Skip to main content

Advertisement

SpringerLink
Log in

Development of an Algorithm to Identify Cases of Nonalcoholic Steatohepatitis Cirrhosis in the Electronic Health Record

  • Original Article
  • Published:
Digestive Diseases and Sciences Aims and scope Submit manuscript

Abstract

Background and Aims

Current genetic research of nonalcoholic steatohepatitis (NASH) cirrhosis is limited by our ability to accurately identify cases on a large scale. Our objective was to develop and validate an electronic health record (EHR) algorithm to accurately identify cases of NASH cirrhosis in the EHR.

Methods

We used Clinical Query 2, a search tool at Beth Israel Deaconess Medical Center, to create a pool of potential NASH cirrhosis cases (n = 5415). We created a training set of 300 randomly selected patients for chart review to confirm cases of NASH cirrhosis. Test characteristics of different algorithms, consisting of diagnosis codes, laboratory values, anthropomorphic measurements, and medication records, were calculated. The algorithms with the highest positive predictive value (PPV) and the highest F score with a PPV ≥ 80% were selected for internal validation using a separate random set of 100 patients from the potential NASH cirrhosis pool. These were then externally validated in another random set of 100 individuals using the research patient data registry tool at Massachusetts General Hospital.

Results

The algorithm with the highest PPV of 100% on internal validation and 92% on external validation consisted of ≥ 3 counts of “cirrhosis, no mention of alcohol” (571.5, K74.6) and ≥ 3 counts of “nonalcoholic fatty liver” (571.8–571.9, K75.81, K76.0) codes in the absence of any diagnosis codes for other common causes of chronic liver disease.

Conclusions

We developed and validated an EHR algorithm using diagnosis codes that accurately identifies patients with NASH cirrhosis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Welter D, MacArthur J, Morales J, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res.. 2014;42(D1):D1001–D1006.

    Article  CAS  Google Scholar 

  2. Visscher PM, Wray NR, Zhang Q, et al. 10 Years of GWAS discovery: biology, function, and translation. Am J Hum Genet.. 2017;101(1):5–22.

    Article  CAS  Google Scholar 

  3. Dawber TR, Meadors GF, Moore FE. Epidemiological approaches to heart disease: the Framingham study. Am J Public Health.. 1951;41(3):279–281.

    Article  CAS  Google Scholar 

  4. Wei WQ, Denny JC. Extracting research-quality phenotypes from electronic health records to support precision medicine. Genome Med.. 2015;7(1):1–14.

    Article  CAS  Google Scholar 

  5. Kho AN, Hayes MG, Rasmussen-Torvik L, et al. Use of diverse electronic medical record systems to identify genetic risk for type 2 diabetes within a genome-wide association study. J Am Med Informa Assoc.. 2012;19(2):212–218.

    Article  Google Scholar 

  6. Blumenthal D, Tavenner M. The, “meaningful use” regulation for electronic health records. N Engl J Med.. 2010;363(6):501–504.

    Article  CAS  Google Scholar 

  7. Newton KM, Peissig PL, Kho AN, et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc.. 2013;20(E1):147–154.

    Article  Google Scholar 

  8. Denny JC, Crawford DC, Ritchie MD, et al. Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies. Am J Hum Genet.. 2011;89(4):529–542.

    Article  CAS  Google Scholar 

  9. Ritchie MD, Denny JC, Crawford DC, et al. Robust replication of genotype-phenotype associations across multiple diseases in an electronic medical record. Am J Hum Genet.. 2010;86(4):560–572.

    Article  CAS  Google Scholar 

  10. Kurreeman F, Liao K, Chibnik L, et al. Genetic basis of autoantibody positive and negative rheumatoid arthritis risk in a multi-ethnic cohort derived from electronic health records. Am J Hum Genet.. 2011;88(1):57–69.

    Article  CAS  Google Scholar 

  11. Beste LA, Leipertz SL, Green PK, et al. Trends in burden of cirrhosis and hepatocellular carcinoma by underlying liver disease in US Veterans, 2001–2013. Gastroenterology.. 2015;149(6):1471–1482.e5.

    Article  Google Scholar 

  12. Corey KE, Kartoun U, Zheng H, Shaw SY. Development and validation of an algorithm to identify nonalcoholic fatty liver disease in the electronic medical record. Dig Dis Sci.. 2016;61(3):913–919.

    Article  Google Scholar 

  13. Matteoni CZY, Gramlich T, et al. Nonalcoholic fatty liver disease: a spectrum of clinical and pathological severity. Gastroenterology. 1999;116(6):1413–1419.

    Article  CAS  Google Scholar 

  14. Galle PR, Forner A, Llovet JM, et al. EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol.. 2018;69(1):182–236.

    Article  Google Scholar 

  15. Younossi ZM, Gramlich T, Liu YC, et al. Nonalcoholic fatty liver disease: assessment of variability in pathologic interpretations. Mod Pathol.. 1998;11(6):560–565.

    CAS  PubMed  Google Scholar 

  16. Younossi Z, Stepanova M, Sanyal AJ, et al. The conundrum of cryptogenic cirrhosis: adverse outcomes without treatment options. J Hepatol.. 2018;69(6):1365–1370.

    Article  Google Scholar 

  17. Newton KM, Peissig PL, Kho AN, et al. Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network. J Am Med Inform Assoc.. 2013;20(e1):e147–e154.

    Article  Google Scholar 

  18. Liao KP, Cai T, Gainer V, et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res (Hoboken).. 2010;62(8):1120–1127.

    Article  Google Scholar 

  19. van der Ploeg T, Austin PC, Steyerberg EW. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med Res Methodol.. 2014;14:137.

    Article  Google Scholar 

  20. Husain N, Blais P, Kramer J, et al. Nonalcoholic fatty liver disease (NAFLD) in the Veterans Administration population: development and validation of an algorithm for NAFLD using automated data. Aliment Pharmacol Ther.. 2014;40(8):949–954.

    Article  CAS  Google Scholar 

  21. Sudlow C, Gallacher J, Allen N, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med.. 2015;12(3):1–10.

    Article  Google Scholar 

  22. Borodulin K, Tolonen H, Jousilahti P, et al. Cohort profile: the national FINRISK study. Int J Epidemiol.. 2018;47(3):696–1471.

    Article  Google Scholar 

  23. Rich NE, Oji S, Mufti AR, et al. Racial and ethnic disparities in nonalcoholic fatty liver disease prevalence, severity, and outcomes in the united states: a systematic review and meta-analysis. Clin Gastroenterol Hepatol.. 2018;16(2):198–210.e2.

    Article  Google Scholar 

  24. Teixeira PL, Wei W-Q, Cronin RM, et al. Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals. J Am Med Inform Assoc.. 2017;24(1):162–171.

    Article  Google Scholar 

  25. Delaney JT, Ramirez AH, Bowton E, et al. Predicting clopidogrel response using DNA samples linked to an electronic health record. Clin Pharmacol Ther.. 2012;91(2):257–263.

    Article  CAS  Google Scholar 

  26. Bowton E, Field JR, Wang S, et al. Biobanks and electronic medical records: enabling cost-effective research. Sci Transl Med. 2014;6(234):234cm3.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center, Liver Center, 110 Francis St. Suite 8E, Boston, MA, 02215, USA

    Christopher J. Danford & Michelle Lai

  2. Department of Medicine, Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA, 02215, USA

    Jennifer Y. Lee

  3. Gastrointestinal Unit, Massachusetts General Hospital, 55 Fruit St, Boston, MA, 02114, USA

    Ian A. Strohbehn & Kathleen E. Corey

Authors
  1. Christopher J. Danford
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jennifer Y. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ian A. Strohbehn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kathleen E. Corey
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michelle Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christopher J. Danford.

Ethics declarations

Conflict of interest

No outside funding or grant support was involved in the production of this manuscript. The authors have no pertinent conflicts of interest to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 26 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Danford, C.J., Lee, J.Y., Strohbehn, I.A. et al. Development of an Algorithm to Identify Cases of Nonalcoholic Steatohepatitis Cirrhosis in the Electronic Health Record. Dig Dis Sci 66, 1452–1460 (2021). https://doi.org/10.1007/s10620-020-06388-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10620-020-06388-y

Keywords

Search

Navigation