Description: Background: Using surrogate biomarkers for disease progression as endpoints in neuroprotective clinical trials may help differentiate symptomatic effects of potential neuroprotective agents from true disease-modifying effects. A systematic review was undertaken to determine what biomarkers for disease progression in Parkinson's disease (PD) exist. Methods: MEDLINE and EMBASE (1950--2010) were searched using five search strategies. Abstracts were assessed to identify papers meriting review in full. Studies of participants with idiopathic PD diagnosed by formal criteria or clearly described clinical means were included. We made no restriction on age, disease duration, drug treatment, or study design. We included studies which attempted to draw associations between any tests used to investigate disease progression and any clinical measures of disease progression. The electronic search was validated by hand-searching the two journals from which most included articles came. Results: 183 studies were included: 163 (89%) cross-sectional, 20 (11%) longitudinal. The electronic search strategy had a sensitivity of 71.4% (95% CI 51.1-86.0) and a specificity of 97.1% (95% CI 96.5-97.7). In longitudinal studies median follow-up was 2.0 years (IQR 1.1-3.5). Included studies were generally poor quality - cross-sectional with small numbers of participants, applying excessive inclusion/exclusion criteria, with flawed methodologies and simplistic statistical analyses. Conclusion: We found insufficient evidence to recommend the use of any biomarker for disease progression in PD clinical trials, which may simply reflect the poor quality of research in this area. We therefore present a provisional 'roadmap' for conducting future disease progression biomarker studies, and recommend new quality criteria by which future studies may be judged.

Description: Background: Numerous papers have been published examining risk factors for revision of primary total hip arthroplasty (THA), but there have been no comprehensive systematic literature reviews that summarize the most recent findings across a broad range of potential predictors. Methods: We performed a PubMed search for papers published between January, 2000 and November, 2010 that provided data on risk factors for revision of primary THA. We collected data on revision for any reason, as well as on revision for aseptic loosening, infection, or dislocation. For each risk factor that was examined in at least three papers, we summarize the number and direction of statistically significant associations reported. Results: Eighty-six papers were included in our review. Factors found to be associated with revision included younger age, greater comorbidity, a diagnosis of avascular necrosis (AVN) as compared to osteoarthritis (OA), low surgeon volume, and larger femoral head size. Male sex was associated with revision due to aseptic loosening and infection. Longer operating time was associated with revision due to infection. Smaller femoral head size was associated with revision due to dislocation. Conclusions: This systematic review of literature published between 2000 and 2010 identified a range of demographic, clinical, surgical, implant, and provider variables associated with the risk of revision following primary THA. These findings can inform discussions between surgeons and patients relating to the risks and benefits of undergoing total hip arthroplasty.

Description: Background: Manual eligibility screening (ES) for a clinical trial typically requires a labor-intensive review of patient records that utilizes many resources. Leveraging state-of-the-art natural language processing (NLP) and information extraction (IE) technologies, we sought to improve the efficiency of physician decision-making in clinical trial enrollment. In order to markedly reduce the pool of potential candidates for staff screening, we developed an automated ES algorithm to identify patients who meet core eligibility characteristics of an oncology clinical trial. Methods: We collected narrative eligibility criteria from ClinicalTrials.gov for 55 clinical trials actively enrolling oncology patients in our institution between 12/01/2009 and 10/31/2011. In parallel, our ES algorithm extracted clinical and demographic information from the Electronic Health Record (EHR) data fields to represent profiles of all 215 oncology patients admitted to cancer treatment during the same period. The automated ES algorithm then matched the trial criteria with the patient profiles to identify potential trial-patient matches. Matching performance was validated on a reference set of 169 historical trial-patient enrollment decisions, and workload, precision, recall, negative predictive value (NPV) and specificity were calculated. Results: Without automation, an oncologist would need to review 163 patients per trial on average to replicate the historical patient enrollment for each trial. This workload is reduced by 85% to 24 patients when using automated ES (precision/recall/NPV/specificity: 12.6%/100.0%/100.0%/89.9%). Without automation, an oncologist would need to review 42 trials per patient on average to replicate the patient-trial matches that occur in the retrospective data set. With automated ES this workload is reduced by 90% to four trials (precision/recall/NPV/specificity: 35.7%/100.0%/100.0%/95.5%). Conclusion: By leveraging NLP and IE technologies, automated ES could dramatically increase the trial screening efficiency of oncologists and enable participation of small practices, which are often left out from trial enrollment. The algorithm has the potential to significantly reduce the effort to execute clinical research at a point in time when new initiatives of the cancer care community intend to greatly expand both the access to trials and the number of available trials.