Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims Data
Purpose: The objective was to expand on prior work by developing and validating a new algorithm to identify multiple myeloma (MM) patients in administrative claims. Methods: Two files were constructed to select MM cases from MarketScan Oncology EMR and controls from the MarketScan Primary Care EMR...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-10-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fonc.2016.00224/full |
| _version_ | 1818198446183546880 |
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| author | Nicole Princic Christopher Gregory Tina Willson Maya Mahue Diana Felici Winifred Werther Gregory Lenhart Kathleen Foley |
| author_facet | Nicole Princic Christopher Gregory Tina Willson Maya Mahue Diana Felici Winifred Werther Gregory Lenhart Kathleen Foley |
| author_sort | Nicole Princic |
| collection | DOAJ |
| description | Purpose: The objective was to expand on prior work by developing and validating a new algorithm to identify multiple myeloma (MM) patients in administrative claims. Methods: Two files were constructed to select MM cases from MarketScan Oncology EMR and controls from the MarketScan Primary Care EMR during 1/1/2000-3/31/2014. Patients were linked to MarketScan claims databases and files were merged. Eligible cases were age >18, had a diagnosis and visit for MM in the Oncology EMR, and were continuously enrolled in claims for >90 days preceding and >30 days after diagnosis. Controls were age >18, had >12 months of overlap in claims enrollment (observation period) in the Primary Care EMR and >1 claim with an ICD-9-CM diagnosis code of MM (203.0x) during that time. Controls were excluded if they had chemotherapy; stem cell transplant; or text documentation of MM in the EMR during the observation period. A split sample was used to develop and validate algorithms. A maximum of 180 days prior to and following each MM diagnosis was used to identify events in the diagnostic process. Of 20 algorithms explored, the baseline algorithm of 2 MM diagnoses and the 3 best performing were validated. Values for sensitivity, specificity, and positive predictive value (PPV) were calculated. Conclusions: Three claims-based algorithms were validated with ~10% improvement in PPV (87%-94%) over prior work (81%) and the baseline algorithm (76%) and can be considered for future research. Consistent with prior work it was found that MM diagnoses before and after tests were needed. |
| first_indexed | 2024-12-12T02:06:00Z |
| format | Article |
| id | doaj.art-52f5ec4c001848c1a2ee9fc66830973a |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-12T02:06:00Z |
| publishDate | 2016-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-52f5ec4c001848c1a2ee9fc66830973a2022-12-22T00:42:02ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2016-10-01610.3389/fonc.2016.00224210690Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims DataNicole Princic0Christopher Gregory1Tina Willson2Maya Mahue3Diana Felici4Winifred Werther5Gregory Lenhart6Kathleen Foley7Truven HealthTruven HealthTruven HealthOnyx Pharmaceuticals Inc., an Amgen subsidiaryOnyx Pharmaceuticals Inc., an Amgen subsidiaryOnyx Pharmaceuticals Inc., an Amgen subsidiaryTruven HealthTruven HealthPurpose: The objective was to expand on prior work by developing and validating a new algorithm to identify multiple myeloma (MM) patients in administrative claims. Methods: Two files were constructed to select MM cases from MarketScan Oncology EMR and controls from the MarketScan Primary Care EMR during 1/1/2000-3/31/2014. Patients were linked to MarketScan claims databases and files were merged. Eligible cases were age >18, had a diagnosis and visit for MM in the Oncology EMR, and were continuously enrolled in claims for >90 days preceding and >30 days after diagnosis. Controls were age >18, had >12 months of overlap in claims enrollment (observation period) in the Primary Care EMR and >1 claim with an ICD-9-CM diagnosis code of MM (203.0x) during that time. Controls were excluded if they had chemotherapy; stem cell transplant; or text documentation of MM in the EMR during the observation period. A split sample was used to develop and validate algorithms. A maximum of 180 days prior to and following each MM diagnosis was used to identify events in the diagnostic process. Of 20 algorithms explored, the baseline algorithm of 2 MM diagnoses and the 3 best performing were validated. Values for sensitivity, specificity, and positive predictive value (PPV) were calculated. Conclusions: Three claims-based algorithms were validated with ~10% improvement in PPV (87%-94%) over prior work (81%) and the baseline algorithm (76%) and can be considered for future research. Consistent with prior work it was found that MM diagnoses before and after tests were needed.http://journal.frontiersin.org/Journal/10.3389/fonc.2016.00224/fullMultiple MyelomaValidationalgorithmElectronic Medical RecordsAdministrative claims |
| spellingShingle | Nicole Princic Christopher Gregory Tina Willson Maya Mahue Diana Felici Winifred Werther Gregory Lenhart Kathleen Foley Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims Data Frontiers in Oncology Multiple Myeloma Validation algorithm Electronic Medical Records Administrative claims |
| title | Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims Data |
| title_full | Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims Data |
| title_fullStr | Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims Data |
| title_full_unstemmed | Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims Data |
| title_short | Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims Data |
| title_sort | development and validation of an algorithm to identify patients with multiple myeloma using administrative claims data |
| topic | Multiple Myeloma Validation algorithm Electronic Medical Records Administrative claims |
| url | http://journal.frontiersin.org/Journal/10.3389/fonc.2016.00224/full |
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