95% CI: -1.20 to -0.27), while traditional resistance exercise was the most effective for 30-second sit-to-stand (SMD 1.01, 95% CI: 0.68 to 1.34) and 6-minute walking (SMD 0.68, 95% CI: 0.34 to 1.03).

CONCLUSION

Our study provides evidence that resistance exercise velocity effects are specific in older adults, as evidenced by physical function test dependence. We suggest that prescriptions based on the velocity of contraction should be individualized to address the specific functional needs of participants.

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.

Effects of dairy intake on markers of cardio-metabolic health in adults - a systematic review with network meta-analysis

Health effects of dairy products are still a matter of scientific debate due to inconsistent findings across trials. Therefore, this systematic review and network meta-analysis (NMA) aimed to compare the effects of different dairy products on markers of cardio-metabolic health. A systematic search was conducted in three electronic databases (MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science, search date: 23/09/2022). Randomized controlled trials (RCTs) were included with ≥12 week intervention comparing any two of the eligible interventions (e.g., high dairy [≥3 servings/day or equal amount in g/day], full-fat dairy, low-fat dairy, naturally fermented milk products, low dairy/control [0-2 servings/day or usual diet]). We performed pairwise meta-analysis and NMA using random-effects model in the frequentist framework for ten outcomes (body weight, BMI, fat mass, waist circumference, LDL-cholesterol, HDL-cholesterol, triglycerides, fasting glucose, glycosylated hemoglobin, systolic blood pressure). Continuous outcome data were pooled using mean difference (MD) and dairy interventions were ranked using the surface under the cumulative ranking curve (SUCRA). Nineteen RCTs with 1427 participants were included. High dairy intake (irrespective of fat content) showed no detrimental effects on anthropometric outcomes, blood lipids, and blood pressure. Both low-fat and full-fat dairy improved systolic blood pressure (MD: -5.22 to -7.60 mmHg; low certainty), but, concomitantly, may impair glycemic control (fasting glucose [MD: 0.31 to 0.43 mmol/L] and glycosylated hemoglobin [MD: 0.37 to 0.47%]). Full-fat dairy may increase HDL-cholesterol compared to a control diet (MD: 0.26 mmol/L, 95% confidence interval (CI): 0.03, 0.49). Yogurt improved waist circumference (MD: -3.47 cm, 95%CI: -6.92, -0.02; low certainty), triglycerides (-0.38 mmol/L, 95%CI: -0.73, -0.03; low certainty), and HDL-cholesterol (MD: 0.19 mmol/L, 95%CI: 0.00, 0.38) compared to milk. In conclusion, our findings indicate that there is little robust evidence that a higher dairy intake has detrimental effects on markers of cardio-metabolic health. (PROSPERO: CRD42022303198).

Empirical evidence of study design biases in nutrition randomised controlled trials: a meta-epidemiological study

BACKGROUND

Instruments to critically appraise randomised controlled trials (RCTs) are based on evidence from meta-epidemiological studies. We aim to conduct a meta-epidemiological study on the average bias associated with reported methodological trial characteristics such as random sequence generation, allocation concealment, blinding, incomplete outcome data, selective reporting, and compliance of RCTs in nutrition research.

METHODS

We searched the Cochrane Database of Systematic Reviews, for systematic reviews of RCTs, published between 01 January 2010 and 31 December 2019. We combined the estimates of the average bias (e.g. ratio of risk ratios [RRR] or differences in standardised mean differences) in meta-analyses using the random-effects model. Subgroup analyses were conducted to investigate the potential differences among the RCTs with low versus high/unclear risk of bias with respect to the different types of interventions (e.g. micronutrients, fatty acids, dietary approach), outcomes (e.g. mortality, pregnancy outcomes), and type of outcome (objective, subjective). Heterogeneity was assessed through I² and τ², and prediction intervals were calculated.

RESULTS

We included 27 Cochrane nutrition reviews with 77 meta-analyses (n = 927 RCTs). The available evidence suggests that intervention effect estimates may not be exaggerated in RCTs with high/unclear risk of bias (versus low) judgement for sequence generation (RRR 0.97, 95% CI 0.93 to 1.02; I² = 28%; τ² = 0.002), allocation concealment (RRR 1.00, 95% CI 0.96 to 1.04; I² = 27%; τ² = 0.001), blinding of participants and personnel (RRR 0.95, 95% CI 0.91 to 1.00; I² = 23%; τ² = 0), selective reporting (RRR 0.97, 95% CI 0.92 to 1.02; I² = 24%; τ² = 0), and compliance (RRR 0.95, 95% CI 0.89 to 1.02; I² = 0%; τ² = 0). Intervention effect estimates seemed to be exaggerated in RCTs with a high/unclear risk of bias judgement for blinding of outcome assessment (RRR 0.81, 95% CI 0.70 to 0.94; I² = 26%; τ² = 0.03), which was predominately driven by subjective outcomes, and incomplete outcome data (RRR 0.92, 95% CI 0.88 to 0.97; I² = 22%; τ² = 0.001). For continuous outcomes, no differences were observed, except for selective reporting.

CONCLUSIONS

On average, most characteristics of nutrition RCTs may not exaggerate intervention effect estimates, but the average bias appears to be greatest in trials of subjective outcomes. Replication of this study is suggested in this field to keep this conclusion updated.

Evaluating agreement between bodies of evidence from randomized controlled trials and cohort studies in medical research: a meta-epidemiological study

BACKGROUND

Randomized controlled trials (RCTs) and cohort studies are the most common study design types used to assess the treatment effects of medical interventions. To evaluate the agreement of effect estimates between bodies of evidence (BoE) from randomized controlled trials (RCTs) and cohort studies and to identify factors associated with disagreement.

METHODS

Systematic reviews were published in the 13 medical journals with the highest impact factor identified through a MEDLINE search. BoE-pairs from RCTs and cohort studies with the same medical research question were included. We rated the similarity of PI/ECO (Population, Intervention/Exposure, Comparison, Outcome) between BoE from RCTs and cohort studies. The agreement of effect estimates across BoE was analyzed by pooling ratio of ratios (RoR) for binary outcomes and difference of mean differences for continuous outcomes. We performed subgroup analyses to explore factors associated with disagreements.

RESULTS

One hundred twenty-nine BoE pairs from 64 systematic reviews were included. PI/ECO-similarity degree was moderate: two BoE pairs were rated as "more or less identical"; 90 were rated as "similar but not identical" and 37 as only "broadly similar". For binary outcomes, the pooled RoR was 1.04 (95% CI 0.97-1.11) with considerable statistical heterogeneity. For continuous outcomes, differences were small. In subgroup analyses, degree of PI/ECO-similarity, type of intervention, and type of outcome, the pooled RoR indicated that on average, differences between both BoE were small. Subgroup analysis by degree of PI/ECO-similarity revealed high statistical heterogeneity and wide prediction intervals across PI/ECO-dissimilar BoE pairs.

CONCLUSIONS

On average, the pooled effect estimates between RCTs and cohort studies did not differ. Statistical heterogeneity and wide prediction intervals were mainly driven by PI/ECO-dissimilarities (i.e., clinical heterogeneity) and cohort studies. The potential influence of risk of bias and certainty of the evidence on differences of effect estimates between RCTs and cohort studies needs to be explored in upcoming meta-epidemiological studies.

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.

A review of methods for addressing components of interventions in meta-analysis

Many healthcare interventions are complex, consisting of multiple, possibly interacting, components. Several methodological articles addressing complex interventions in the meta-analytical context have been published. We hereby provide an overview of methods used to evaluate the effects of complex interventions with meta-analytical models. We summarized the methodology, highlighted new developments, and described the benefits, drawbacks, and potential challenges of each identified method. We expect meta-analytical methods focusing on components of several multicomponent interventions to become increasingly popular due to recently developed, easy-to-use, software tools that can be used to conduct the relevant analyses. The different meta-analytical methods are illustrated through two examples comparing psychotherapies for panic disorder.

The Effect of Transfusion of Two Units of Fresh Frozen Plasma on the Perioperative Fibrinogen Levels and the Outcome of Patients Undergoing Elective Endovascular Repair for Abdominal Aortic Aneurysm

BACKGROUND

We aimed to investigate whether the transfusion of 2 units of fresh frozen plasma (FFP) immediately post aneurysm exclusion has any effect on the perioperative fibrinogen levels and the outcome of patients undergoing elective endovascular repair (EVAR) of abdominal aortic aneurysm (AAA).

METHODS

Consecutive infrarenal AAA patients undergoing elective EVAR with the bifurcated Endurant-II stent-graft (Medtronic) were recruited from 2 vascular units. The first unit has a routine policy of administering 2 units of FFP immediately upon aneurysm exclusion (FFP group), whereas the second unit has no such policy (control group). Serum fibrinogen levels were measured on admission and 24 hr post-EVAR and the perioperative change in fibrinogen (Δfib) was calculated (24-hr postoperative minus preoperative fibrinogen). The 2 groups were compared with regards to the perioperative fibrinogen levels (preoperative, 24-hr postoperative, and Δfib) and the outcome (endoleaks, reinterventions, major adverse cardiovascular events, death) during follow up.

RESULTS

A total of 70 patients (41 in the FFP group, 29 controls) were examined. There were 68 men, the mean age was 70 ± 7 years and the maximum AAA diameter was 63.3 ± 13.8 mm. During the follow up (34 ± 19 months), a total of 6 endoleaks were recorded (2 type Ia, 2 type Ib and 1 type II). Mean preoperative fibrinogen, 24-hr postoperative fibrinogen and Δfib was 391.1 ± 92.8 mg/dL, 367.7 ± 97.8 mg/dL and -23.5 ± 51.02 mg/dL, respectively. There was a trend for the fibrinogen to fall 24 hr postprocedure, but this was not statistically significant (P = 0.07). There was a weak negative association between Δfib and endoleaks (P = 0.007, r = -0.29). Compared to controls, the FFP group had a higher 24-hr postoperative fibrinogen (401.8 ± 112.9 mg/dL vs. 319.3 ± 34.9 mg/dL, P < 0.0001) and a lower Δfib (-3.00 ± 56.01 mg/dL vs. -52.48 ± 21.15 mg/dL, P < 0.0001). No significant difference was observed between the 2 groups with regards to endoleaks, reinterventions, major adverse cardiovascular events, or deaths.

CONCLUSIONS

Transfusion of 2 units of FFP postaneurysm exclusion prevents a significant drop in plasma fibrinogen 24 hr post-EVAR, but the impact on clinical outcome has yet to be defined.

High-Energy Neutrinos from Blazar Flares and Implications of TXS 0506+056

Motivated by the observation of a > 290 TeV muon neutrino by IceCube, coincident with a ~6 month-long γ-ray flare of the blazar TXS 0506+056, and an archival search which revealed 13 ± 5 further, lower-energy neutrinos in the direction of the source in 2014-2015 we discuss the likely contribution of blazars to the diffuse high-energy neutrino intensity, the implications for neutrino emission from TXS 0506+056 based on multi-wavelength observations of the source, and a multi-zone model that allows for sufficient neutrino emission so as to reconcile the multi-wavelength cascade constraints with the neutrino emission seen by IceCube in the direction of TXS 0506+056.

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.

Association between fractional flow reserve, instantaneous wave-free ratio and dobutamine stress echocardiography in patients with stable coronary artery disease

AIMS

The association between fractional flow reserve (FFR) and dobutamine stress echocardiography (DSE) in real-world stable angina patients is scant and controversial whereas no such comparison exists with instantaneous wave-free ratio (iFR). The current retrospective study aimed to investigate the associations among these modalities in patients with stable coronary artery disease (CAD) and intermediate coronary lesions.

METHODS AND RESULTS

We studied 62 consecutive stable angina patients who underwent DSE and subsequently coronary angiography with FFR (in all 62) and iFR (in 46/62 patients) assessment of intermediate single-vessel lesions between 2014 and 2015. Using receiver operating characteristic (ROC) curves we sought to identify the optimal FFR and iFR cut-off points with the highest discriminative power to predict the DSE result. The kappa coefficient was used to assess the agreement between FFR, iFR and DSE. The mean age of the study cohort was 63.5±12 years and 35 (56.5%) were males. Thirteen (21%) lesions were adjudicated as causing reversible ischaemia on DSE. Using ROC (FFR predicting DSE result), the area under the curve was 0.952 (95% CI: 0.902 to 1), whereas for iFR it was 0.743 (95% CI: 0.560 to 0.927), pAUC comparison=0.03. The optimal FFR cut-off point predicting positive DSE was 0.80. There was strong agreement between DSE and FFR (kappa 0.682, p<0.001). There was only modest agreement between iFR and DSE (kappa 0.258, p=0.068) using a cut-off value of 0.9.

CONCLUSIONS

In patients referred for evaluation of stable CAD, there was good agreement between DSE and FFR (87%) but less so with iFR (71.7%).

A comparison of 20 heterogeneity variance estimators in statistical synthesis of results from studies: a simulation study

When we synthesize research findings via meta-analysis, it is common to assume that the true underlying effect differs across studies. Total variability consists of the within-study and between-study variances (heterogeneity). There have been established measures, such as I² , to quantify the proportion of the total variation attributed to heterogeneity. There is a plethora of estimation methods available for estimating heterogeneity. The widely used DerSimonian and Laird estimation method has been challenged, but knowledge of the overall performance of heterogeneity estimators is incomplete. We identified 20 heterogeneity estimators in the literature and evaluated their performance in terms of mean absolute estimation error, coverage probability, and length of the confidence interval for the summary effect via a simulation study. Although previous simulation studies have suggested the Paule-Mandel estimator, it has not been compared with all the available estimators. For dichotomous outcomes, estimating heterogeneity through Markov chain Monte Carlo is a good choice if an informative prior distribution for heterogeneity is employed (eg, by published Cochrane reviews). Nonparametric bootstrap and positive DerSimonian and Laird perform well for all assessment criteria for both dichotomous and continuous outcomes. Hartung-Makambi estimator can be the best choice when the heterogeneity values are close to 0.07 for dichotomous outcomes and medium heterogeneity values (0.01 , 0.05) for continuous outcomes. Hence, there are heterogeneity estimators (nonparametric bootstrap DerSimonian and Laird and positive DerSimonian and Laird) that perform better than the suggested Paule-Mandel. Maximum likelihood provides the best performance for both types of outcome in the absence of heterogeneity.

Characteristics and knowledge synthesis approach for 456 network meta-analyses: a scoping review

BACKGROUND

Network meta-analysis (NMA) has become a popular method to compare more than two treatments. This scoping review aimed to explore the characteristics and methodological quality of knowledge synthesis approaches underlying the NMA process. We also aimed to assess the statistical methods applied using the Analysis subdomain of the ISPOR checklist.

METHODS

Comprehensive literature searches were conducted in MEDLINE, PubMed, EMBASE, and Cochrane Database of Systematic Reviews from inception until April 14, 2015. References of relevant reviews were scanned. Eligible studies compared at least four different interventions from randomised controlled trials with an appropriate NMA approach. Two reviewers independently performed study selection and data abstraction of included articles. All discrepancies between reviewers were resolved by a third reviewer. Data analysis involved quantitative (frequencies) and qualitative (content analysis) methods. Quality was evaluated using the AMSTAR tool for the conduct of knowledge synthesis and the ISPOR tool for statistical analysis.

RESULTS

After screening 3538 citations and 877 full-text papers, 456 NMAs were included. These were published between 1997 and 2015, with 95% published after 2006. Most were conducted in Europe (51%) or North America (31%), and approximately one-third reported public sources of funding. Overall, 84% searched two or more electronic databases, 62% searched for grey literature, 58% performed duplicate study selection and data abstraction (independently), and 62% assessed risk of bias. Seventy-eight (17%) NMAs relied on previously conducted systematic reviews to obtain studies for inclusion in their NMA. Based on the AMSTAR tool, almost half of the NMAs incorporated quality appraisal results to formulate conclusions, 36% assessed publication bias, and 16% reported the source of funding. Based on the ISPOR tool, half of the NMAs did not report if an assessment for consistency was conducted or whether they accounted for inconsistency when present. Only 13% reported heterogeneity assumptions for the random-effects model.

CONCLUSIONS

The knowledge synthesis methods and analytical process for NMAs are poorly reported and need improvement.

Modelling accretion disc and stellar wind interactions: the case of Sgr A

Sgr A* is an ideal target to study low-luminosity accreting systems. It has been recently proposed that properties of the accretion flow around Sgr A* can be probed through its interactions with the stellar wind of nearby massive stars belonging to the S-cluster. When a star intercepts the accretion disc, the ram and thermal pressures of the disc terminate the stellar wind leading to the formation of a bow shock structure. Here, a semi-analytical model is constructed which describes the geometry of the termination shock formed in the wind. With the employment of numerical hydrodynamic simulations, this model is both verified and extended to a region prone to Kelvin-Helmholtz instabilities. Because the characteristic wind and stellar velocities are in ∼10⁸ cm s^-1 range, the shocked wind may produce detectable X-rays via thermal bremsstrahlung emission. The application of this model to the pericentre passage of S2, the brightest member of the S-cluster, shows that the shocked wind produces roughly a month long X-ray flare with a peak luminosity of L ≈ 4 × 10³³ erg s^-1 for a stellar mass-loss rate, disc number density, and thermal pressure strength of [Formula: see text], n_d = 10⁵ cm^-3, and α = 0.1, respectively. This peak luminosity is comparable to the quiescent X-ray emission detected from Sgr A* and is within the detection capabilities of current X-ray observatories. Its detection could constrain the density and thickness of the disc at a distance of ∼3000 gravitational radii from the supermassive black hole.