BACKGROUND: The outcomes of several randomized trials on extracorporeal cardiopulmonary resuscitation (ECPR) in patients with refractory out-of-hospital cardiac arrest were examined using frequentist methods, resulting in a dichotomous interpretation of results based on p-values rather than in the probability of clinically relevant treatment effects. To determine such a probability of a clinically relevant ECPR-based treatment effect on neurological outcomes, the authors of these trials performed a Bayesian meta-analysis of the totality of randomized ECPR evidence.
METHODS: A systematic search was applied to three electronic databases. Randomized trials that compared ECPR-based treatment with conventional CPR for refractory out-of-hospital cardiac arrest were included. The study was preregistered in INPLASY (INPLASY2023120060). The primary Bayesian hierarchical meta-analysis estimated the difference in 6-month neurologically favorable survival in patients with all rhythms, and a secondary analysis assessed this difference in patients with shockable rhythms (Bayesian hierarchical random-effects model). Primary Bayesian analyses were performed under vague priors. Outcomes were formulated as estimated median relative risks, mean absolute risk differences, and numbers needed to treat with corresponding 95% credible intervals (CrIs). The posterior probabilities of various clinically relevant absolute risk difference thresholds were estimated.
RESULTS: Three randomized trials were included in the analysis (ECPR, n = 209 patients; conventional CPR, n = 211 patients). The estimated median relative risk of ECPR for 6-month neurologically favorable survival was 1.47 (95%CrI 0.73-3.32) with a mean absolute risk difference of 8.7% (- 5.0; 42.7%) in patients with all rhythms, and the median relative risk was 1.54 (95%CrI 0.79-3.71) with a mean absolute risk difference of 10.8% (95%CrI - 4.2; 73.9%) in patients with shockable rhythms. The posterior probabilities of an absolute risk difference > 0% and > 5% were 91.0% and 71.1% in patients with all rhythms and 92.4% and 75.8% in patients with shockable rhythms, respectively.
CONCLUSIONS: The current Bayesian meta-analysis found a 71.1% and 75.8% posterior probability of a clinically relevant ECPR-based treatment effect on 6-month neurologically favorable survival in patients with all rhythms and shockable rhythms. These results must be interpreted within the context of the reported credible intervals and varying designs of the randomized trials.
BACKGROUND: INPLASY (INPLASY2023120060, December 14th, 2023, https://doi.org/10.37766/inplasy2023.12.0060 ).

The present study examined the effects of home-based remotely supervised transcranial direct current stimulation on quantitative sensory testing measurements in older adults with knee osteoarthritis. Participants were hypothesized to experience improved pain measurements over time.
Open-label, single-arm trial.
Southeast Texas between March and November 2018 at a nursing school and participant homes.
Older adults (aged 50-85 years) with self-reported unilateral or bilateral knee osteoarthritis pain who met eligibility criteria set by the American College of Rheumatology.
The intervention was applied with a constant current intensity for 20 minutes every weekday for two weeks (10 total sessions). Quantitative measures of pain were collected three times over 10 days (days 1, 5, and 10) and included heat threshold and tolerance, pressure pain threshold, punctate mechanical pain, pain, and conditioned pain modulation. Analyses used nonparametric tests to evaluate differences between day 1 and day 10. Generalized linear mixed models were then used to evaluate change across all three time points for each measure. Bayesian inference was used to provide the posterior probability of longitudinal effects.
Nonparametric tests found improvements in seven measures, and longitudinal models supported improvements in 10 measures, with some nonlinear effects.
The home-based, remotely supervised intervention improved quantitative measurements of pain in older adults with knee osteoarthritis. This study contributes to the growing body of literature supporting home-based noninvasive stimulation interventions.

Transcranial direct current stimulation (tDCS) has demonstrated effectiveness in reducing clinical and experimental measures of pain in patients with chronic pain; however, research examining the mechanisms of action for the effects of tDCS has been lacking. The present study investigated the effect of active tDCS on measures of inflammation and stress. Older adults (aged 50-70 years) with knee osteoarthritis (OA) were randomly assigned to receive daily 20-min sessions of either tDCS (n = 20) or sham tDCS (n = 20) for 5 consecutive days. Participants provided blood samples at baseline and the end of treatment. The following measures of immune function and stress were collected: interleukin (IL)-6 and 10, tumor necrosis factor-α (TNF-α), C-reactive protein, cortisol, and β-endorphin. Generalized linear modeling evaluated each posttreatment measure as a function of tDCS group, controlling for baseline (measuring residual change, analogous to analysis of covariance). Bayesian statistical inference was used to directly quantify the probability of the effect of active tDCS. IL-6, IL-10, TNF-α, and β-endorphin demonstrated lower levels of stress and inflammation in the active tDCS group. These findings provide preliminary evidence that active (relative to sham) tDCS is associated with reduced levels of inflammation.

Inferring structural information from the intensity of a small-angle scattering (SAS) experiment is an ill-posed inverse problem. Thus, the determination of a solution is in general non-trivial. In this work, the indirect Fourier transform (IFT), which determines the pair distance distribution function from the intensity and hence yields structural information, is discussed within two different statistical inference approaches, namely a frequentist one and a Bayesian one, in order to determine a solution objectively From the frequentist approach the cross-validation method is obtained as a good practical objective function for selecting an IFT solution. Moreover, modern machine learning methods are employed to suppress oscillatory behaviour of the solution, hence extracting only meaningful features of the solution. By comparing the results yielded by the different methods presented here, the reliability of the outcome can be improved and thus the approach should enable more reliable information to be deduced from SAS experiments.