BACKGROUND: To date, few administrative diabetes mellitus (DM) registries have distinguished type 1 diabetes mellitus (T1DM) from type 2 diabetes mellitus (T2DM). OBJECTIVE: Using a classification tree model, a prediction rule was developed to distinguish T1DM from T2DM in a large administrative database. METHODS: The Medical Archival Retrieval System at the University of Pittsburgh Medical Center included administrative and clinical data from January 1, 2000, through September 30, 2009, for 209,647 DM patients aged ≥18 years. Probable cases (8,173 T1DM and 125,111 T2DM) were identified by applying clinical criteria to administrative data. Nonparametric classification tree models were fit using TIBCO Spotfire S+ 8.1 (TIBCO Software), with model size based on 10-fold cross validation. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of T1DM were estimated. RESULTS: The main predictors that distinguished T1DM from T2DM are age <40 years; International Classification of Disease, 9th revision, codes of T1DM or T2DM diagnosis; inpatient oral hypoglycemic agent use; inpatient insulin use; and episode(s) of diabetic ketoacidosis diagnosis. Compared with a complex clinical algorithm, the tree-structured model to predict T1DM had 92.8% sensitivity, 99.3% specificity, 89.5% PPV, and 99.5% NPV. CONCLUSION: The preliminary predictive rule appears to be promising. Being able to distinguish between DM subtypes in administrative databases will allow large-scale subtype-specific analyses of medical care costs, morbidity, and mortality.
BACKGROUND: To date, few administrative diabetes mellitus (DM) registries have distinguished type 1 diabetes mellitus (T1DM) from type 2 diabetes mellitus (T2DM). OBJECTIVE: Using a classification tree model, a prediction rule was developed to distinguish T1DM from T2DM in a large administrative database. METHODS: The Medical Archival Retrieval System at the University of Pittsburgh Medical Center included administrative and clinical data from January 1, 2000, through September 30, 2009, for 209,647 DMpatients aged ≥18 years. Probable cases (8,173 T1DM and 125,111 T2DM) were identified by applying clinical criteria to administrative data. Nonparametric classification tree models were fit using TIBCO Spotfire S+ 8.1 (TIBCO Software), with model size based on 10-fold cross validation. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of T1DM were estimated. RESULTS: The main predictors that distinguished T1DM from T2DM are age <40 years; International Classification of Disease, 9th revision, codes of T1DM or T2DM diagnosis; inpatient oral hypoglycemic agent use; inpatient insulin use; and episode(s) of diabetic ketoacidosis diagnosis. Compared with a complex clinical algorithm, the tree-structured model to predict T1DM had 92.8% sensitivity, 99.3% specificity, 89.5% PPV, and 99.5% NPV. CONCLUSION: The preliminary predictive rule appears to be promising. Being able to distinguish between DM subtypes in administrative databases will allow large-scale subtype-specific analyses of medical care costs, morbidity, and mortality.
Authors: Linda M Siminerio; Scott R Drab; Robert A Gabbay; Kathleen Gold; Sue McLaughlin; Gretchen A Piatt; Joe Solowiejczyk; Richard Weil Journal: Diabetes Educ Date: 2008 May-Jun Impact factor: 2.140
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Authors: Alanna Weisman; Karen Tu; Jacqueline Young; Matthew Kumar; Peter C Austin; Liisa Jaakkimainen; Lorraine Lipscombe; Ronnie Aronson; Gillian L Booth Journal: BMJ Open Diabetes Res Care Date: 2020-06
Authors: Lauren G Gilstrap; Michael E Chernew; Christina A Nguyen; Sartaj Alam; Barbara Bai; J Michael McWilliams; Bruce E Landon; Mary Beth Landrum Journal: JAMA Netw Open Date: 2019-08-02
Authors: Simona Cammarota; Lucio Marcello Falconio; Dario Bruzzese; Alberico Luigi Catapano; Manuela Casula; Anna Citarella; Luigi De Luca; Maria Elena Flacco; Lamberto Manzoli; Maria Masulli; Enrica Menditto; Andrea Mezzetti; Salvatore Riegler; Ettore Novellino; Gabriele Riccardi Journal: PLoS One Date: 2013-11-07 Impact factor: 3.240