ECTS Abstracts (2015) 1 P12

Identification of Antithrombotic Drugs Related to Total Joint Replacement using Anonymised Free Text Notes

Johannes Nielen1,2, Bart van den Bemt3,4, Andrea Burden5,6, Annelies Boonen7, Pieter C Dagnelie1, Pieter Emans8, Nicole Veldhorst6, Arief Lalmohamed2,9, Tjeerd-Pieter van Staa2 & Frank de Vries2,6

1Department of Epidemiology, Maastricht University, Maastricht, The Netherlands; 2Division of Pharmacoepidemiology & Clinical Pharmacology, Utrecht University, Utrecht, The Netherlands; 3Department of Pharmacy, Sint Maartenskliniek, Nijmegen, The Netherlands; 4Department of Pharmacy, Radboud University Medical Centre, Nijmegen, The Netherlands; 5School Caphri, Maastricht University, Maastricht, The Netherlands; 6Department of Clinical Pharmacy and Toxicology, Maastricht University Medical Center, Maastricht, The Netherlands; 7Department of Rheumatology, Maastricht University Medical Center, Maastricht, The Netherlands; 8Department of Orthopaedics, Maastricht University Medical Center, Maastricht, The Netherlands; 9Department of Clinical Pharmacy, University Medical Center Utrecht, Utrecht, The Netherlands.

Background: Intensive antithrombotic treatment, predominantly dispensed at the hospital, is recommended to prevent venous thromboembolic events after total joint replacement (TJR). Unfortunately, hospital prescription data is often lacking in general practitioner databases, thereby limiting the applicability of these rich databases to study long-term effects in real practice. Un-coded anonymised free text from hospital discharge letters may be used to collect additional information.

Objective: To design and test a method to extract additional information on anticoagulation therapy in patients undergoing TJR from anonymised free text notes in the Clinical Practice Research Datalink (CPRD).

Methods: Anticoagulant drug use related to total hip (THR) or knee replacement (TKR) was identified using both anonymised free text and prescription data. Internal validity of our newly designed method was determined by calculating positive predictive values (PPVs) of hits for predefined keywords in a random sample of anonymised free text notes. In order to determine potential detection bias, TJR patient characteristics were compared with regard to their status of exposure to antithrombotics.

Results: PPVs ranging between 97% - 99% for NOAC or LMWH exposure related to TJR were obtained with our method. Our algorithm increased detection rates by 57%, yielding a total proportion of 18.5% of all THR and 18.6% of all TKR surgeries. Identified users of NOACs and LMWHs were largely similar with regards to age, sex, lifestyle, disease and drug history compared with patients without identified drug use.

Conclusion: We have developed a useful method to identify additional exposure to NOACs or LMWHs with TJR surgery.

Disclosure: The authors declared no competing interests.