Objectives:To test the effects of pregabalin on the induction of neurogenic claudication.Methods:This study was a randomized, double-blind, active placebo-controlled, 2-period, crossover trial. Twenty-nine subjects were randomized to receive pregabalin followed by active placebo (i.e., diphenhydramine) or active placebo followed by pregabalin. Each treatment period lasted 10 days, including a 2-step titration. Periods were separated by a 10-day washout period, including a 3-day taper phase after the first period. The primary outcome variable was the time to first moderate pain symptom (Numeric Rating Scale score ≥4) during a 15-minute treadmill test (Tfirst). Secondary outcome measures included pain intensity at rest, pain intensity at the end of the treadmill test, distance walked, and validated self-report measures of pain and functional limitation including the Roland-Morris Disability Questionnaire, modified Brief Pain Inventory–Short Form, Oswestry Disability Index, and Swiss Spinal Stenosis Questionnaire.Results:No significant difference was found between pregabalin and active placebo for the time to first moderate pain symptom (difference in median Tfirst = −1.08 [95% confidence interval −2.25 to 0.08], p = 0.61). In addition, none of the secondary outcome measures of pain or functional limitation were significantly improved by pregabalin compared with active placebo.Conclusions:Pregabalin was not more effective than active placebo in reducing painful symptoms or functional limitations in patients with neurogenic claudication associated with lumbar spinal stenosis.Classification of evidence:This study provides Class I evidence that for patients with neurogenic claudication, compared with diphenhydramine, pregabalin does not increase the time to moderate pain during a treadmill test.

No abstract

These data support the use of CTA as an accurate method of calculating carotid artery stenosis based on agreement with Strandness criteria applied to CDUS velocities. When additional imaging beyond CDUS is necessary, we report no significant difference between diameter and CSA measurements obtained from CTA for preoperative evaluation of carotid disease.

We consider a study-level meta-analysis with a normally distributed outcome variable and possibly unequal study-level variances, where the object of inference is the difference in means between a treatment and control group. A common complication in such an analysis is missing sample variances for some studies. A frequently-used approach is to impute the weighted (by sample size) mean of the observed variances (mean imputation). Another approach is to include only those studies with variances reported (complete case analysis). Both mean imputation and complete case analysis are only valid under the missing-completely-at-random assumption (MCAR), and even then the inverse variance weights produced are not necessarily optimal. We propose a multiple imputation method employing gamma meta-regression to impute the missing sample variances. Our method takes advantage of study-level covariates that may be used to provide information about the missing data. Through simulation studies, we show that multiple imputation, when the imputation model is correctly specified, is superior to competing methods in terms of confidence interval coverage probability and type I error probability when testing a specified group difference. Finally, we describe a similar approach to handling missing variances in cross-over studies.

No abstract