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The Validity of the Rx-Risk Comorbidity Index Using Medicines Mapped to the Anatomical Therapeutic Chemical (ATC) Classification System

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The Validity of the Rx-Risk Comorbidity Index Using Medicines Mapped to the Anatomical Therapeutic Chemical (ATC) Classification System Open Access Research BMJ Open: first published as 10.1136/bmjopen-2017-021122 on 13 April 2018. Downloaded from The validity of the Rx-Risk Comorbidity Index using medicines mapped to the Anatomical Therapeutic Chemical (ATC) Classification System Nicole L Pratt, Mhairi Kerr, John D Barratt, Anna Kemp-Casey, Lisa M Kalisch Ellett, Emmae Ramsay, Elizabeth Ellen Roughead To cite: Pratt NL, Kerr M, ABSTRACT Strength and limitations of this study Barratt JD, et al. The validity Objectives To provide a map of Anatomical Therapeutic of the Rx-Risk Comorbidity Chemical (ATC) Classification System codes to individual ► This study provides an up-to-date list of medicines Index using medicines mapped Rx-Risk comorbidities and to validate the Rx-Risk to the Anatomical Therapeutic identified by Anatomical Therapeutic Chemical (ATC) Comorbidity Index. Chemical (ATC) Classification codes mapped to individual Rx-Risk categories. Design The 46 comorbidities in the Rx-Risk Index were System. BMJ Open ► Rx-Risk mapped to ATC codes can be easily adapted mapped to dispensing’s indicative of each condition 2018;8:e021122. doi:10.1136/ for use in other health systems making this index a using ATC codes. Prescription dispensing claims in 2014 bmjopen-2017-021122 useful resource for researchers worldwide. were used to calculate the Rx-Risk. A baseline logistic ► The Rx-Risk Index has been updated and mapped ► Prepublication history for regression model was fitted using age and gender as this paper is available online. to ATC codes based on medicine availability in covariates. Rx-Risk was added to the base model as an (1) To view these files, please visit Australia; hence, modifications may be required for unweighted score, (2) weighted score and as (3) individual the journal online (http:// dx. doi. use in other health systems. comorbidity categories indicating the presence or absence org/ 10. 1136/ bmjopen- 2017- ► This study was limited to patients over 65 years of of each condition. The Akaike information criterion and 021122). age, so Rx-Risk category weights derived in this c-statistic were used to compare the models. study may not be applicable to younger populations. Received 12 December 2017 Setting Models were developed in the Australian Revised 15 March 2018 Government Department of Veterans’ Affairs health claims Accepted 20 March 2018 data, and external validation was undertaken in a 10% http://bmjopen.bmj.com/ sample of the Australian Pharmaceutical Benefits Scheme for multiple comorbidities are required in Data. order to make valid comparisons between Participants Subjects aged 65 years or older. treatments. The Rx-risk is a measure for Outcome measures Death within 1 year (eg, 2015). determining an individual’s current comor- Results Compared with the base model (c-statistic 0.738, bidities based on their prescription medicine 95% CI 0.734 to 0.742), including Rx-Risk improved dispensing.4 5 It was initially developed for prediction of mortality; unweighted score 0.751, 95% CI 6 0.747 to 0.754, weighted score 0.786, 95% CI 0.782 to predicting costs of healthcare and was subse- 0.789 and individual comorbidities 0.791, 95% CI 0.788 quently adapted to predict mortality in outpa- on September 27, 2021 by guest. Protected copyright. 7 8 to 0.795. External validation confirmed the utility of the tient populations. Rx-risk has been found weighted index (c-statistic=0.833). to be a better predictor of 1-year and 3-year Conclusions The updated Rx-Risk Comorbidity Score mortality (1-year mortality: weighted Rx-risk was predictive of 1-year mortality and may be useful in c-statistic=0.728, 95% CI 0.723 to 0.733, 3-year practice to adjust for confounding in observational studies mortality: 0.731, 95% CI 0.728 to 0.734) using medication claims data. compared with simple prescription counts in the same time periods (1-year mortality: 0.715, 95% CI 0.710 to 0.720, 3-year mortality: INTRODUCTIOn 0.718, 95% CI 0.715 to 0.721).9 The prevalence of multimorbidity in the The first pharmacy-based measure of Quality Use of Medicines and population is increasing,1 2 and patients with comorbidity, developed in 1992, was the Pharmacy Research Centre, 4 University of South Australia, multiple conditions are at greater risk of Chronic Disease Score (CDS), consisting 3 Adelaide, South Australia, adverse outcomes. In observational studies, of 17 comorbidity categories. The CDS was Australia in which the aim is to determine the asso- subsequently updated and renamed in 2003 ciation between medicine use and adverse as the Rx-Risk-V Index, consisting of 45 cate- Correspondence to 10 Associate Professor Nicole events, adjustment for multimorbidity is gories of comorbidity. The Rx-Risk-V Index L Pratt; required to avoid biased results. Reliable was then adapted to only include comorbidi- nicole. pratt@ unisa. edu. au methods for identifying and controlling ties for which a medicine could be prescribed Pratt NL, et al. BMJ Open 2018;8:e021122. doi:10.1136/bmjopen-2017-021122 1 Open Access BMJ Open: first published as 10.1136/bmjopen-2017-021122 on 13 April 2018. Downloaded from and therefore could be applied to prescription claims data basic demographic information regarding gender, year resulting in an index based on 42 categories of comor- of birth and year of death. It is maintained by the Austra- bidity.11 Due to continual advances in pharmaceutical lian Government Department of Human Services. The disease management and as new medicines are used to current treatment population consists of 1 346 340 treat particular diseases, for example, treatment for hepa- members of the Australian community. The median age titis B and C, the Rx-Risk requires periodical updating and of the PBS treatment population is 43 years and 48% revalidation. Additionally, the original published weights are men. for the Rx-Risk Score were calculated by predicting cost of treatment rather than risk of death which is a more Patient and public involvement clinically relevant outcome.6 A list of Rx-Risk comorbid- Patients and public were not involved in this research. ities and their corresponding medicines mapped to a standardised international coding system has not been Study population published previously and would facilitate use of the index The DVA study population included individuals with at across health systems. Other comorbidity scores such as least one healthcare encounter in the 6-month period the Elixhauser and Charlson Index12 13 require diagnostic from 1 July 2013 to 31 December 2013. A healthcare information in their construction. The advantage of the encounter included any of the following: a medication Rx-Risk is that it requires prescription data only and dispensing, a doctor’s visit or hospitalisation. Analysis was provides researchers with the ability to measure comor- restricted to veterans who were DVA Gold Card holders bidity even in a predominately outpatient setting. prior to 1 July 2013 (ensuring they were eligible for all The aim of this study was to facilitate the use of the DVA subsidised services; thus, the dataset captured all Rx-Risk in practice by providing a list of the individual their health claims), and were between the ages of 65 Rx-isk categories mapped, using clinical expertise, to and 100 years at 1 January 2015 (n=135 406). Inclusion WHO’s Anatomical Therapeutic Chemical (ATC) Clas- criteria for the PBS cohort were people with a healthcare sification System14 and to determine the validity of the encounter (defined as at least one medicine dispensing) Rx-Risk Index, in predicting 1-year mortality in an outpa- in the 6-month period from 1 July 2013 to 31 December tient population. 2013, who were aged between 65 and 100 years at 1 January 2015 (n=303 135). The primary outcome for this study was death recorded METHOD in 2015; hence, patients were only included if they were Rx-Risk Index mapping alive as at 1 January 2015. Rx-Risk Scores were calculated The Rx-Risk Index consists of 46 comorbidity categories. separately in each dataset using prescription claims for For each Rx-risk category, medicines indicative of that supplied medicines over a 1-year baseline period between http://bmjopen.bmj.com/ condition were mapped (see table 1). This mapping was 1 January 2014 and 31 December 2014. performed at the ATC classification level and performed by consensus between two pharmacists. If an individual Calculating Rx-Risk Scores and prescription counts had ≥1 dispensing for a medicine in a given category, The full Rx-Risk Index has 46 comorbidity categories; then they were considered to have been treated (using however, tuberculosis and hepatitis B and C were removed medicines) for that comorbidity. Medications in both from the Rx-Risk Index for this study as the number the Department of Veterans’ Affairs (DVA) and Pharma- of individuals with these conditions in the DVA cohort ceutical Benefits Scheme (PBS) datasets are coded using was less than 10 so weights could not be generated. This on September 27, 2021 by guest. Protected copyright. WHO ATC Classification System14 and PBS item codes.15 resulted in 43 categories in the validation study. The following forms of the Rx-Risk Score were generated. The Data sources unweighted Rx-Risk Score was calculated as the count of The primary data source was the Australian Government the number of different comorbidity categories for which DVA’s administrative claims database. This database an individual was treated with a possible score ranging contains details of all prescription medicines, medical from 0 to 43. The weighted Rx-Risk Score was calculated and allied health services, and hospitalisations subsi- by adding the 43 indicator variables to a logistic regres- dised by DVA.
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