Do Diagnostic and Procedure Codes Within Population-Based, Administrative Datasets Accurately Identify Patients with Rectal Cancer?
- 84 Downloads
Procedural and diagnostic codes may inaccurately identify specific patient populations within administrative datasets.
Measure the accuracy of previously used coding algorithms using administrative data to identify patients with rectal cancer resections (RCR).
Using a previously published coding algorithm, we re-created a RCR cohort within administrative databases, limiting the search to a single institution. The accuracy of this cohort was determined against a gold standard reference population. A systematic review of the literature was then performed to identify studies that use similar coding methods to identify RCR cohorts and whether or not they comment on accuracy.
Over the course of the study period, there were 664,075 hospitalizations at our institution. Previously used coding algorithms identified 1131 RCRs (administrative data incidence 1.70 per 1000 hospitalizations). The gold standard reference population was 821 RCR over the same period (1.24 per 1000 hospitalizations). Administrative data methods yielded a RCR cohort of moderate accuracy (sensitivity 89.5%, specificity 99.9%) and poor positive predictive value (64.9%). Literature search identified 18 studies that utilized similar coding methods to derive a RCR cohort. Only 1/18 (5.6%) reported on the accuracy of their study cohort.
The use of diagnostic and procedure codes to identify RCR within administrative datasets may be subject to misclassification bias because of low PPV. This underscores the importance of reporting on the accuracy of RCR cohorts derived within population-based datasets.
KeywordsRectal cancer Administrative data
All authors contributed to the concept and design of the study, as well as drafting and revising of the manuscript. All authors gave final approval for the manuscript. Authors Musselman, Gomes, Rothwell, Auer, and vanWalraven were directly involved in data collection and analysis.
This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC). The opinions, results, and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred. Parts of this material are based on data and information compiled and provided by Canadian Institute for Health Information (CIHI). However, the analyses, conclusions, opinions, and statements expressed herein are those of the author, and not necessarily those of CIHI.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflicts of interest.
- 8.Prentice RL. Surrogate endpoints in clinical trials: definition and operational criteria. Statistics in Medicine 1989, v8 n4, 431–440.Google Scholar
- 11.AJCC (American Joint Committee on Cancer) Cancer Staging Manual, 7th edition, Edge, SB, Byrd, DR, Compton, CC, et al (Eds), Springer, New York 2010. p.143Google Scholar
- 12.Wong R, Berry S, Spithoff K et al. Preoperative or Postoperative Therapy for the Management of Patients with Stage II or III rectal cancer: Guideline Recommendations. July, 2008. Available at https://www.cancercare.on.ca/cms/One.aspx?portalId=1377&pageId=10207. Accessed 08/07, 2013.
- 15.Juurlink D, Preyra C, Croxford R et al. Canadian Institute for Health Information Discharge Abstract Database: a validation study. Toronto: Institute for Clinical Evaluative Sciences; 2006.Google Scholar
- 28.Thompson BS, Coory MD, Lumley JW. National trends in the uptake of laparoscopic resection for colorectal cancer, 2000-2008. MJA 2011; 194: 443–447Google Scholar
- 37.The Center for Administrative Data Research. http://cadr.wustl.edu. Updated 2018. Accessed on 05/23, 2018.