Cross‐sectional observational study of the availability of evidence supporting novel implantable devices used in gastrointestinal surgery. Issue 6 (20th February 2017) - Chapman, S. J.; Shelton, B.; Maruthappu, M.; Singh, P.; McCulloch, P.; Bhangu, A.
Abstract: Background: Evidence supporting the implementation of novel surgical devices is unstandardized, despite recommendations for assessing novel innovations. This study aimed to determine the proportion of novel implantable devices used in gastrointestinal surgery that are supported by evidence from RCTs. Methods: A list of novel implantable devices placed intra‐abdominally during gastrointestinal surgery was produced. Systematic searches were performed for all devices via PubMed and clinical trial registries. The primary outcome measure was the availability of at least one published RCT for each device. Published RCTs were appraised using the Cochrane tool for assessing risk of bias. Results: A total of 116 eligible devices were identified (implantable mesh 42, topical haemostatics 22, antiadhesion barriers 10, gastric bands 8, suture and staple‐line reinforcement 7, artificial sphincters 5, other 22). One hundred and twenty‐eight published RCTs were found for 33 of 116 devices (28·4 per cent). Most were assessed as having a high risk of bias, with only 12 of 116 devices (10·3 per cent) supported by a published RCT considered to be low risk. A further 95 ongoing and 23 unpublished RCTs were identified for 42 of 116 devices (36·2 per cent), but many (64 of 116, 55·2 per cent) had no evidence from published, ongoing or unpublished RCTs. The highest stage of innovation according to the IDEAL Framework was stage 1 for 11 devices, stage 2a for 23 devices, stage 2b for one device and stage 3 for 33 devices. The remaining 48 devices had no relevant clinical evidence. Conclusion: Only one in ten novel implantable devices available for use in gastrointestinal surgical practice is supported by high‐quality RCT evidence. Abstract : Over‐reliance on insufficient and poor‐quality data