Exploring in-hospital adverse drug events using ICD-10 codes
Sumit Parikh A F , Donna Christensen B , Peter Stuchbery C , Jenny Peterson B , Anastasia Hutchinson A E and Terri Jackson A DA Northern Clinical Research Centre, Northern Health, 185 Cooper Street, Epping, Vic. 3076, Australia. Email: anastasia.hutchinson@nh.org.au
B Quality, Safety and Risk Management Unit, Northern Health, 185 Cooper Street, Epping, Vic. 3076, Australia. Email: Donna.Christensen@nh.org.au; Jenny.Peterson@nh.org.au
C Pharmacy Services, Northern Health, 185 Cooper Street, Epping, Vic. 3076, Australia. Email: Peter.Stuchbery@nh.org.au
D School of Population Health, The University of Melbourne, 207 Bouverie Street, Parkville, Vic. 3010, Australia. Email: Terri.Jackson@unimelb.edu.au
E School of Nursing & Midwifery, Deakin University, 221 Burwood Highway, Burwood, Vic. 3125, Australia.
F Corresponding author. Email: sumit.parikh@nh.org.au
Australian Health Review 38(4) 454-460 https://doi.org/10.1071/AH13166
Submitted: 3 September 2013 Accepted: 2 March 2014 Published: 29 May 2014
Abstract
Objective Adverse drug events (ADEs) during hospital admissions are a widespread problem associated with adverse patient outcomes. The ‘external cause’ codes in the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10) provide opportunities for identifying the incidence of ADEs acquired during hospital stays that may assist in targeting interventions to decrease their occurrence. The aim of the present study was to use routine administrative data to identify ADEs acquired during hospital admissions in a suburban healthcare network in Melbourne, Australia.
Methods Thirty-nine secondary diagnosis fields of hospital discharge data for a 1-year period were reviewed for ‘diagnoses not present on admission’ and assigned to the Classification of Hospital Acquired Diagnoses (CHADx) subclasses. Discharges with one or more ADE subclass were extracted for retrospective analysis.
Results From 57 205 hospital discharges, 7891 discharges (13.8%) had at least one CHADx, and 402 discharges (0.7%) had an ADE recorded. The highest proportion of ADEs was due to administration of analgesics (27%) and systemic antibiotics (23%). Other major contributors were anticoagulation (13%), anaesthesia (9%) and medications with cardiovascular side-effects (9%).
Conclusion Hospital data coded in ICD-10 can be used to identify ADEs that occur during hospital stays and also clinical conditions, therapeutic drug classes and treating units where these occur. Using the CHADx algorithm on administrative datasets provides a consistent and economical method for such ADE monitoring.
What is known about the topic? Adverse drug events (ADEs) can result in several different physical consequences, ranging from allergic reactions to death, thereby posing a significant burden on patients and the health system. Numerous studies have compared manual, written incident reporting systems used by hospital staff with computerised automated systems to identify ADEs acquired during hospital admissions. Despite various approaches aimed at improving the detection of ADEs, they remain under-reported, as a result of which interventions to mitigate the effect of ADEs cannot be initiated effectively.
What does this paper add? This research article demonstrates major methodological advances over comparable published studies looking at the effectiveness of using routine administrative data to monitor rates of ADEs that occur during a hospital stay and reviews the type of ADEs and their frequency patterns during patient admission. It also provides an insight into the effect of ADEs that occur within different hospital treating units. The method implemented in this study is unique because it uses a grouping algorithm developed for the Australian Commission on Safety and Quality in Health Care (ACSQHC) to identify ADEs not present on admission from patient data coded in ICD-10. This algorithm links the coded external causes of ADEs with their consequences or manifestations. ADEs identified through the use of programmed code based on this algorithm have not been studied in the past and therefore this paper adds to previous knowledge in this subject area.
What are the implications for health professionals? Although not all ADEs can be prevented with current medical knowledge, this study can assist health professionals in targeting interventions that can efficiently reduce the rate of ADEs that occur during a hospital stay, and improve information available for future medication management decisions.
Additional keywords: adverse drug reaction reporting systems, drug toxicity classification, drug toxicity diagnosis, international classification of diseases, patient admission, prescriptions, statistics and numerical data.
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