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

Abstract

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: EHR; ICD-9; US; breast cancer; electronic medical records; unstructured data.

Figure 1

Traditional and modular technology-enabled chart abstraction process from EHR. Notes: In traditional chart abstraction, the abstractor is presented with the entire chart and a single CRF. In a modular approach, the CRF is divided into thematically related modules and abstractors are presented with relevant data only. Abbreviations: EHR, electronic health records; CRF, case report form; MD, Medicinae Doctor or physician; QA, quality assurance.

Figure 2

Study cohort selection.

Figure 3

Venn diagram of patients identified with a diagnosis of bone metastasis by data source among 8,796 confirmed women with a diagnosis of breast cancer, illustrating potential cases and false positives. Abbreviations: EHR, electronic health records; ICD-9, International Classification of Diseases, ninth revision.