Mahar AL, Jeong Y, Zagorski B, Coburn N. Validating an algorithm to identify metastatic gastric cancer in the absence of routinely collected TNM staging data.
BMC Health Serv Res 2018;
18:309. [PMID:
29716600 PMCID:
PMC5930789 DOI:
10.1186/s12913-018-3125-7]
[Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 04/16/2018] [Indexed: 12/16/2022] Open
Abstract
Background
Accurate TNM stage information is essential for cancer health services research, but is often impractical and expensive to collect at the population-level. We evaluated algorithms using administrative healthcare data to identify patients with metastatic gastric cancer.
Methods
A population-based cohort of gastric cancer patients diagnosed between 2005 and 2007 identified from the Ontario Cancer Registry were linked to routinely collected healthcare data. Reference standard data identifying metastatic disease were obtained from a province-wide chart review, according to the Collaborative Staging method. Algorithms to identify metastatic gastric cancer were created using administrative healthcare data from hospitalization, emergency department, and physician billing records. Time frames of data collection in the peri-diagnosis period, and the diagnosis codes used to identify metastatic disease were varied. Algorithm sensitivity, specificity, and accuracy were evaluated.
Results
Of 2366 gastric cancer patients, included within the chart review, 54.3% had metastatic disease. Algorithm sensitivity ranged from 50.0- 90%, specificity ranged from 27.6 - 92.5%, and accuracy from 61.5 - 73.4%. Sensitivity and specificity were maximized when the most conservative list of diagnosis codes from hospitalization and outpatient records in the six months prior to and the six months following diagnosis were included.
Conclusion
Algorithms identifying metastatic gastric cancer can be used for research purposes using administrative healthcare data, although they are imperfect measures. The properties of these algorithms may be generalizable to other high fatality cancers and other healthcare systems. This study provides further support for the collection of population-based, TNM stage data.
Electronic supplementary material
The online version of this article (10.1186/s12913-018-3125-7) contains supplementary material, which is available to authorized users.
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