ArticleJCO clinical cancer informatics2019
Performance of Cancer Recurrence Algorithms After Coding Scheme Switch From International Classification of Diseases 9th Revision to International Classification of Diseases 10th Revision.
Article in JCO clinical cancer informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Estimating Breast Cancer Recurrence at the Population Level With Administrative Data.Journal of the National Cancer Institute · 2020Pooled it
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- Diagnostic framework to validate clinical machine learning models locally on temporally stamped data.Communications medicine · 2025Article
- Using Electronic Health Records to Classify Cancer Site and Metastasis.Applied clinical informatics · 2025Article
- Patterns of recurrence among adults diagnosed with screen-detected lung cancer.Cancer epidemiology · 2025Article
- Evaluation of Algorithms Using Automated Health Plan Data to Identify Breast Cancer Recurrences.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2024Article
- Estimated incidence of disruptions to event-free survival from non-metastatic cancers in New South Wales, Australia - a population-wide epidemiological study of linked cancer registry and treatment data.Frontiers in oncology · 2024Article
- Real-World Patient Characteristics, Utilization Patterns, and Outcomes of US Patients with HR+, HER2- Metastatic Breast Cancer Treated with Abemaciclib.Drugs - real world outcomes · 2022Article
- Adult and Elderly Risk Factors of Mortality in 23,614 Emergently Admitted Patients with Rectal or Rectosigmoid Junction Malignancy.International journal of environmental research and public health · 2022Article
- Regarding the Utility of Unstructured Data and Natural Language Processing for Identification of Breast Cancer Recurrence.JCO clinical cancer informatics · 2021Article
- Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study.JMIR cancer · 2020Article
- Rectal NETs and rectosigmoid junction NETs may need to be treated differently.Cancer medicine · 2020Article
- Heterogeneity introduced by EHR system implementation in a de-identified data resource from 100 non-affiliated organizations.JAMIA open · 2019Article
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Authors and funding
8 authors.
Funding
Abstract
purposeWe 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.
methodsUsing 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.
resultsA 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.
conclusionPerformance 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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