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Objective: Transcutaneous auricular vagus nerve stimulation (tVNS) may be an innovative treatment for symptoms of knee osteoarthritis (OA) due to possible shared pathological mechanisms between diminished parasympathetic function, central pain mechanisms, and knee pain. Thus, we sought to test the safety and preliminary efficacy of tVNS in people with knee OA. Design: A pilot trial in which participants received a 60-min tVNS was conducted. At baseline, immediately after, and 15 ​min after tVNS, we assessed knee pain, pressure pain threshold (PPT), temporal summation (TS), conditioned pain modulation (CPM), and high-frequency power of heart rate variability (HF). We examined the extent to which these outcome measures changed after tVNS using linear mixed models. Results: 30 participants with knee OA were included, and all completed the intervention without any major side effects. Compared to baseline, knee pain was reduced by 1.27 (95 ​% CI, −1.74, −0.80) immediately after and by 1.87 (−2.33, −1.40) 15 ​min after tVNS; CPM improved by 0.11 (0.04, 0.19) and 0.07 (−0.01, 0.15); and HF improved by 213.29 (−0.38, 426.96) and 234.17 (20.49, 447.84). PPT and TS were not changed after tVNS. Conclusions: Our preliminary data demonstrated that tVNS may be a safe pain-relieving treatment for people with knee OA. Our findings suggest that improvement of knee pain might be derived from improvement of parasympathetic function and central pain mechanisms as no local therapy was applied. A large study is needed to confirm that tVNS is a novel intervention to ameliorate knee pain in people with knee OA. Clinical Trial: ClinicalTrials.gov (NCT05625178).

Abstract Individual-level assessment of health and well-being permits analysis of community well-being and health risk evaluations across several dimensions of health. It also enables comparison and rankings of reported health and well-being for large geographical areas such as states, metropolitan areas, and counties. However, there is large variation in reported well-being within such large spatial units underscoring the importance of analyzing well-being at more granular levels, such as ZIP codes. In this paper, we address this problem by modeling well-being data to generate ZIP code tabulation area (ZCTA)-level rankings through spatially informed statistical modeling. We build regression models for individual-level overall well-being index and scores from five subscales (Physical, Financial, Social, Community, Purpose) using individual-level demographic characteristics as predictors while including a ZCTA-level spatial effect. The ZCTA neighborhood information is incorporated by using a graph Laplacian matrix; this enables estimation of the effect of a ZCTA on well-being using individual-level data from that ZCTA as well as by borrowing information from neighboring ZCTAs. We deploy our model on well-being data for the U.S. states of Massachusetts and Georgia. We find that our model can capture the effects of demographic features while also offering spatial effect estimates for all ZCTAs, including ones with no observations, under certain conditions. These spatial effect estimates provide community health and well-being rankings of ZCTAs, and our method can be deployed more generally to model other outcomes that are spatially dependent as well as data from other states or groups of states.

Background Established cardiovascular disease (CVD) risk prediction functions may not accurately predict CVD risk in people with HIV. We assessed the performance of 3 CVD risk prediction functions in 2 HIV cohorts. Methods and Results CVD risk scores were calculated in the Mass General Brigham and Kaiser Permanente Northern California HIV cohorts, using the American College of Cardiology/American Heart Association atherosclerotic CVD function, the FHS (Framingham Heart Study) hard coronary heart disease function and the Framingham Heart Study hard CVD function. Outcomes were myocardial infarction or coronary death for FHS hard coronary heart disease function; and myocardial infarction, stroke, or coronary death for American College of Cardiology/American Heart Association and FHS hard CVD function. We calculated regression coefficients and assessed discrimination and calibration by sex; predicted to observed risk of outcome was also compared. In the combined cohort of 9412, 158 (1.7%) had a coronary heart disease event, and 309 (3.3%) had a CVD event. Among women, CVD risk was generally underestimated by all 3 risk functions. Among men, CVD risk was underestimated by the American College of Cardiology/American Heart Association and FHS hard CVD function, but overestimated by the FHS hard coronary heart disease function. Calibration was poor for women using the FHS hard CVD function and for men using all functions. Discrimination in all functions was good for women (c‐statistics ranging from 0.78 to 0.90) and moderate for men (c‐statistics ranging from 0.71 to 0.72). Conclusions Established CVD risk prediction functions generally underestimate risk in people with HIV. Differences in model performance by sex underscore the need for both HIV‐specific and sex‐specific functions. Development of CVD risk prediction models tailored to HIV will enhance care for aging people with HIV.

Bayesian Optimal Interval (BOIN) designs are a class of model-assisted dose-finding designs that can be used in oncology trials to determine the maximum tolerated dose (MTD) of a study drug based on safety or the optimal biological dose (OBD) based on safety and efficacy. BOIN designs provide a complete suite for dose finding in early phase trials, as well as a consistent way to explore different scenarios such as toxicity, efficacy, continuous outcomes, delayed toxicity or efficacy and drug combinations in a unified manner with easy access to software to implement most of these designs. Although built upon Bayesian probability models, BOIN designs are operationally simple in general and have good statistical operating characteristics compared to other dose-finding designs. This review paper describes the original BOIN design and its many extensions, their advantages and limitations, the software used to implement them, and the most suitable situation for use of each of these designs. Published examples of the implementation of BOIN designs are provided in the Appendix.

Objective Hypomagnesemia increases the risk of chondrocalcinosis and calcium pyrophosphate disease. We examined whether the use of drugs that can cause hypomagnesemia, diuretics and proton pump inhibitors (PPIs), increases the risk of chondrocalcinosis. Methods Participants in the Multicenter Osteoarthritis (MOST) Study obtained weight‐bearing knee radiographs, and their medication use was recorded at baseline and 30‐, 60‐, 84‐, and 144‐month examinations. We read radiographs serially for chondrocalcinosis and characterized incident chondrocalcinosis when it first appeared. We classified diuretic use as thiazide, loop, and other. To test drug effects on incident chondrocalcinosis at each interval (eg, 30‐60 months), we excluded persons with chondrocalcinosis at the interval’s beginning. For each drug, we evaluated exposure at the beginning and end of the interval. We conducted knee‐based analyses using Bayesian mixed‐effects discrete time survival models adjusted for age, sex, body mass index, radiographic osteoarthritis, race, and clinic site. Results Of 5272 knees, 196 developed chondrocalcinosis. Thiazide use (21.7% of examinations) and PPI use (13.7%) were common. Neither loop nor other diuretic use was associated with incident chondrocalcinosis. Thiazide use at the beginning and end of the interval of incidence conferred a high risk (hazard ratio [HR] = 2.18; 95% confidence interval [CI] 1.23‐3.89), but use at the beginning of the interval was not associated with risk (HR = 1.04). PPI use at the interval’s beginning increased risk of chondrocalcinosis (HR = 2.29; 95% CI 1.37‐3.79). Conclusion Thiazide diuretics, but not other diuretics, and PPI use probably increase the risk of chondrocalcinosis. These findings may have important clinical implications.

With the emergence of immunotherapy and other novel therapies, the traditional assumption that the efficacy of the study drug increases monotonically with dose levels is not always true. Therefore, dose-finding methods evaluating only toxicity data may not be adequate. In this paper, we have first compared the Modified Toxicity Probability Interval (mTPI) and Toxicity Equivalence Range (TEQR) dose-finding oncology designs for safety with identical stopping rules; we have then extended both designs to include efficacy in addition to safety – we determine the optimal dose for safety and efficacy using these designs by applying isotonic regression to the observed toxicity and efficacy rates, once the early phase trial is completed. We consider multiple types of underlying dose response curves, i.e., monotonically increasing, plateau, or umbrella-shaped. We conduct simulation studies to investigate the operating characteristics of the two proposed designs and compare them to existing designs. We found that the extended mTPI design selects the optimal dose for safety and efficacy more accurately than the other designs for most of the scenarios considered. Keywords: Early phase immunooncology design considering efficacy and safety, Extended mTPI design, Extended TEQR design, Optimal biological dose isotonic design, Eff-Tox design, Umbrella-shaped dose-response curve

Dose finding Phase I oncology designs can be broadly categorized as rule based, such as the 3 + 3 and the accelerated titration designs, or model based, such as the CRM and Eff-Tox designs. This paper systematically reviews and compares through simulations several statistical operating characteristics, including the accuracy of maximum tolerated dose (MTD) selection, the percentage of patients assigned to the MTD, over-dosing, under-dosing, and the trial dose-limiting toxicity (DLT) rate, of eleven rule-based and model-based Phase I oncology designs that target or pre-specify a DLT rate of ∼0.2, for three sets of true DLT probabilities. These DLT probabilities are generated at common dosages from specific linear, logistic, and log-logistic dose-toxicity curves. We find that all the designs examined select the MTD much more accurately when there is a clear separation between the true DLT rate at the MTD and the rates at the dose level immediately above and below it, such as for the DLT rates generated using the chosen logistic dose-toxicity curve; the separations in these true DLT rates depend, in turn, not only on the functional form of the dose-toxicity curve but also on the investigated dose levels and the parameter set-up. The model based mTPI, TEQR, BOIN, CRM and EWOC designs perform well and assign the greatest percentages of patients to the MTD, and also have a reasonably high probability of picking the true MTD across the three dose-toxicity curves examined. Among the rule-based designs studied, the 5 + 5 a design picks the MTD as accurately as the model based designs for the true DLT rates generated using the chosen log-logistic and linear dose-toxicity curves, but requires enrolling a higher number of patients than the other designs. We also find that it is critical to pick a design that is aligned with the true DLT rate of interest. Further, we note that Phase I trials are very small in general and hence may not provide accurate estimates of the MTD. Thus our work provides a map for planning Phase I oncology trials or developing new ones.

Designs, such as the Eff-Tox, OBD (optimal biological dose), STEIN (simple efficacy toxicity interval), and TEPI (toxicity efficacy probability interval) designs, have been proposed to determine the optimal dose of a new oncology drug using both efficacy and toxicity. The goal of these designs is to select the optimal drug dose for further phase trials more accurately than dose finding designs that only consider toxicity, such as the 3 + 3, TEQR (toxicity equivalence range), mTPI (modified toxicity probability interval), and EWOC (escalation with overdose control) designs. We propose a new frequentist design for optimal dose selection, the 2D TEQR design, that is easier to understand and simpler to implement than the TEPI, Eff-Tox, STEIN and OBD designs, as it is based on the empirical or observed toxicity and efficacy rates and does not require specialized computations. We compare the performance of this new design with those of the TEPI, STEIN, Eff-Tox and OBD Isotonic designs. Although for the same sample size and cohort size, the frequentist 2D TEQR design is less accurate than the Bayesian TEPI design and also the STEIN design in selecting the optimal dose, the accuracy of optimal dose selection of the 2D TEQR design can be increased, in many cases, with a moderate increase in cohort size. The 2D TEQR design is as accurate as or more accurate than the Eff-Tox design in optimal dose selection, and better than the OBD Isotonic design, unless there is a clear peak in the true response rates, in which case the OBD Isotonic design performs better than the other designs. Keywords: Early phase oncology design, 2D TEQR design, OBD isotonic and eff-tox designs, Optimal dose for safety and efficacy

Background Microvascular dysfunction is a marker of early vascular disease that predicts cardiovascular events. Whether metabolically healthy obese individuals have impaired microvascular function remains unclear. The aim of this study was to evaluate the relation of obesity phenotypes stratified by metabolic status to microvascular function. Methods and Results We meta‐analyzed aggregate data from 3 large cohorts (Brazilian Longitudinal Study of Adult Health, the Framingham Heart Study, and the Gutenberg Heart Study; n=16 830 participants, age range 19–90, 51.3% men). Regression slopes between cardiovascular risk factors and microvascular function, measured by peripheral arterial tonometry (PAT), were calculated. Individuals were classified as normal‐weight, overweight, or obese by body mass index (BMI) and stratified by healthy or unhealthy metabolic status based on metabolic syndrome using the ATP‐III criteria. Male sex, BMI, and metabolic risk factors were associated with higher baseline pulse amplitude and lower PAT ratio. There was stepwise impairment of vascular measures from normal weight to obesity in both metabolic status strata. Metabolically healthy obese individuals had more impaired vascular function than metabolically healthy normal‐weight individuals (baseline pulse amplitude 6.12±0.02 versus 5.61±0.01; PAT ratio 0.58±0.01 versus 0.76±0.01, all P

Meta-analyses conducted via the Cochrane Collaboration adhere to strict methodological and reporting standards aiming to minimize bias, maximize transparency/reproducibility, and improve the accuracy of summarized data. Whether this results in differences in the results reported by meta-analyses on the same topic conducted outside the Cochrane Collaboration is an open question.We conducted a matched-pair analysis with individual meta-analyses as the unit of analysis, comparing Cochrane and non-Cochrane reviews. Using meta-analyses from the cardiovascular literature, we identified pairs that matched on intervention and outcome. The pairs were contrasted in terms of how frequently results disagreed between the Cochrane and non-Cochrane reviews, whether effect sizes and statistical precision differed systematically, and how these differences related to the frequency of secondary citations of those reviews.Our search yielded 40 matched pairs of reviews. The two sets were similar in terms of which was first to publication, how many studies were included, and average sample sizes. The paired reviews included a total of 344 individual clinical trials: 111 (32.3%) studies were included only in a Cochrane review, 104 (30.2%) only in a non-Cochrane review, and 129 (37.5%) in both. Stated another way, 62.5% of studies were only included in one or the other meta-analytic literature. Overall, 37.5% of pairs had discrepant results. The most common involved shifts in the width of 95% confidence intervals that would yield a different statistical interpretation of the significance of results (7 pairs). Additionally, 20% differed in the direction of the summary effect size (5 pairs) or reported greater than a 2-fold difference in its magnitude (3 pairs). Non-Cochrane reviews reported significantly higher effect sizes (P< 0.001) and lower precision (P

Practice guidelines recommend addressing obesity for people with knee OA, however, the association of obesity with walking independent of pain is not known. We investigated this association within the Multicenter Osteoarthritis Study, a cohort of older adults who have or are at high risk of knee OA. Subjects wore a StepWatch to record steps taken over 7 days. We measured knee pain from a visual analogue scale and obesity by BMI. We examined the association of obesity with walking using linear regression adjusting for pain and covariates. Of 1788 subjects, the mean steps/day taken was 8872.9±3543.4. Subjects with a BMI ≥35 took 3355 fewer steps per day independent of knee pain compared with those with a BMI ≤25 (95% CI −3899, −2811). BMI accounted for 9.7% of the variability of walking while knee pain accounted for 2.9%. BMI was associated with walking independent of knee pain.