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Identifying monoclonal gammopathy of undetermined significance in electronic health data
Author(s) -
Epstein Mara Meyer,
Saphirak Cassandra,
Zhou Yanhua,
LeBlanc Candace,
Rosmarin Alan G.,
Ash Arlene,
Singh Sonal,
Fisher Kimberly,
Birmann Brenda M.,
Gurwitz Jerry H.
Publication year - 2020
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.4912
Subject(s) - medicine , monoclonal gammopathy of undetermined significance , multiple myeloma , asymptomatic , diagnosis code , false positive paradox , medical record , population , algorithm , pediatrics , immunology , monoclonal , antibody , environmental health , monoclonal antibody , machine learning , computer science
Purpose Monoclonal gammopathy of undetermined significance (MGUS) is a prevalent yet largely asymptomatic precursor to multiple myeloma. Patients with MGUS must undergo regular surveillance and testing, with few known predictors of progression. We developed an algorithm to identify MGUS patients in electronic health data to facilitate large‐scale, population‐based studies of this premalignant condition. Methods We developed a four‐step algorithm using electronic health record and health claims data from men and women aged 50 years or older receiving care from a large, multispecialty medical group between 2007 and 2015. The case definition required patients to have at least two MGUS ICD‐9 diagnosis codes within 12 months, at least one serum and/or urine protein electrophoresis and one immunofixation test, and at least one in‐office hematology/oncology visit. Medical charts for selected cases were abstracted then adjudicated independently by two physicians. We assessed algorithm validity by positive predictive value (PPV). Results We identified 833 people with at least two MGUS diagnosis codes; 429 (52%) met all four algorithm criteria. We randomly selected 252 charts for review, including 206 from patients meeting all four algorithm criteria. The PPV for the 206 algorithm‐identified charts was 76% (95% CI, 70%‐82%). Among the 49 cases deemed to be false positives (24%), 33 were judged to have multiple myeloma or another lymphoproliferative condition, such as lymphoma. Conclusions We developed a simple algorithm that identified MGUS cases in electronic health data with reasonable accuracy. Inclusion of additional steps to eliminate cases with malignant disease may improve algorithm performance.