Deliwe P. Ngwezi MBChB, PhD , Anamaria Savu PhD , Roseanne O. Yeung MD, FRCPC, MPH , Sonia Butalia BSc, MD, FRCPC, MSc (Epi) , Padma Kaul PhD
{"title":"基于索赔的 1 型、2 型和妊娠期糖尿病替代算法的有效性","authors":"Deliwe P. Ngwezi MBChB, PhD , Anamaria Savu PhD , Roseanne O. Yeung MD, FRCPC, MPH , Sonia Butalia BSc, MD, FRCPC, MSc (Epi) , Padma Kaul PhD","doi":"10.1016/j.jcjd.2023.07.003","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>Our aim in this study was to evaluate the accuracy of alternative algorithms for identifying pre-existing type 1 or 2 diabetes (T1DM or T2DM) and gestational diabetes mellitus (GDM) in pregnant women.</p></div><div><h3>Methods</h3><p>Data from a clinical registry of pregnant women presenting to an Edmonton diabetes clinic between 2002 and 2009 were linked and administrative health records. Three algorithms for identifying women with T1DM<span>, T2DM, and GDM based on International Classification of Diseases---tenth revision (ICD-10) codes were assessed: delivery hospitalization records (Algorithm #1), outpatient clinics during pregnancy (Algorithm #2), and delivery hospitalization plus outpatient clinics during pregnancy (Algorithm #3). In a subset of women with clinic visits between 2005 and 2009, we examined the performance of an additional Algorithm #4 based on Algorithm #3 plus outpatient clinics in the 2 years before pregnancy. Using the diabetes clinical registry as the “gold standard,” we calculated true positive rates and agreement levels for the algorithms.</span></p></div><div><h3>Results</h3><p>The clinical registry included data on 928 pregnancies, of which 90 were T1DM, 89 were T2DM, and 749 were GDM. Algorithm #3 had the highest true positive rate for the detection of T1DM, T2DM, and GDM of 94%, 72%, and 99.9%, respectively, resulting in an overall agreement of 97% in diagnosis between the administrative databases and the clinical registry. Algorithm #4 did not provide much improvement over Algorithm #3 in overall agreement.</p></div><div><h3>Conclusions</h3><p>An algorithm based on ICD-10 codes in the delivery hospitalization and outpatient clinic records during pregnancy can be used to accurately identify women with T1DM, T2DM, and GDM.</p></div>","PeriodicalId":9565,"journal":{"name":"Canadian Journal of Diabetes","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Validity of Alternative Claims-based Algorithms for Type 1, Type 2, and Gestational Diabetes in Pregnancy\",\"authors\":\"Deliwe P. Ngwezi MBChB, PhD , Anamaria Savu PhD , Roseanne O. Yeung MD, FRCPC, MPH , Sonia Butalia BSc, MD, FRCPC, MSc (Epi) , Padma Kaul PhD\",\"doi\":\"10.1016/j.jcjd.2023.07.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>Our aim in this study was to evaluate the accuracy of alternative algorithms for identifying pre-existing type 1 or 2 diabetes (T1DM or T2DM) and gestational diabetes mellitus (GDM) in pregnant women.</p></div><div><h3>Methods</h3><p>Data from a clinical registry of pregnant women presenting to an Edmonton diabetes clinic between 2002 and 2009 were linked and administrative health records. Three algorithms for identifying women with T1DM<span>, T2DM, and GDM based on International Classification of Diseases---tenth revision (ICD-10) codes were assessed: delivery hospitalization records (Algorithm #1), outpatient clinics during pregnancy (Algorithm #2), and delivery hospitalization plus outpatient clinics during pregnancy (Algorithm #3). In a subset of women with clinic visits between 2005 and 2009, we examined the performance of an additional Algorithm #4 based on Algorithm #3 plus outpatient clinics in the 2 years before pregnancy. Using the diabetes clinical registry as the “gold standard,” we calculated true positive rates and agreement levels for the algorithms.</span></p></div><div><h3>Results</h3><p>The clinical registry included data on 928 pregnancies, of which 90 were T1DM, 89 were T2DM, and 749 were GDM. Algorithm #3 had the highest true positive rate for the detection of T1DM, T2DM, and GDM of 94%, 72%, and 99.9%, respectively, resulting in an overall agreement of 97% in diagnosis between the administrative databases and the clinical registry. Algorithm #4 did not provide much improvement over Algorithm #3 in overall agreement.</p></div><div><h3>Conclusions</h3><p>An algorithm based on ICD-10 codes in the delivery hospitalization and outpatient clinic records during pregnancy can be used to accurately identify women with T1DM, T2DM, and GDM.</p></div>\",\"PeriodicalId\":9565,\"journal\":{\"name\":\"Canadian Journal of Diabetes\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2023-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Canadian Journal of Diabetes\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1499267123001648\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Journal of Diabetes","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1499267123001648","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Validity of Alternative Claims-based Algorithms for Type 1, Type 2, and Gestational Diabetes in Pregnancy
Objective
Our aim in this study was to evaluate the accuracy of alternative algorithms for identifying pre-existing type 1 or 2 diabetes (T1DM or T2DM) and gestational diabetes mellitus (GDM) in pregnant women.
Methods
Data from a clinical registry of pregnant women presenting to an Edmonton diabetes clinic between 2002 and 2009 were linked and administrative health records. Three algorithms for identifying women with T1DM, T2DM, and GDM based on International Classification of Diseases---tenth revision (ICD-10) codes were assessed: delivery hospitalization records (Algorithm #1), outpatient clinics during pregnancy (Algorithm #2), and delivery hospitalization plus outpatient clinics during pregnancy (Algorithm #3). In a subset of women with clinic visits between 2005 and 2009, we examined the performance of an additional Algorithm #4 based on Algorithm #3 plus outpatient clinics in the 2 years before pregnancy. Using the diabetes clinical registry as the “gold standard,” we calculated true positive rates and agreement levels for the algorithms.
Results
The clinical registry included data on 928 pregnancies, of which 90 were T1DM, 89 were T2DM, and 749 were GDM. Algorithm #3 had the highest true positive rate for the detection of T1DM, T2DM, and GDM of 94%, 72%, and 99.9%, respectively, resulting in an overall agreement of 97% in diagnosis between the administrative databases and the clinical registry. Algorithm #4 did not provide much improvement over Algorithm #3 in overall agreement.
Conclusions
An algorithm based on ICD-10 codes in the delivery hospitalization and outpatient clinic records during pregnancy can be used to accurately identify women with T1DM, T2DM, and GDM.
期刊介绍:
The Canadian Journal of Diabetes is Canada''s only diabetes-oriented, peer-reviewed, interdisciplinary journal for diabetes health-care professionals.
Published bimonthly, the Canadian Journal of Diabetes contains original articles; reviews; case reports; shorter articles such as Perspectives in Practice, Practical Diabetes and Innovations in Diabetes Care; Diabetes Dilemmas and Letters to the Editor.