Our latest research, all in one place. Browse our collection of journal articles, reports and conference proceedings to see how we’re contributing to HEOR research. Remember to:
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To determine the relative effects (benefits and harms) of different designs, articulations, and fixation techniques of arthroplasties for treating hip fractures in adults.
This will include assessment of the relative effects (benefits and harms) of the following:
- THR versus HA
- Cemented versus uncemented arthroplasties
- Alternative articulation designs in THA
- Different designs of HA, e.g. bipolar versus unipolar
AIMS: The Biventricular vs Right Ventricular Pacing in Heart Failure Patients with Atrioventricular Block (BLOCK-HF) demonstrated that biventricular (BiV) pacing resulted in better clinical and structural outcomes compared to right ventricular (RV) pacing in patients with atrioventricular (AV) block and reduced left ventricular ejection fraction (LVEF; =50%). This study investigated the cost-effectiveness of BiV vs RV pacing in the patient population enrolled in the BLOCK-HF trial.
METHODS: All-cause mortality, New York Heart Association (NYHA) Class distribution over time, and NYHA-specific heart failure (HF)-related healthcare utilization rates were predicted using statistical models based on BLOCK-HF patient data. A proportion-in-state model calculated cost-effectiveness from the Medicare payer perspective.
RESULTS: The predicted patient survival was 6.78 years with RV and 7.52 years with BiV pacing, a 10.9% increase over lifetime. BiV pacing resulted in 0.41 more quality-adjusted life years (QALYs) compared to RV pacing, at an additional cost of $12,537. The "base-case" incremental cost-effectiveness ratio (ICER) was $30,860/QALY gained. Within the clinical sub-groups, the highest observed ICER was $43,687 (NYHA Class I). Patients receiving combined BiV pacing and defibrillation (BiV-D) devices were projected to benefit more (0.84 years gained) than BiV pacemaker (BiV-P) recipients (0.49 years gained), compared to dual-chamber pacemakers.
CONCLUSIONS: BiV pacing in AV block patients improves survival and attenuates HF progression compared to RV pacing. ICERs were consistently below the US acceptability threshold ($50,000/QALY). From a US Medicare perspective, the additional up-front cost associated with offering BiV pacing to the BLOCK-HF patient population appears justified.
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To assess the relative effects (benefits and harms) of all surgical treatments used in the management of extracapsular hip fractures in adults, using a network meta-analysis of randomised trials, and to generate a hierarchy of interventions according to their outcomes.
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To assess the relative effects (benefits and harms) of all surgical treatments used in the management of intracapsular hip fractures in adults, using a network meta-analysis of randomised trials, and to generate a hierarchy of interventions according to their outcomes.
BACKGROUND: People with diabetes are at increased risk of developing chronic kidney disease (CKD) and should undergo annual screening, but adherence is poor. A home urinalysis self-test has been developed to improve compliance with screening. The objective of this paper is to report on a clinical evaluation and economic analysis of home urinalysis self-testing.
RESEARCH DESIGN AND METHODS: People with diabetes who had not undergone screening within the previous 18 months were recruited to a single-arm clinical evaluation to assess the uptake and compliance of home urinalysis self-testing. An economic evaluation assessed the likely cost-consequences of the use of home urinalysis self-testing over a lifetime time horizon.
RESULTS: A total of 2,196 people with diabetes were contacted as part of the clinical evaluation. Of these, 695 people agreed to be sent a home urinalysis self-testing kit and 499 people completed and returned the test. Cost savings of £2,008 per person were estimated over a lifetime due to increased CKD diagnosis and reduced progression to end stage renal disease.
CONCLUSIONS:
Home urinalysis self-testing of ACR in people with diabetes is estimated to be a cost-effective use of NHS resources in England in people who would otherwise not comply with standard care.
OBJECTIVE: The objective of this study was to assess current Cochrane Review practice in identifying and incorporating information from clinical trial registers.
STUDY DESIGN AND SETTING: A cross-sectional study was conducted to assess a sample of new or updated intervention reviews from all Cochrane Review Groups up to February 1, 2017. Two assessors independently extracted data from each review using a pretested audit questionnaire. Data were analyzed relating to the frequency of reporting (1) the register source and search strategy; (2) the results of trial register searches; and (3) the use of trial register information in the review.
RESULTS: Over 90% (236/260) of Cochrane Reviews reported searching a trial register (e.g., ClinicalTrials.gov or the WHO International Clinical Trials Registry Platform). In reviews that reported trial register searches, 39% (92/236) indicated the number of trial records retrieved and 56.8% (134/236) used information from the trial register records in the review. Trial record information was incorporated into the results (39.6%; 53/134), risk of bias assessments (53.7%; 72/134), and discussion (24.6%, 33/134) and conclusion sections (25.4%, 34/134).
CONCLUSION: Most audited reviews used trial register information. Guidance may be needed to better incorporate information from these valuable resources in Cochrane Reviews to assist future research decisions made by funders and prospective study investigators.
Information specialists search resources to identify information to answer research questions. Typically, we investigate the questions, identify relevant sources of information that might answer those questions, search those sources and retrieve documents. We may also be involved in managing retrieved records and documents and sometimes we also select information from the records or documents that may be most relevant to the questions being asked. Sometimes information specialists produce summaries of the most relevant information.
Information specialists have been undertaking these tasks for decades and many textbooks and guidance documents are available to support them. New techniques are constantly developing which can help with information retrieval and it is important to assess and appreciate what they may offer. New text mining tools, which analyse the frequency and relationship of words in texts, are growing in number and availability. These tools may be particularly relevant and attractive to information specialists developing searches for systematic reviews since text mining can assist with developing searches for broad-based and hard to define topics. It can also provide opportunities to manage and process large volumes of records, which can be a challenge for some research questions.
The use of text mining is not yet standard practice in systematic review searching and indeed is not necessary for many non-complex topics. However, its potential is being recognised and explored and it has been subject to several recent scoping reviews in the health sector. In disciplines where systematic reviews are not yet a common method, an awareness of the potential value of text mining is likely to be similarly low. Outside of the systematic review context, information specialists should also find that text mining is useful for many tasks needing textual analysis, including assessing terminology in complex topics, for appreciating the concepts being covered within a specific literature and for undertaking a variety of citation analyses.
This chapter presents an overview of what text mining tools have to offer for all disciplines and their potential value to information specialists. Table 7.1 (pages 165-7) contains the names and website addresses of all the text mining tools referred to in this chapter.
