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Introduction. Regional anesthesia is a rapidly growing subspecialty. There are few published meta-analyses exploring pain outcome measures utilised in regional anesthesia randomized controlled trials (RCTs), which may be due to heterogeneity in outcomes assessed. This systematic review explores postoperative pain outcomes utilised in regional anesthesia RCTs. Methods. A literature search was performed using three databases (Medline, Embase, and CINAHL). Regional anesthesia RCTs with postoperative pain as a primary outcome were included if written in English and published in one of the top 20 impact factor journals between 2005 and 2017. Study quality was assessed using the Cochrane Collaboration’s tool for assessing risk of bias. Results. From the 31 included articles, 15 different outcome measures in total were used to assess postoperative pain. The most commonly (16/31) used outcome measures were verbal numerical grading of pain out of 10, total opioid consumption, and visual analogue scale 10 cm (VAS). The need for analgesia was used as an outcome measure where studies did not use a pain rating score. Ten studies reported pain scores on activity and 27/31 studies utilised ≥2 pain outcomes. Time of measurement of pain score also varied with a total of 51 different time points used in total. Conclusion. Analysis of the articles demonstrated heterogeneity and inconsistency in choice of pain outcome and time of measurement within regional anesthesia studies. Identification of these pain outcomes utilised can help to create a definitive list of core outcomes, which may guide future researchers when designing such studies.

Many studies have sought to describe the relationship between sleep disturbance and cognition in Parkinson's disease (PD). The Parkinson's Disease Sleep Scale (PDSS) and its variants (the Parkinson's disease Sleep Scale-Revised; PDSS-R, and the Parkinson's Disease Sleep Scale-2; PDSS-2) quantify a range of symptoms impacting sleep in only 15 items. However, data from these scales may be problematic as included items have considerable conceptual breadth, and there may be overlap in the constructs assessed. Multidimensional measurement models, accounting for the tendency for items to measure multiple constructs, may be useful more accurately to model variance than traditional confirmatory factor analysis. In the present study, we tested the hypothesis that a multidimensional model (a bifactor model) is more appropriate than traditional factor analysis for data generated by these types of scales, using data collected using the PDSS-R as an exemplar. 166 participants diagnosed with idiopathic PD participated in this study. Using PDSS-R data, we compared three models: a unidimensional model; a 3-factor model consisting of sub-factors measuring insomnia, motor symptoms and obstructive sleep apnoea (OSA) and REM sleep behaviour disorder (RBD) symptoms; and, a confirmatory bifactor model with both a general factor and the same three sub-factors. Only the confirmatory bifactor model achieved satisfactory model fit, suggesting that PDSS-R data are multidimensional. There were differential associations between factor scores and patient characteristics, suggesting that some PDSS-R items, but not others, are influenced by mood and personality in addition to sleep symptoms. Multidimensional measurement models may also be a helpful tool in the PDSS and the PDSS-2 scales and may improve the sensitivity of these instruments.