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Identifying And Validating Type 1 And Type 2 Diabetic Cases Using Administrative Date: A Tree-Structured Model

Lo, Wei-Hsuan (2010) Identifying And Validating Type 1 And Type 2 Diabetic Cases Using Administrative Date: A Tree-Structured Model. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

Background: Planning, implementing, monitoring, temporal evolution and prognosis differ between type 1 diabetes (T1DM) and type 2 diabetes (T2DM). To date, few administrative diabetes registries have distinguished T1DM from T2DM, reflecting the lack of required differential information and possible recording bias. Objective: Using a classification tree model, we developed a prediction rule to distinguish T1DM from T2DM accurately, using information from a large administrative database.Methods: The Medical Archival Retrieval System (MARS) at the University of Pittsburgh Medical Center from 1/1/2000-9/30/2009 included administrative and clinical data for 209,642 unique diabetic patients aged ≥ 18 years. We identified 10,004 T1DM and 156,712 T2DM patients as probable or possible cases, based on clinical criteria. Classification tree models were fit using TIBCO Spotfire S+ 8.1 (TIBCO Software). We used 10-fold cross-validation to choose model size. We estimated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of T1DM.Results: The main predictors that distinguished T1DM from T2DM include age < 40 vs. ≥ 40 years, ICD-9 codes of T1DM or T2DM diagnosis, oral hypoglycemic agent use, insulin use, and episode(s) of diabetic ketoacidosis diagnosis. History of hypoglycemic coma, duration in the MARS database, in-patient diagnosis of diabetes, and number of complications (including myocardial infarction, coronary artery bypass graft, dialysis, neuropathy, retinopathy, and amputation) are ancillary predictors. The tree-structured model to predict T1DM from probable cases yields sensitivity (99.63%), specificity (99.28%), PPV (89.87%) and NPV (99.71%).Conclusion: Our preliminary predictive rule to distinguish between T1DM and T2DM cases in a large administrative database appears to be promising and needs to be validated. The public health significance is that being able to distinguish between these diabetes subtypes will allow future subtype-specific analyses of cost, morbidity, and mortality. Future work will focus on ascertaining the validity and generalizability of our predictive rule, by conducting a review of medical charts (as an internal validation) and applying the rule to another MARS dataset or other administrative databases (as external validations).


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Lo, Wei-Hsuanb87608035@ntu.edu.tw
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairStone, Rosyln Aroslyn@pitt.eduROSLYN
Committee MemberZgibor, Janice Cedcjan@pitt.eduEDCJAN
Committee MemberRuppert, Kristineruppertkm@upmc.edu
Committee MemberArena, Vincent Carena@pitt.eduARENA
Date: 28 June 2010
Date Type: Completion
Defense Date: 19 April 2010
Approval Date: 28 June 2010
Submission Date: 12 April 2010
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: classification and regression tree (CART); T1DM; T2DM; Type 1 diabetes; Type 2 diabetes; Diabetes mellitus; tree-structured model; validation; administrative data
Other ID: http://etd.library.pitt.edu/ETD/available/etd-04122010-221446/, etd-04122010-221446
Date Deposited: 10 Nov 2011 19:36
Last Modified: 15 Nov 2016 13:39
URI: http://d-scholarship.pitt.edu/id/eprint/7078

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