Validation of an electronic algorithm for Hodgkin and non-Hodgkin lymphoma in ICD-10-CM

Mara M. Epstein*, Sarah K. Dutcher, Judith C. Maro, Cassandra Saphirak, Sandra DeLuccia, Muthalagu Ramanathan, Tejaswini Dhawale, Sonali Harchandani, Christopher Delude, Laura Hou, Autumn Gertz, Nina DiNunzio, Cheryl N. McMahill-Walraven, Mano S. Selvan, Justin Vigeant, David V. Cole, Kira Leishear, Jerry H. Gurwitz, Susan Andrade, Noelle M. Cocoros

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Lymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of lymphoma cases. We developed and validated an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)-based algorithm to identify lymphoma in healthcare claims data. Methods: We developed a three-component algorithm to identify patients aged ≥15 years who were newly diagnosed with Hodgkin (HL) or non-Hodgkin (NHL) lymphoma from January 2016 through July 2018 among members of four Data Partners within the FDA's Sentinel System. The algorithm identified potential cases as patients with ≥2 ICD-10-CM lymphoma diagnosis codes on different dates within 183 days; ≥1 procedure code for a diagnostic procedure (e.g., biopsy, flow cytometry) and ≥1 procedure code for a relevant imaging study within 90 days of the first lymphoma diagnosis code. Cases identified by the algorithm were adjudicated via chart review and a positive predictive value (PPV) was calculated. Results: We identified 8723 potential lymphoma cases via the algorithm and randomly sampled 213 for validation. We retrieved 138 charts (65%) and adjudicated 134 (63%). The overall PPV was 77% (95% confidence interval: 69%–84%). Most cases also had subtype information available, with 88% of cases identified as NHL and 11% as HL. Conclusions: Seventy-seven percent of lymphoma cases identified by an algorithm based on ICD-10-CM diagnosis and procedure codes and applied to claims data were true cases. This novel algorithm represents an efficient, cost-effective way to target an important health outcome of interest for large-scale drug safety and public health surveillance studies.

Original languageEnglish
Pages (from-to)910-917
Number of pages8
JournalPharmacoepidemiology and Drug Safety
Volume30
Issue number7
DOIs
Publication statusPublished - Jul 2021
Externally publishedYes

Bibliographical note

Funding Information:
The authors would like to acknowledge the contributions of Yunping Zhou (Humana Healthcare Research, Inc. (HHR)), Jennifer Kuntz (Kaiser Permanente Northwest), Kevin Haynes, Lauren Parlett, and Shia Kent (HealthCore (Anthem)), and Michael Nguyen (FDA), and thank them for their assistance with this project. This work was supported by funding from the U.S. Food and Drug Administration under the following contract: FDA HHSF223201400030I. M.M.E. is supported in part by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant KL2TR001454.

Funding Information:
The authors would like to acknowledge the contributions of Yunping Zhou (Humana Healthcare Research, Inc. (HHR)), Jennifer Kuntz (Kaiser Permanente Northwest), Kevin Haynes, Lauren Parlett, and Shia Kent (HealthCore (Anthem)), and Michael Nguyen (FDA), and thank them for their assistance with this project. This work was supported by funding from the U.S. Food and Drug Administration under the following contract: FDA HHSF223201400030I. M.M.E. is supported in part by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant KL2TR001454.

Publisher Copyright:
© 2021 John Wiley & Sons Ltd.

Keywords

  • algorithm
  • lymphoma
  • pharmacoepidemiology
  • validation

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