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Phenotype Submission - Marginal zone lymphoma

Cohort Definition Name : Earliest event of Marginal zone lymphoma

Contributor name : Jill Hardin

Contributor OrcId :

Logic Description : Earliest occurrence of Marginal zone lymphoma, indexed on diagnosis date, for the first time in history cohort exit is the end of continuous observation

Recommended study application : target

Assertion statement : This cohort definition was executed on at least one real person-level observational health data source and resulted in a cohort with at least 1 person.

Target Clinical Description : Marginal Zone lymphoma (MZL) is a group of non-Hodgkin lymphomas (NHL) derived from the malignant transformation of marginal zone B-cells. The MZLs occur more often in older people and grow slowly. There are three types of MZLs. Extranodal MZL or Mucosa-Associated Lymphoid Tissue (MALT) is the most common form, usually arising in organs devoid of lymphoid tissue, such as the stomach (the most frequently affected), small intestine, salivary glands, thyroid, breast, eyes, lungs, and skins. Nodal MZL (sometimes called monocytoid B-cell lymphoma) develops within the lymph nodes. Splenic MZL arises from the spleen, blood marrow, and peripheral blood. MZL, particularly MALT lymphomas, is often associated with the chronic inflammation induced by infections or autoimmune disorders.

"Evaluation conclusion : We developed a prevalent cohort definition for marginal zone lymphoma (MZL) using a concept set containing the concepts below which incorporated all those found from the literature review and from the analysis of PHOEBE and orphan concepts in cohort diagnostics. We performed the evaluation across a network of claim data sources and 1 EHR US data source. The data sources are: IBM® MarketScan® Commercial Database (CCAE), Optum’s longitudinal EHR repository (Optum EHR), Optum’s Clinformatics® Data Mart (DOD), IBM® MarketScan® Multi-State Medicaid Database (MDCD), IBM® MarketScan® Medicare Supplemental Database (MDCR), Japan Claims Database (JMDC), Clinical Practice Research Datalink (CPRD) , IQVIA® Australia Longitudinal Patient Data (LPD) database (Australia), IQVIA® Disease Analyzer (DA) France database (France), QVIA® Disease Analyzer (DA) Germany database (Germany), IQVIA® Adjudicated Health Plan Claims Data (formerly PharMetrics Plus) - US database (PharMetrics), IQVIA® Ambulatory EMR (EMR) . The algorithm retrieves subjects from all 9 out of the 11 databases tested. We found that there were signs and symptoms in the period prior to the first diagnosis of MZL indicative of index date misclassification. These included codes anemia ( 439777), dyspnea (312437), lymphadenopathy (315085), abdominal pain (200219), and malaise ( 4272240), common symptoms of MZL. However in the context of this disease the latency period is lengthy as many subjects are not officially diagnosed for years and because prevalent cohorts are being developed we opted to not use a cohort adjusted for index date misclassification. In addition, we developed two more specific cohort definitions; the first requiring a second diagnosis code for MZL in the time period 31-365 days after index and the second either requiring a second code in the time period 31-365 days after index or a visit with a specialist and a diagnostic code. These cohorts improve the specificity of the algorithm albeit at the expense of sensitivity as determined by PheValuator. Because use of second codes is not common in some databases and use of codes in future can result in immortal time bias and the use of an algorithm using 2 codes in this case does not dramatically alter the sensitivity and ppv metrics we recommend using the algorithm requiring only 1 code.

Performance characteristics were determined for 6 of the 11 databases. The remaining databases did not contain enough subjects to produce an accurate diagnostic model. Using one code, sensitivity ranged from about 88% in EHR to about 98% in SES while positive predictive value ranged from about 50% in MDCD to about 77% in Pharmetrics. Using 2 codes, sensitivity decreased, ranging from about 65% in EHR to about 78% in Pharmetrics while positive predictive value increased, ranging from 73% in MDCD to about 96% in SES.

Imported to the OHDSI Phenotype Library. It may be expected to be found with id = 1011 in the next release. Thank you