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

Overview of attention for article published in Clinical Epidemiology, November 2015
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2 tweeters

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40 Mendeley
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 Khorana Hernandez, Maayan Roth, Geoffrey Calkins, Leonardo Nicacio, Katherine Larrabee

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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Unknown 39 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 18%
Student > Master 6 15%
Student > Postgraduate 4 10%
Student > Ph. D. Student 3 8%
Other 3 8%
Other 8 20%
Unknown 9 23%
Readers by discipline Count As %
Medicine and Dentistry 17 43%
Computer Science 4 10%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Business, Management and Accounting 1 3%
Agricultural and Biological Sciences 1 3%
Other 6 15%
Unknown 10 25%

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
#4,677,986
of 6,650,505 outputs
Outputs from Clinical Epidemiology
#109
of 193 outputs
Outputs of similar age
#144,867
of 236,182 outputs
Outputs of similar age from Clinical Epidemiology
#5
of 8 outputs
Altmetric has tracked 6,650,505 research outputs across all sources so far. This one is in the 26th percentile – i.e., 26% of other outputs scored the same or lower than it.
So far Altmetric has tracked 193 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 236,182 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.