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IntroductionPersons with Parkinson disease (PD) are hospitalized at higher rates, have longer lengths of stay, and are more likely to die in the hospital than age-matched peers. Although prior studies have compared inpatient outcomes between persons with and without PD, little is known about inpatient outcomes across the PD trajectory, or whether hospitalizations occurring in the last 6 months of life differ from earlier hospitalizations.MethodsThis cross-sectional study compared Medicare Part A and B beneficiaries aged 65 and older with a qualifying PD diagnosis who were hospitalized in 2017: decedents who died between 7/1/2017 and 12/31/2017 from all causes and were hospitalized at least once in their last 6 months of life, and non-decedents who were hospitalized between 1/1/2017 and 6/30/2017 and lived 6 or more months after discharge. End-of-life (EoL) hospitalizations were defined as those occurring in the last 6 months of life. Descriptive analyses compared patient-level variables (e.g., demographics, comorbidities, treatment intensity) and encounter-level variables (e.g., length of stay, total charges) between groups. Multivariable logistic regression models also compared rates of intensive care unit (ICU) admission and 30-day readmission between hospitalized decedents and hospitalized non-decedents, adjusting for age, sex, race/ethnicity, rural residence, and Charlson Comorbidity Index Score.ResultsOf 26,492 Medicare decedents with PD, 16,187 (61.1%) were hospitalized in their last 6 months of life. Of 347,512 non-decedents with PD, 62,851 (18.1%) were hospitalized in a 6-month period. Hospitalized decedents were slightly older than hospitalized non-decedents (82.3 [SD 7.40] vs. 79.5 [SD 7.54] years) and had significantly more comorbidities. Compared to non-EoL hospitalizations, EoL hospitalizations were slightly longer (5 [IQR 3–9] vs. 4 [IQR 3–7] days) and more expensive based on total charges per admission ($36,323 [IQR 20,091-69,048] vs. $32,309 [IQR 18,789–57,756]). In covariate-adjusted regression models using hospitalized non-decedents as the reference group, hospitalized decedents were more likely to experience an ICU admission (AOR 2.36; CI 2.28–2.45) and 30-day readmission (AOR 2.43; CI 2.34–2.54).DiscussionHospitalizations occurring in the last 6 months of life among persons with PD in the United States are longer, more costly, and more resource intensive than earlier hospitalizations and may stem from medical comorbidities. Once hospitalized, ICU admission and 30-day readmission may aid in prognostication and serve as markers of transition to the EoL period.

Abstract. The use of routinely collected health data (real-world data, RWD) to generate real-world evidence (RWE) for research purposes is a growing field. Computerized search methods, large electronic databases, and the development of novel statistical methods allow for valid analysis of data outside its primary clinical purpose. Here, we systematically reviewed the methodology used for RWE studies in pain research. We searched 3 databases (PubMed, EMBASE, and Web of Science) for studies using retrospective data sources comparing multiple groups or treatments. The protocol was registered under the DOI:10.17605/OSF.IO/KGVRM. A total of 65 studies were included. Of those, only 4 compared pharmacological interventions, whereas 49 investigated differences in surgical procedures, with the remaining studying alternative or psychological interventions or epidemiological factors. Most 39 studies reported significant results in their primary comparison, and an additional 12 reported comparable effectiveness. Fifty-eight studies used propensity scores to account for group differences, 38 of them using 1:1 case:control matching. Only 17 of 65 studies provided sensitivity analyses to show robustness of their findings, and only 4 studies provided links to publicly accessible protocols. RWE is a relevant construct that can provide evidence complementary to randomized controlled trials (RCTs), especially in scenarios where RCTs are difficult to conduct. The high proportion of studies reporting significant differences between groups or comparable effectiveness could imply a relevant degree of publication bias. RWD provides a potentially important resource to expand high-quality evidence beyond clinical trials, but rigorous quality standards need to be set to maximize the validity of RWE studies.

Abstract. Although certain risk factors can identify individuals who are most likely to develop chronic pain, few interventions to prevent chronic pain have been identified. To facilitate the identification of preventive interventions, an IMMPACT meeting was convened to discuss research design considerations for clinical trials investigating the prevention of chronic pain. We present general design considerations for prevention trials in populations that are at relatively high risk for developing chronic pain. Specific design considerations included subject identification, timing and duration of treatment, outcomes, timing of assessment, and adjusting for risk factors in the analyses. We provide a detailed examination of 4 models of chronic pain prevention (ie, chronic postsurgical pain, postherpetic neuralgia, chronic low back pain, and painful chemotherapy-induced peripheral neuropathy). The issues discussed can, in many instances, be extrapolated to other chronic pain conditions. These examples were selected because they are representative models of primary and secondary prevention, reflect persistent pain resulting from multiple insults (ie, surgery, viral infection, injury, and toxic or noxious element exposure), and are chronically painful conditions that are treated with a range of interventions. Improvements in the design of chronic pain prevention trials could improve assay sensitivity and thus accelerate the identification of efficacious interventions. Such interventions would have the potential to reduce the prevalence of chronic pain in the population. Additionally, standardization of outcomes in prevention clinical trials will facilitate meta-analyses and systematic reviews and improve detection of preventive strategies emerging from clinical trials.

An interesting and valuable discussion has arisen from our recent article (Segerdahl, Mezue et al., 2015) and we are pleased here to have the opportunity to expand on the various points we made. Equally important, we wish to correct several important misunderstandings that were made by Davis and colleagues that possibly contributed to their concerns about power when assessing our paper (e.g. actual subject numbers used in control experiment and the reality of the signal-to-noise and sampling of the multi-TI technique we employed). Here, we clarify the methods and analysis plus discuss how we interpret the data in the Brief Communication noting that the extrapolation and inferences made by Davis and colleagues are not consistent with our report or necessarily, in our opinion, what the data supports. We trust this reassures the F1000Research readership regarding the robustness of our results and what we actually concluded in the paper regarding their possible meaning. We are pleased, though, that Davis and colleagues have used our article to raise an important discussion around pain perception, and here offer some further insights towards that broader discussion.

Despite the significant health impact of gout, there is no consensus on management standards. To guide physician practice, we sought to develop quality of care indicators for gout management. A systematic literature review of gout therapy was performed using the Medline database. Two abstractors independently reviewed each of the articles for relevance and satisfaction of minimal inclusion criteria. Based on the review of the literature, 11 preliminary quality indicators were developed and then reviewed and refined by an initial feasibility panel of community and academic rheumatologists. A twelfth indicator was added at the request of the first panel. Using a modification of the RAND/University of California at Los Angeles appropriateness method (bridging teleconference and white-board Internet technology were added), a second expert panel rated each of the proposed indicators for validity using a 9-point scale, in which ratings of 1–3, 4–6, and 7–9 were considered “invalid,” “indeterminate,” and “highly valid,” respectively. Indicators were considered valid if the median panel rating was ≥7 and there was no evidence of panel disagreement (defined to occur when 2 of 6 panelists provided a validity rating of 1–3 and 2 panelists provided a validity rating of 7–9). Ten of the 12 draft indicators were rated to be valid by our second expert panel. Validated indicators pertained to 1) the use of urate-lowering medications in chronic gout, 2) the use of antiinflammatory drugs, and 3) counseling on lifestyle modifications. Using a combination of evidence and expert opinion, 10 indicators for quality of gout care were developed. These indicators represent an important initial step in quality improvement initiatives for gout care. [ABSTRACT FROM AUTHOR]

The aim of this study was to use Monte Carlo simulations to compare logistic regression with propensity scores in terms of bias, precision, empirical coverage probability, empirical power, and robustness when the number of events is low relative to the number of confounders. The authors simulated a cohort study and performed 252,480 trials. In the logistic regression, the bias decreased as the number of events per confounder increased. In the propensity score, the bias decreased as the strength of the association of the exposure with the outcome increased. Propensity scores produced estimates that were less biased, more robust, and more precise than the logistic regression estimates when there were seven or fewer events per confounder. The logistic regression empirical coverage probability increased as the number of events per confounder increased. The propensity score empirical coverage probability decreased after eight or more events per confounder. Overall, the propensity score exhibited more empirical power than logistic regression. Propensity scores are a good alternative to control for imbalances when there are seven or fewer events per confounder; however, empirical power could range from 35% to 60%. Logistic regression is the technique of choice when there are at least eight events per confounder. [ABSTRACT FROM AUTHOR]

Purpose: Hot flashes are a common and debilitating symptom among survivors of breast cancer. This study aimed at evaluating the effects of electroacupuncture (EA) versus gabapentin (GP) for hot flashes among survivors of breast cancer, with a specific focus on the placebo and nocebo effects., Patients and Methods: We conducted a randomized controlled trial involving 120 survivors of breast cancer experiencing bothersome hot flashes twice per day or greater. Participants were randomly assigned to receive 8 weeks of EA or GP once per day with validated placebo controls (sham acupuncture [SA] or placebo pills [PPs]). The primary end point was change in the hot flash composite score (HFCS) between SA and PP at week 8, with secondary end points including group comparisons and additional evaluation at week 24 for durability of treatment effects., Results: By week 8, SA produced significantly greater reduction in HFCS than did PP (-2.39; 95% CI, -4.60 to -0.17). Among all treatment groups, the mean reduction in HFCS was greatest in the EA group, followed by SA, GP, and PP (-7.4 v -5.9 v -5.2 v -3.4; P = < .001). The pill groups had more treatment-related adverse events than did the acupuncture groups: GP (39.3%), PP (20.0%), EA (16.7%), and SA (3.1%), with P = .005. By week 24, HFCS reduction was greatest in the EA group, followed by SA, PP, and GP (-8.5 v -6.1 v -4.6 v -2.8; P = .002)., Conclusion: Acupuncture produced larger placebo and smaller nocebo effects than did pills for the treatment of hot flashes. EA may be more effective than GP, with fewer adverse effects for managing hot flashes among breast cancer survivors; however, these preliminary findings need to be confirmed in larger randomized controlled trials with long-term follow-up., (© 2015 by American Society of Clinical Oncology.)