Literature DB >> 30869998

Performance of Cancer Recurrence Algorithms After Coding Scheme Switch From International Classification of Diseases 9th Revision to International Classification of Diseases 10th Revision.

Nikki M Carroll1, Debra P Ritzwoller1, Matthew P Banegas2, Maureen O'Keeffe-Rosetti2, Angel M Cronin3, Hajime Uno3,4, Mark C Hornbrook2, Michael J Hassett3,4.   

Abstract

PURPOSE: We previously developed and validated informatic algorithms that used International Classification of Diseases 9th revision (ICD9)-based diagnostic and procedure codes to detect the presence and timing of cancer recurrence (the RECUR Algorithms). In 2015, ICD10 replaced ICD9 as the worldwide coding standard. To understand the impact of this transition, we evaluated the performance of the RECUR Algorithms after incorporating ICD10 codes.
METHODS: Using publicly available translation tables along with clinician and other expertise, we updated the algorithms to include ICD10 codes as additional input variables. We evaluated the performance of the algorithms using gold standard recurrence measures associated with a contemporary cohort of patients with stage I to III breast, colorectal, and lung (excluding IIIB) cancer and derived performance measures, including the area under the receiver operating curve, average absolute prediction error, and correct classification rate. These values were compared with the performance measures derived from the validation of the original algorithms.
RESULTS: A total of 659 colorectal, 280 lung, and 2,053 breast cancer cases were identified. Area under the receiver operating curve derived from the updated algorithms was 89.0% (95% CI, 82.3% to 95.7%), 88.9% (95% CI, 79.3% to 98.2%), and 80.5% (95% CI, 72.8% to 88.2%) for the colorectal, lung, and breast cancer algorithms, respectively. Average absolute prediction errors for recurrence timing were 2.7 (SE, 11.3%), 2.4 (SE, 10.4%), and 5.6 months (SE, 21.8%), respectively, and timing estimates were within 6 months of actual recurrence for more than 80% of colorectal, more than 90% of lung, and more than 50% of breast cancer cases using the updated algorithm.
CONCLUSION: Performance measures derived from the updated and original algorithms had overlapping confidence intervals, suggesting that the ICD9 to ICD10 transition did not affect the RECUR Algorithm performance.

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Year:  2019        PMID: 30869998      PMCID: PMC6706070          DOI: 10.1200/CCI.18.00113

Source DB:  PubMed          Journal:  JCO Clin Cancer Inform        ISSN: 2473-4276


  18 in total

1.  Representativeness of participants in the cancer care outcomes research and surveillance consortium relative to the surveillance, epidemiology, and end results program.

Authors:  Paul J Catalano; John Z Ayanian; Jane C Weeks; Katherine L Kahn; Mary Beth Landrum; Alan M Zaslavsky; Jeannette Lee; Jane Pendergast; David P Harrington
Journal:  Med Care       Date:  2013-02       Impact factor: 2.983

2.  The Cancer Research Network: a platform for epidemiologic and health services research on cancer prevention, care, and outcomes in large, stable populations.

Authors:  Jessica Chubak; Rebecca Ziebell; Robert T Greenlee; Stacey Honda; Mark C Hornbrook; Mara Epstein; Larissa Nekhlyudov; Pamala A Pawloski; Debra P Ritzwoller; Nirupa R Ghai; Heather Spencer Feigelson; Heather A Clancy; V Paul Doria-Rose; Lawrence H Kushi
Journal:  Cancer Causes Control       Date:  2016-09-17       Impact factor: 2.506

3.  Detecting Lung and Colorectal Cancer Recurrence Using Structured Clinical/Administrative Data to Enable Outcomes Research and Population Health Management.

Authors:  Michael J Hassett; Hajime Uno; Angel M Cronin; Nikki M Carroll; Mark C Hornbrook; Debra Ritzwoller
Journal:  Med Care       Date:  2017-12       Impact factor: 2.983

4.  Validating ICD coding algorithms for diabetes mellitus from administrative data.

Authors:  Guanmin Chen; Nadia Khan; Robin Walker; Hude Quan
Journal:  Diabetes Res Clin Pract       Date:  2010-04-02       Impact factor: 5.602

5.  Building a virtual cancer research organization.

Authors:  Mark C Hornbrook; Gene Hart; Jennifer L Ellis; Donald J Bachman; Gary Ansell; Sarah M Greene; Edward H Wagner; Roy Pardee; Mark M Schmidt; Ann Geiger; Amy L Butani; Terry Field; Hassan Fouayzi; Irina Miroshnik; Liyan Liu; Robert Diseker; Karen Wells; Rick Krajenta; Lois Lamerato; Christine Neslund Dudas
Journal:  J Natl Cancer Inst Monogr       Date:  2005

6.  Estimation of the Number of Women Living with Metastatic Breast Cancer in the United States.

Authors:  Angela B Mariotto; Ruth Etzioni; Marc Hurlbert; Lynne Penberthy; Musa Mayer
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2017-05-18       Impact factor: 4.254

7.  Determining the Time of Cancer Recurrence Using Claims or Electronic Medical Record Data.

Authors:  Hajime Uno; Debra P Ritzwoller; Angel M Cronin; Nikki M Carroll; Mark C Hornbrook; Michael J Hassett
Journal:  JCO Clin Cancer Inform       Date:  2018-12

8.  Development, Validation, and Dissemination of a Breast Cancer Recurrence Detection and Timing Informatics Algorithm.

Authors:  Debra P Ritzwoller; Michael J Hassett; Hajime Uno; Angel M Cronin; Nikki M Carroll; Mark C Hornbrook; Lawrence C Kushi
Journal:  J Natl Cancer Inst       Date:  2018-03-01       Impact factor: 13.506

9.  The HMO Research Network Virtual Data Warehouse: A Public Data Model to Support Collaboration.

Authors:  Tyler R Ross; Daniel Ng; Jeffrey S Brown; Roy Pardee; Mark C Hornbrook; Gene Hart; John F Steiner
Journal:  EGEMS (Wash DC)       Date:  2014-03-24

10.  Metastatic Breast Cancer With ESR1 Mutation: Clinical Management Considerations From the Molecular and Precision Medicine (MAP) Tumor Board at Massachusetts General Hospital.

Authors:  Aditya Bardia; John A Iafrate; Tilak Sundaresan; Jerry Younger; Valentina Nardi
Journal:  Oncologist       Date:  2016-08-22
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  5 in total

1.  A Systematic Review of Estimating Breast Cancer Recurrence at the Population Level With Administrative Data.

Authors:  Hava Izci; Tim Tambuyzer; Krizia Tuand; Victoria Depoorter; Annouschka Laenen; Hans Wildiers; Ignace Vergote; Liesbet Van Eycken; Harlinde De Schutter; Freija Verdoodt; Patrick Neven
Journal:  J Natl Cancer Inst       Date:  2020-10-01       Impact factor: 13.506

2.  Heterogeneity introduced by EHR system implementation in a de-identified data resource from 100 non-affiliated organizations.

Authors:  Earl F Glynn; Mark A Hoffman
Journal:  JAMIA Open       Date:  2019-08-07

3.  Rectal NETs and rectosigmoid junction NETs may need to be treated differently.

Authors:  Wen Cai; Weiting Ge; Hanguang Hu; Jianshan Mao
Journal:  Cancer Med       Date:  2019-12-16       Impact factor: 4.452

4.  Adult and Elderly Risk Factors of Mortality in 23,614 Emergently Admitted Patients with Rectal or Rectosigmoid Junction Malignancy.

Authors:  Lior Levy; Abbas Smiley; Rifat Latifi
Journal:  Int J Environ Res Public Health       Date:  2022-07-27       Impact factor: 4.614

5.  Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study.

Authors:  Teresa A'mar; Jessica Chubak; Ruth Etzioni; J David Beatty; Catherine Fedorenko; Daniel Markowitz; Thomas Corey; Jane Lange; Stephen M Schwartz; Bin Huang
Journal:  JMIR Cancer       Date:  2020-08-17
  5 in total

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