Mara Meyer Epstein1,2, Cassandra Saphirak1, Yanhua Zhou1, Candace LeBlanc3, Alan G Rosmarin4, Arlene Ash5, Sonal Singh1,6, Kimberly Fisher1,7, Brenda M Birmann8, Jerry H Gurwitz1,2. 1. The Meyers Primary Care Institute, a joint venture of Reliant Medical Group, Fallon Health, and the University of Massachusetts Medical School, Worcester, MA, USA. 2. Division of Geriatric Medicine, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA. 3. Reliant Medical Group, Worcester, MA, USA. 4. UpToDate, Waltham, MA, USA. 5. Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA. 6. Department of Family Medicine and Community Health, University of Massachusetts Medical School, Worcester, MA, USA. 7. Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA. 8. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Abstract
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.
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.
Keywords:
administrative claims; algorithms; electronic health records; health care; monoclonal gammopathy of undetermined significance; pharmacoepidemiology
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