ArticlePharmacoepidemiology and drug safety2020
Identifying monoclonal gammopathy of undetermined significance in electronic health data.
Article in Pharmacoepidemiology and drug safety, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multimorbidity in patients with monoclonal gammopathy of undetermined significance.International journal of cancer · 2023Article
- Identifying monoclonal gammopathy of undetermined significance from electronic health records.Cancer reports (Hoboken, N.J.) · 2023Review
- Multi-trajectory models of serum biomarkers among patients with monoclonal gammopathy of undetermined significance.Hematological oncology · 2022Article
- Differences in Hospital, Emergency Room and Outpatient Visits Among Adults With and Without Monoclonal Gammopathy of Undetermined Significance.Cancer control : journal of the Moffitt Cancer CenterArticle
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Authors and funding
10 authors.
Funding
Abstract
purposeMonoclonal 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.
methodsWe 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).
resultsWe 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.
conclusionsWe 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.
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