Dynamic early identification of hip replacement implants with high revision rates. Study based on the NJR data from UK during 2004-2012

Begun, Alexander, MacGregor, Alexander J., Pchejetski, Dmitri and Kulinskaya, Elena (2020) Dynamic early identification of hip replacement implants with high revision rates. Study based on the NJR data from UK during 2004-2012. PLoS One, 15 (8). ISSN 1932-6203

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Abstract

BACKGROUND: Hip replacement and hip resurfacing are common surgical procedures with an estimated risk of revision of 4% over 10 year period. Approximately 58% of hip replacements will last 25 years. Some implants have higher revision rates and early identification of poorly performing hip replacement implant brands and cup/head brand combinations is vital. AIMS: Development of a dynamic monitoring method for the revision rates of hip implants. METHODS: Data on the outcomes following the hip replacement surgery between 2004 and 2012 was obtained from the National Joint Register (NJR) in the UK. A novel dynamic algorithm based on the CUmulative SUM (CUSUM) methodology with adjustment for casemix and random frailty for an operating unit was developed and implemented to monitor the revision rates over time. The Benjamini-Hochberg FDR method was used to adjust for multiple testing of numerous hip replacement implant brands and cup/ head combinations at each time point. RESULTS: Three poorly performing cup brands and two cup/ head brand combinations have been detected. Wright Medical UK Ltd Conserve Plus Resurfacing Cup (cup o), DePuy ASR Resurfacing Cup (cup e), and Endo Plus (UK) Limited EP-Fit Plus Polyethylene cup (cup g) showed stable multiple alarms over the period of a year or longer. An addition of a random frailty term did not change the list of underperforming components. The model with added random effect was more conservative, showing less and more delayed alarms. CONCLUSIONS: Our new algorithm is an efficient method for early detection of poorly performing components in hip replacement surgery. It can also be used for similar tasks of dynamic quality monitoring in healthcare.

Item Type: Article
Uncontrolled Keywords: biochemistry, genetics and molecular biology(all),agricultural and biological sciences(all),general ,/dk/atira/pure/subjectarea/asjc/1300
Faculty \ School: Faculty of Science > School of Computing Sciences
Faculty of Medicine and Health Sciences > Norwich Medical School
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Depositing User: LivePure Connector
Date Deposited: 14 Aug 2020 23:52
Last Modified: 24 Sep 2020 00:03
URI: https://ueaeprints.uea.ac.uk/id/eprint/76449
DOI: 10.1371/journal.pone.0236701

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