| Title |
Validation of International Classification of Diseases coding for bone metastases in electronic health records using technology-enabled abstraction
|
|---|---|
| Published in |
Clinical Epidemiology, November 2015
|
| DOI | 10.2147/clep.s92209 |
| Pubmed ID | |
| Authors |
Alexander Liede, Rohini K Hernandez, Maayan Roth, Geoffrey Calkins, Katherine Larrabee, Leo Nicacio |
| Abstract |
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. 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. 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. 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. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Unknown | 50 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 22% |
| Student > Ph. D. Student | 5 | 10% |
| Student > Master | 5 | 10% |
| Student > Doctoral Student | 3 | 6% |
| Other | 3 | 6% |
| Other | 10 | 20% |
| Unknown | 14 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 19 | 37% |
| Computer Science | 5 | 10% |
| Mathematics | 2 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 4% |
| Business, Management and Accounting | 1 | 2% |
| Other | 5 | 10% |
| Unknown | 17 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 December 2015.
All research outputs
#19,945,185
of 25,374,917 outputs
Outputs from Clinical Epidemiology
#606
of 793 outputs
Outputs of similar age
#201,829
of 294,812 outputs
Outputs of similar age from Clinical Epidemiology
#5
of 8 outputs
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