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BMC Health Services Research
Published in: BMC Health Services Research 1/2018

Open Access 01-12-2018 | Research article

Validating an algorithm to identify metastatic gastric cancer in the absence of routinely collected TNM staging data

Authors: Alyson L. Mahar, Yunni Jeong, Brandon Zagorski, Natalie Coburn

Published in: BMC Health Services Research | Issue 1/2018

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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.
Appendix
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Literature
1.
Brierley JD, Srigley JR, Yurcan M, Li B, Rahal R, Ross J, King MJ, Sherar M, Skinner R, Sawka C. The value of collecting population-based cancer stage data to support decision-making at organizational, regional and population levels. Healthcare quarterly (Toronto, Ont). 2013;16(3):27–33.CrossRef
2.
Falcaro M, Carpenter JR. Correcting bias due to missing stage data in the non-parametric estimation of stage-specific net survival for colorectal cancer using multiple imputation. Cancer Epidemiol. 2017;48:16–21.CrossRefPubMed
3.
Butler J, Foot C, Bomb M, Hiom S, Coleman M, Bryant H, Vedsted P, Hanson J, Richards M. The international Cancer benchmarking partnership: an international collaboration to inform cancer policy in Australia, Canada, Denmark, Norway, Sweden and the United Kingdom. Health Policy. 2013;112(1–2):148–55.CrossRefPubMed
4.
Maringe C, Walters S, Rachet B, Butler J, Fields T, Finan P, Maxwell R, Nedrebo B, Pahlman L, Sjovall A, et al. Stage at diagnosis and colorectal cancer survival in six high-income countries: a population-based study of patients diagnosed during 2000-2007. Acta oncologica. 2013;52(5):919–32.CrossRefPubMed
5.
Walters S, Maringe C, Coleman MP, Peake MD, Butler J, Young N, Bergstrom S, Hanna L, Jakobsen E, Kolbeck K, et al. Lung cancer survival and stage at diagnosis in Australia, Canada, Denmark, Norway, Sweden and the UK: a population-based study, 2004-2007. Thorax. 2013;68(6):551–64.CrossRefPubMed
6.
Benitez-Majano S, Fowler H, Maringe C, Di Girolamo C, Rachet B. Deriving stage at diagnosis from multiple population-based sources: colorectal and lung cancer in England. Br J Cancer. 2016;115(3):391–400.CrossRefPubMedPubMedCentral
7.
Luo Q, Egger S, Yu XQ, Smith DP, O'Connell DL. Validity of using multiple imputation for "unknown" stage at diagnosis in population-based cancer registry data. PLoS One. 2017;12(6):e0180033.CrossRefPubMedPubMedCentral
8.
Ostenfeld EB, Froslev T, Friis S, Gandrup P, Madsen MR, Sogaard M. Completeness of colon and rectal cancer staging in the Danish Cancer registry, 2004-2009. Clinical epidemiology. 2012;4(Suppl 2):33–8.CrossRefPubMedPubMedCentral
9.
Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, Sorensen HT, von Elm E, Langan SM. The REporting of studies conducted using observational routinely-collected health data (RECORD) statement. PLoS Med. 2015;12(10):e1001885.CrossRefPubMedPubMedCentral
10.
van Walraven C, Bennett C, Forster AJ. Administrative database research infrequently used validated diagnostic or procedural codes. J Clin Epidemiol. 2011;64(10):1054–9.CrossRefPubMed
11.
Whyte JL, Engel-Nitz NM, Teitelbaum A, Gomez Rey G, Kallich JD. An evaluation of algorithms for identifying metastatic breast, lung, or colorectal Cancer in administrative claims data. Med Care. 2015;53(7):e49–57.CrossRefPubMed
12.
13.
Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359–86.CrossRefPubMed
14.
Dixon M, Mahar AL, Helyer LK, Vasilevska-Ristovska J, Law C, Coburn NG. Prognostic factors in metastatic gastric cancer: results of a population-based, retrospective cohort study in Ontario. Gastric Cancer. 2016;19(1):150–9.CrossRefPubMed
15.
16.
National Cancer Registration and Analysis Service. Stage Breakdown by CCG 2014. London: NCRAS; 2016.
17.
Northern Ireland Cancer Registry. Incidence by stage 2010–2014. Belfast: Queens University Belfast; 2016.
18.
ISD Scotland. Detect Cancer early staging data. Scotland: ISD; 2016.
19.
American Joint Committee on Cancer. AJCC staging manual. 7th ed. Chicago: Spring; 2012.
20.
Brooks GA, Landrum MB, Keating NL. An administrative stage inference algorithm for use in patients receiving chemotherapy for colorectal cancer. J Clin Oncol. 2017;35:e18121.
21.
Lash TL, Riis AH, Ostenfeld EB, Erichsen R, Vyberg M, Thorlacius-Ussing O. A validated algorithm to ascertain colorectal cancer recurrence using registry resources in Denmark. Int J Cancer. 2015;136(9):2210–5.CrossRefPubMed
22.
Chubak J, Pocobelli G, Weiss NS. Tradeoffs between accuracy measures for electronic health care data algorithms. J Clin Epidemiol. 2012;65(3):343–349.e342.CrossRefPubMed
23.
Lash TL, Fox MP, MacLehose RF, Maldonado G, McCandless LC, Greenland S. Good practices for quantitative bias analysis. Int J Epidemiol. 2014;43(6):1969–85.CrossRefPubMed
24.
Murphree D, Ngufor C, Upadhyaya S, Madde N, Clifford L, Kor DJ, Pathak J. Ensemble learning approaches to predicting complications of blood transfusion. Conf Proc IEEE Eng Med Biol Soc Ann Conf. 2015;2015:7222–5.
25.
Howlader N, Noone AM, Krapcho M, Garshell J, Miller D, Altekruse SF, Kosary CL, Yu M, Ruhl J, Tatalovich Z, et al. SEER Cancer statistics review, 1975-2011, based on November 2013 SEER data submission. Bethesda: National Cancer Institute; 2014.
Metadata
Title
Validating an algorithm to identify metastatic gastric cancer in the absence of routinely collected TNM staging data
Authors
Alyson L. Mahar
Yunni Jeong
Brandon Zagorski
Natalie Coburn
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2018
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-018-3125-7

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