Validation of an electronic algorithm for Hodgkin and non‐Hodgkin lymphoma in ICD‐10‐CM
Purpose Lymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of lymphoma cases. We developed and validated an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM)‐based al...
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| Published in | Pharmacoepidemiology and drug safety Vol. 30; no. 7; pp. 910 - 917 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Inc
01.07.2021
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1053-8569 1099-1557 1099-1557 |
| DOI | 10.1002/pds.5256 |
Cover
| Abstract | Purpose
Lymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of lymphoma cases. We developed and validated an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM)‐based algorithm to identify lymphoma in healthcare claims data.
Methods
We developed a three‐component algorithm to identify patients aged ≥15 years who were newly diagnosed with Hodgkin (HL) or non‐Hodgkin (NHL) lymphoma from January 2016 through July 2018 among members of four Data Partners within the FDA's Sentinel System. The algorithm identified potential cases as patients with ≥2 ICD‐10‐CM lymphoma diagnosis codes on different dates within 183 days; ≥1 procedure code for a diagnostic procedure (e.g., biopsy, flow cytometry) and ≥1 procedure code for a relevant imaging study within 90 days of the first lymphoma diagnosis code. Cases identified by the algorithm were adjudicated via chart review and a positive predictive value (PPV) was calculated.
Results
We identified 8723 potential lymphoma cases via the algorithm and randomly sampled 213 for validation. We retrieved 138 charts (65%) and adjudicated 134 (63%). The overall PPV was 77% (95% confidence interval: 69%–84%). Most cases also had subtype information available, with 88% of cases identified as NHL and 11% as HL.
Conclusions
Seventy‐seven percent of lymphoma cases identified by an algorithm based on ICD‐10‐CM diagnosis and procedure codes and applied to claims data were true cases. This novel algorithm represents an efficient, cost‐effective way to target an important health outcome of interest for large‐scale drug safety and public health surveillance studies. |
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| AbstractList | PurposeLymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of lymphoma cases. We developed and validated an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM)‐based algorithm to identify lymphoma in healthcare claims data.MethodsWe developed a three‐component algorithm to identify patients aged ≥15 years who were newly diagnosed with Hodgkin (HL) or non‐Hodgkin (NHL) lymphoma from January 2016 through July 2018 among members of four Data Partners within the FDA's Sentinel System. The algorithm identified potential cases as patients with ≥2 ICD‐10‐CM lymphoma diagnosis codes on different dates within 183 days; ≥1 procedure code for a diagnostic procedure (e.g., biopsy, flow cytometry) and ≥1 procedure code for a relevant imaging study within 90 days of the first lymphoma diagnosis code. Cases identified by the algorithm were adjudicated via chart review and a positive predictive value (PPV) was calculated.ResultsWe identified 8723 potential lymphoma cases via the algorithm and randomly sampled 213 for validation. We retrieved 138 charts (65%) and adjudicated 134 (63%). The overall PPV was 77% (95% confidence interval: 69%–84%). Most cases also had subtype information available, with 88% of cases identified as NHL and 11% as HL.ConclusionsSeventy‐seven percent of lymphoma cases identified by an algorithm based on ICD‐10‐CM diagnosis and procedure codes and applied to claims data were true cases. This novel algorithm represents an efficient, cost‐effective way to target an important health outcome of interest for large‐scale drug safety and public health surveillance studies. Purpose Lymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of lymphoma cases. We developed and validated an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD‐10‐CM)‐based algorithm to identify lymphoma in healthcare claims data. Methods We developed a three‐component algorithm to identify patients aged ≥15 years who were newly diagnosed with Hodgkin (HL) or non‐Hodgkin (NHL) lymphoma from January 2016 through July 2018 among members of four Data Partners within the FDA's Sentinel System. The algorithm identified potential cases as patients with ≥2 ICD‐10‐CM lymphoma diagnosis codes on different dates within 183 days; ≥1 procedure code for a diagnostic procedure (e.g., biopsy, flow cytometry) and ≥1 procedure code for a relevant imaging study within 90 days of the first lymphoma diagnosis code. Cases identified by the algorithm were adjudicated via chart review and a positive predictive value (PPV) was calculated. Results We identified 8723 potential lymphoma cases via the algorithm and randomly sampled 213 for validation. We retrieved 138 charts (65%) and adjudicated 134 (63%). The overall PPV was 77% (95% confidence interval: 69%–84%). Most cases also had subtype information available, with 88% of cases identified as NHL and 11% as HL. Conclusions Seventy‐seven percent of lymphoma cases identified by an algorithm based on ICD‐10‐CM diagnosis and procedure codes and applied to claims data were true cases. This novel algorithm represents an efficient, cost‐effective way to target an important health outcome of interest for large‐scale drug safety and public health surveillance studies. Lymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of lymphoma cases. We developed and validated an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)-based algorithm to identify lymphoma in healthcare claims data.PURPOSELymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of lymphoma cases. We developed and validated an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)-based algorithm to identify lymphoma in healthcare claims data.We developed a three-component algorithm to identify patients aged ≥15 years who were newly diagnosed with Hodgkin (HL) or non-Hodgkin (NHL) lymphoma from January 2016 through July 2018 among members of four Data Partners within the FDA's Sentinel System. The algorithm identified potential cases as patients with ≥2 ICD-10-CM lymphoma diagnosis codes on different dates within 183 days; ≥1 procedure code for a diagnostic procedure (e.g., biopsy, flow cytometry) and ≥1 procedure code for a relevant imaging study within 90 days of the first lymphoma diagnosis code. Cases identified by the algorithm were adjudicated via chart review and a positive predictive value (PPV) was calculated.METHODSWe developed a three-component algorithm to identify patients aged ≥15 years who were newly diagnosed with Hodgkin (HL) or non-Hodgkin (NHL) lymphoma from January 2016 through July 2018 among members of four Data Partners within the FDA's Sentinel System. The algorithm identified potential cases as patients with ≥2 ICD-10-CM lymphoma diagnosis codes on different dates within 183 days; ≥1 procedure code for a diagnostic procedure (e.g., biopsy, flow cytometry) and ≥1 procedure code for a relevant imaging study within 90 days of the first lymphoma diagnosis code. Cases identified by the algorithm were adjudicated via chart review and a positive predictive value (PPV) was calculated.We identified 8723 potential lymphoma cases via the algorithm and randomly sampled 213 for validation. We retrieved 138 charts (65%) and adjudicated 134 (63%). The overall PPV was 77% (95% confidence interval: 69%-84%). Most cases also had subtype information available, with 88% of cases identified as NHL and 11% as HL.RESULTSWe identified 8723 potential lymphoma cases via the algorithm and randomly sampled 213 for validation. We retrieved 138 charts (65%) and adjudicated 134 (63%). The overall PPV was 77% (95% confidence interval: 69%-84%). Most cases also had subtype information available, with 88% of cases identified as NHL and 11% as HL.Seventy-seven percent of lymphoma cases identified by an algorithm based on ICD-10-CM diagnosis and procedure codes and applied to claims data were true cases. This novel algorithm represents an efficient, cost-effective way to target an important health outcome of interest for large-scale drug safety and public health surveillance studies.CONCLUSIONSSeventy-seven percent of lymphoma cases identified by an algorithm based on ICD-10-CM diagnosis and procedure codes and applied to claims data were true cases. This novel algorithm represents an efficient, cost-effective way to target an important health outcome of interest for large-scale drug safety and public health surveillance studies. Lymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of lymphoma cases. We developed and validated an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM)-based algorithm to identify lymphoma in healthcare claims data. We developed a three-component algorithm to identify patients aged ≥15 years who were newly diagnosed with Hodgkin (HL) or non-Hodgkin (NHL) lymphoma from January 2016 through July 2018 among members of four Data Partners within the FDA's Sentinel System. The algorithm identified potential cases as patients with ≥2 ICD-10-CM lymphoma diagnosis codes on different dates within 183 days; ≥1 procedure code for a diagnostic procedure (e.g., biopsy, flow cytometry) and ≥1 procedure code for a relevant imaging study within 90 days of the first lymphoma diagnosis code. Cases identified by the algorithm were adjudicated via chart review and a positive predictive value (PPV) was calculated. We identified 8723 potential lymphoma cases via the algorithm and randomly sampled 213 for validation. We retrieved 138 charts (65%) and adjudicated 134 (63%). The overall PPV was 77% (95% confidence interval: 69%-84%). Most cases also had subtype information available, with 88% of cases identified as NHL and 11% as HL. Seventy-seven percent of lymphoma cases identified by an algorithm based on ICD-10-CM diagnosis and procedure codes and applied to claims data were true cases. This novel algorithm represents an efficient, cost-effective way to target an important health outcome of interest for large-scale drug safety and public health surveillance studies. |
| Author | DiNunzio, Nina Maro, Judith C. Vigeant, Justin DeLuccia, Sandra Hou, Laura Ramanathan, Muthalagu Cole, David V. Epstein, Mara M. Saphirak, Cassandra Harchandani, Sonali Delude, Christopher Gertz, Autumn Cocoros, Noelle M. Gurwitz, Jerry H. McMahill‐Walraven, Cheryl N. Andrade, Susan Selvan, Mano S. Dutcher, Sarah K. Dhawale, Tejaswini Leishear, Kira |
| AuthorAffiliation | 7 Aetna, a CVS Health company 5 Division of Hematology and Oncology, Department of Medicine, UMass Memorial Medical Center, Worcester, MA USA 8 Humana Healthcare Research, Inc. (HHR), Sugar Land, TX USA 1 Division of Geriatric Medicine, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA 2 The Meyers Primary Care Institute, a joint endeavor of the University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health, Worcester, MA USA 4 Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA USA 6 Division of Medicine, Massachusetts General Hospital, Boston, MA USA 3 Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA |
| AuthorAffiliation_xml | – name: 7 Aetna, a CVS Health company – name: 1 Division of Geriatric Medicine, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA – name: 5 Division of Hematology and Oncology, Department of Medicine, UMass Memorial Medical Center, Worcester, MA USA – name: 6 Division of Medicine, Massachusetts General Hospital, Boston, MA USA – name: 3 Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA – name: 4 Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA USA – name: 2 The Meyers Primary Care Institute, a joint endeavor of the University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health, Worcester, MA USA – name: 8 Humana Healthcare Research, Inc. (HHR), Sugar Land, TX USA |
| Author_xml | – sequence: 1 givenname: Mara M. orcidid: 0000-0001-7906-4856 surname: Epstein fullname: Epstein, Mara M. email: mara.epstein@umassmed.edu organization: a joint endeavor of the University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health – sequence: 2 givenname: Sarah K. orcidid: 0000-0003-0574-2890 surname: Dutcher fullname: Dutcher, Sarah K. organization: Food and Drug Administration – sequence: 3 givenname: Judith C. surname: Maro fullname: Maro, Judith C. organization: Harvard Pilgrim Health Care Institute, Harvard Medical School – sequence: 4 givenname: Cassandra surname: Saphirak fullname: Saphirak, Cassandra organization: a joint endeavor of the University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health – sequence: 5 givenname: Sandra surname: DeLuccia fullname: DeLuccia, Sandra organization: Harvard Pilgrim Health Care Institute, Harvard Medical School – sequence: 6 givenname: Muthalagu surname: Ramanathan fullname: Ramanathan, Muthalagu organization: UMass Memorial Medical Center – sequence: 7 givenname: Tejaswini surname: Dhawale fullname: Dhawale, Tejaswini organization: Massachusetts General Hospital – sequence: 8 givenname: Sonali surname: Harchandani fullname: Harchandani, Sonali organization: UMass Memorial Medical Center – sequence: 9 givenname: Christopher surname: Delude fullname: Delude, Christopher organization: a joint endeavor of the University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health – sequence: 10 givenname: Laura surname: Hou fullname: Hou, Laura organization: Harvard Pilgrim Health Care Institute, Harvard Medical School – sequence: 11 givenname: Autumn surname: Gertz fullname: Gertz, Autumn organization: Harvard Pilgrim Health Care Institute, Harvard Medical School – sequence: 12 givenname: Nina surname: DiNunzio fullname: DiNunzio, Nina organization: Harvard Pilgrim Health Care Institute, Harvard Medical School – sequence: 13 givenname: Cheryl N. surname: McMahill‐Walraven fullname: McMahill‐Walraven, Cheryl N. organization: Aetna, a CVS Health company – sequence: 14 givenname: Mano S. surname: Selvan fullname: Selvan, Mano S. organization: Humana Healthcare Research, Inc. (HHR) – sequence: 15 givenname: Justin surname: Vigeant fullname: Vigeant, Justin organization: Harvard Pilgrim Health Care Institute, Harvard Medical School – sequence: 16 givenname: David V. surname: Cole fullname: Cole, David V. organization: Harvard Pilgrim Health Care Institute, Harvard Medical School – sequence: 17 givenname: Kira surname: Leishear fullname: Leishear, Kira organization: Food and Drug Administration – sequence: 18 givenname: Jerry H. surname: Gurwitz fullname: Gurwitz, Jerry H. organization: a joint endeavor of the University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health – sequence: 19 givenname: Susan surname: Andrade fullname: Andrade, Susan organization: a joint endeavor of the University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health – sequence: 20 givenname: Noelle M. orcidid: 0000-0001-7090-2761 surname: Cocoros fullname: Cocoros, Noelle M. organization: Harvard Pilgrim Health Care Institute, Harvard Medical School |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33899311$$D View this record in MEDLINE/PubMed |
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| Notes | Funding information National Center for Advancing Translational Sciences, Grant/Award Number: KL2TR001454; U.S. Food and Drug Administration, Grant/Award Number: HHSF223201400030I An abstract was presented at the 2020 Society for Epidemiologic Annual Meeting (virtual) as a poster. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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Lymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of... Lymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of lymphoma... PurposeLymphoma is a health outcome of interest for drug safety studies. Studies using administrative claims data require the accurate identification of... |
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| SubjectTerms | algorithm Algorithms Biopsy Databases, Factual Diagnosis Electronics Flow cytometry Humans International Classification of Diseases Lymphoma Lymphoma, Non-Hodgkin - diagnosis Lymphoma, Non-Hodgkin - epidemiology Non-Hodgkin's lymphoma pharmacoepidemiology Pharmacovigilance Product safety Public health validation |
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| Title | Validation of an electronic algorithm for Hodgkin and non‐Hodgkin lymphoma in ICD‐10‐CM |
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