It has been awhile since I called to collaborate and proposed to track results of clinical trials and studies. Unfortunately, nobody expressed an interest. So, starting from this year, I’ve attempted to do it myself. Today, I’m sharing results of the first 3 months of this experiment.
I track results of:
- published clinical trials;
- published not registered (as trials) clinical case studies (where number of patients >3);
- interim and final results of unpublished industry trials via press releases or/and conference reports.
In order to capture all trials and studies, I set “very loose” filter – PubMed RSS feed of “stem cell” term. I do “hand coding” and sort out all results manually.
- to determine usability of the proposed filter;
- to identify advantages/ disadvantages of of standard searching tools and find the best searching strategy;
- to determine a frequency of reporting results in cell therapy clinical studies;
- to determine a value of registered trials with ID among all reports;
- to set an example for potential crowdsourcing and collaboration.
I routinely use PubMed RSS feeds in order to capture everything in stem cell/ cell therapy/ regenerative medicine field. I found that “stem cell” feed is the best option to capture all published reports. Unexpectedly, this filter also captures almost all cell therapy trials (even without indication to stem cells), for example, cellular immunotherapy. Disadvantage of this filter is low selectivity – every week there is about 500-700 articles. It’s very time consuming.
To capture press releases from companies, I was using simple Google search for “news” by keywords: “stem cell”, “cell therapy”, “regenerative medicine”.
I’ve tried standard searching tools, used by many data miners, such as “clinical trial” filter in PubMed. It didn’t work. For the first quarter of 2014, search in PubMed by a query:
- “stem cell” with filter “clinical trial” yielded only 1 result
- “cell therapy” with filter “clinical trial” yielded 2 results
- “mesenchymal” with filter “clinical trial” yielded 2 results
Using my RSS filter + Google search, I was able to track 41 clinical studies reports (Q1 of 2014), including:
- 38 published trials and studies and
- 3 companies press releases
24 of 41 (59%) studies had trial IDs and were registered in databases. 7 of 41 studies were categorized as “case series”. I was not able to identify trial ID in 10 of 41 (24%) reports.
As an example of tracking, I’m sharing raw data for the first quarter of 2014 – you can see it here (you can view, but not edit this spreadsheet).
- PubMed RSS feed for “stem cell” is the best tool to capture all reports for clinical studies results in cell therapy/ regenerative medicine. It’s not automated, required “hand coding” and very time consuming.
- Standard PubMed search, using “clinical trial” filter misses about 95% of reports.
- The frequency of reporting results for Q1 of 2014 was 1 report every 2.2 days. Basically, every 3 days somebody around the world reports cell therapy clinical studies results.
- More than half reports (~60%) were associated with defined trial ID and registered in international databases.
- Capturing cell therapy trials and clinical studies results is a huge task and required collaborative efforts. This post is a snapshot of the first 3 months of 2014 and call for collaboration and crowdsourcing of the data.
- The best available tools for capturing reports are manual and not sensitive. Therefore, there is a big demand for improving strategy for capturing cell therapy reports. Crowdsourcing can provide a common strategy or an algorithm set by consensus.
- Analysis of results will allow to define: failure/ success rates by phase of trial, rate and time of results reporting, trends in cell types, trial type, geographical distribution and so on.
I’d be happy to hear any opinions and suggestions for improvement and collaboration on this project.