New and emerging technologies for the diagnosis and monitoring of chronic obstructive pulmonary disease: A horizon scanning review.

There is a need for straightforward, novel diagnostic and monitoring technologies to enable the early diagnosis of COPD and its differentiation from other respiratory diseases, to establish the cause of acute exacerbations and to monitor disease progression. We sought to establish whether technologies already in development could potentially address these needs. A systematic horizon scanning review was undertaken to identify technologies in development from a wide range of commercial and non-commercial sources. Technologies were restricted to those likely to be available within 18 months, and then evaluated for degree of innovation, potential for impact, acceptability to users and likelihood of adoption by clinicians and patients with COPD. Eighty technologies were identified, of which 25 were considered particularly promising. Biomarker tests, particularly those using sputum or saliva samples and/or available at the point of care, were positively evaluated, with many offering novel approaches to early diagnosis and to determining the cause for acute exacerbations. Several wrist-worn devices and smartphone-based spirometers offering the facility for self-monitoring and early detection of exacerbations were also considered promising. The most promising identified technologies have the potential to improve COPD care and patient outcomes. Further research and evaluation activities should be focused on these technologies.

PageRank as a method to rank biomedical literature by importance.

BACKGROUND: Optimal ranking of literature importance is vital in overcoming article overload. Existing ranking methods are typically based on raw citation counts, giving a sum of 'inbound' links with no consideration of citation importance. PageRank, an algorithm originally developed for ranking webpages at the search engine, Google, could potentially be adapted to bibliometrics to quantify the relative importance weightings of a citation network. This article seeks to validate such an approach on the freely available, PubMed Central open access subset (PMC-OAS) of biomedical literature. RESULTS: On-demand cloud computing infrastructure was used to extract a citation network from over 600,000 full-text PMC-OAS articles. PageRanks and citation counts were calculated for each node in this network. PageRank is highly correlated with citation count (R = 0.905, P < 0.01) and we thus validate the former as a surrogate of literature importance. Furthermore, the algorithm can be run in trivial time on cheap, commodity cluster hardware, lowering the barrier of entry for resource-limited open access organisations. CONCLUSIONS: PageRank can be trivially computed on commodity cluster hardware and is linearly correlated with citation count. Given its putative benefits in quantifying relative importance, we suggest it may enrich the citation network, thereby overcoming the existing inadequacy of citation counts alone. We thus suggest PageRank as a feasible supplement to, or replacement of, existing bibliometric ranking methods.