Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Mar:111:83-89.
doi: 10.1016/j.ijmedinf.2017.12.024. Epub 2017 Dec 28.

Natural language processing of clinical notes for identification of critical limb ischemia

Naveed Afzal  1 , Vishnu Priya Mallipeddi  2 , Sunghwan Sohn  1 , Hongfang Liu  1 , Rajeev Chaudhry  3 , Christopher G Scott  1 , Iftikhar J Kullo  2 , Adelaide M Arruda-Olson  4
Affiliations

Natural language processing of clinical notes for identification of critical limb ischemia

Naveed Afzal et al. Int J Med Inform. 2018 Mar.

Abstract

Background: Critical limb ischemia (CLI) is a complication of advanced peripheral artery disease (PAD) with diagnosis based on the presence of clinical signs and symptoms. However, automated identification of cases from electronic health records (EHRs) is challenging due to absence of a single definitive International Classification of Diseases (ICD-9 or ICD-10) code for CLI.

Methods and results: In this study, we extend a previously validated natural language processing (NLP) algorithm for PAD identification to develop and validate a subphenotyping NLP algorithm (CLI-NLP) for identification of CLI cases from clinical notes. We compared performance of the CLI-NLP algorithm with CLI-related ICD-9 billing codes. The gold standard for validation was human abstraction of clinical notes from EHRs. Compared to billing codes the CLI-NLP algorithm had higher positive predictive value (PPV) (CLI-NLP 96%, billing codes 67%, p < 0.001), specificity (CLI-NLP 98%, billing codes 74%, p < 0.001) and F1-score (CLI-NLP 90%, billing codes 76%, p < 0.001). The sensitivity of these two methods was similar (CLI-NLP 84%; billing codes 88%; p < 0.12).

Conclusions: The CLI-NLP algorithm for identification of CLI from narrative clinical notes in an EHR had excellent PPV and has potential for translation to patient care as it will enable automated identification of CLI cases for quality projects, clinical decision support tools and support a learning healthcare system.

Keywords: Critical limb ischemia; Electronic health records; Natural language processing; Peripheral artery disease; Subphenotyping.

PubMed Disclaimer

Conflict of interest statement

Conflict of interest

The author(s) declare(s) that there is no conflict of interest.

Figures

Fig. 1
Fig. 1
Study design.
Fig. 2
Fig. 2
Distribution of Clinical Notes by Medical Specialty.
Fig. 3
Fig. 3
Distribution of Note Types.
See this image and copyright information in PMC

References

    1. Kullo IJ, Rooke TW. Peripheral artery disease. N Engl J Med. 2016;2016(374):861–871. - PubMed
    1. Gerhard-Herman MD, Gornik HL, Barrett C, et al. 2016 AHA/ACC guideline on the management of patients with lower extremity peripheral artery disease: a report of the american college of cardiology/american heart association task force on clinical practice guidelines. J Am Coll Cardiol. 2017;69(11):e71–e126. - PubMed
    1. Norgren L, Hiatt WR, Dormandy JA, Nehler MR, Harris KA, Fowkes FG. Inter-society consensus for the management of peripheral arterial disease (TASC II) J Vasc Surg. 2007:S5–67. - PubMed
    1. Farber A, Eberhardt RT. The current state of critical limb ischemia: a systematic review. JAMA Surg. 2016;151(11):1070–1077. - PubMed
    1. Shishehbor MH, White CJ, Gray BH, et al. Critical limb ischemia: an expert statement. J Am Coll Cardiol. 2016;68(18):2002–2015. - PubMed

Publication types