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Validation of International Classification of Diseases coding for bone metastases in electronic health records using technology-enabled abstraction

Authors Liede A, Hernandez RK, Roth M, Calkins G, Larrabee K, Nicacio L

Received 16 July 2015

Accepted for publication 9 September 2015

Published 11 November 2015 Volume 2015:7 Pages 441—448

DOI https://doi.org/10.2147/CLEP.S92209

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Ivy Shiue

Peer reviewer comments 3

Editor who approved publication: Professor Vera Ehrenstein


Alexander Liede,1 Rohini K Hernandez,1 Maayan Roth,2 Geoffrey Calkins,2 Katherine Larrabee,2 Leo Nicacio2

1Center for Observational Research, Amgen Inc., South San Francisco and Thousand Oaks, CA, 2Flatiron Health, New York, NY, USA

Objective: The accuracy of bone metastases diagnostic coding based on International Classification of Diseases, ninth revision (ICD-9) is unknown for most large databases used for epidemiologic research in the US. Electronic health records (EHR) are the preferred source of data, but often clinically relevant data occur only as unstructured free text. We examined the validity of bone metastases ICD-9 coding in structured EHR and administrative claims relative to the complete (structured and unstructured) patient chart obtained through technology-enabled chart abstraction.
Patients and methods: Female patients with breast cancer with ≥1 visit after November 2010 were identified from three community oncology practices in the US. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of bone metastases ICD-9 code 198.5. The technology-enabled abstraction displays portions of the chart to clinically trained abstractors for targeted review, thereby maximizing efficiency. We evaluated effects of misclassification of patients developing skeletal complications or treated with bone-targeting agents (BTAs), and timing of BTA.
Results: Among 8,796 patients with breast cancer, 524 had confirmed bone metastases using chart abstraction. Sensitivity was 0.67 (95% confidence interval [CI] =0.63–0.71) based on structured EHR, and specificity was high at 0.98 (95% CI =0.98–0.99) with corresponding PPV of 0.71 (95% CI =0.67–0.75) and NPV of 0.98 (95% CI =0.98–0.98). From claims, sensitivity was 0.78 (95% CI =0.74–0.81), and specificity was 0.98 (95% CI =0.98–0.98) with PPV of 0.72 (95% CI =0.68–0.76) and NPV of 0.99 (95% CI =0.98–0.99). Structured data and claims missed 17% of bone metastases (89 of 524). False negatives were associated with measurable overestimation of the proportion treated with BTA or with a skeletal complication. Median date of diagnosis was delayed in structured data (32 days) and claims (43 days) compared with technology-assisted EHR.
Conclusion: Technology-enabled chart abstraction of unstructured EHR greatly improves data quality, minimizing false negatives when identifying patients with bone metastases that may lead to inaccurate conclusions that can affect delivery of care.

Keywords: electronic medical records, EHR, US, ICD-9, breast cancer, unstructured data

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