Automated Detection and Classification of Type 1 Versus Type 2 Diabetes Using Electronic Health Record Data

Klompas, Michael; Eggleston, Emma M.; McVetta, Jason; Lazarus, Ross; Li, Lingling; Platt, Richard

doi:10.2337/dc12-0964

Cited by 179 publications

(186 citation statements)

References 16 publications

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“…Second, classification of diabetes status is necessarily imperfect, although sensitivity is estimated at 91% and has a positive predictive value of 94% (46). Accurately distinguishing between type 1 and type 2 diabetes using electronic diagnosis data or duration of diabetes is not possible (7). Third, CV events were based on primary hospital discharge diagnoses and were not adjudicated.…”

Section: Discussionmentioning

confidence: 99%

“…Diabetes status was classified using a previously validated algorithm (5,7–9). Study criteria for diabetes required either one or more inpatient diabetes diagnosis codes (ICD-9 Clinical Modification 250.x, 357.2, 366.41, 362.01–362.07) or any combination of two or more of the following events on separate days no more than 2 years apart: 1 ) A1C ≥6.5% (48 mmol/mol), 2 ) fasting plasma glucose ≥126 mg/dL, 3 ) random plasma glucose ≥200 mg/dL, 4 ) outpatient visit diabetes diagnosis code (same codes as that used for inpatients), or 5 ) any filled prescription for a glucose-lowering medication.…”

Section: Methodsmentioning

confidence: 99%

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Preventable Major Cardiovascular Events Associated With Uncontrolled Glucose, Blood Pressure, and Lipids and Active Smoking in Adults With Diabetes With and Without Cardiovascular Disease: A Contemporary Analysis

Vazquez‐Benitez

Desai

et al. 2015

Diabetes Care

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OBJECTIVEThe objective of this study was to assess the incidence of major cardiovascular (CV) hospitalization events and all-cause deaths among adults with diabetes with or without CV disease (CVD) associated with inadequately controlled glycated hemoglobin (A1C), high LDL cholesterol (LDL-C), high blood pressure (BP), and current smoking.RESEARCH DESIGN AND METHODSStudy subjects included 859,617 adults with diabetes enrolled for more than 6 months during 2005–2011 in a network of 11 U.S. integrated health care organizations. Inadequate risk factor control was classified as LDL-C ≥100 mg/dL, A1C ≥7% (53 mmol/mol), BP ≥140/90 mm Hg, or smoking. Major CV events were based on primary hospital discharge diagnoses for myocardial infarction (MI) and acute coronary syndrome (ACS), stroke, or heart failure (HF). Five-year incidence rates, rate ratios, and average attributable fractions were estimated using multivariable Poisson regression models.RESULTSMean (SD) age at baseline was 59 (14) years; 48% of subjects were female, 45% were white, and 31% had CVD. Mean follow-up was 59 months. Event rates per 100 person-years for adults with diabetes and CVD versus those without CVD were 6.0 vs. 1.7 for MI/ACS, 5.3 vs. 1.5 for stroke, 8.4 vs. 1.2 for HF, 18.1 vs. 40 for all CV events, and 23.5 vs. 5.0 for all-cause mortality. The percentages of CV events and deaths associated with inadequate risk factor control were 11% and 3%, respectively, for those with CVD and 34% and 7%, respectively, for those without CVD.CONCLUSIONSAdditional attention to traditional CV risk factors could yield further substantive reductions in CV events and mortality in adults with diabetes.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Preventable Major Cardiovascular Events Associated With Uncontrolled Glucose, Blood Pressure, and Lipids and Active Smoking in Adults With Diabetes With and Without Cardiovascular Disease: A Contemporary Analysis

Vazquez‐Benitez

Desai

et al. 2015

Diabetes Care

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“…Clinical algorithms for types I and 2 diabetes have been developed in the ESPnet system using ICD-9 codes for diabetes, an elevated fasting glucose or hemoglobin A 1c , prescription for an oral hypoglycemic agent other than metformin, or a prescription for insulin not during pregnancy (28). The optimized algorithm for type 1 diabetes had a sensitivity of 97% and a PPV of 88%.…”

Section: Ehrs For Chronic Disease Surveillancementioning

confidence: 99%

Uses of Electronic Health Records for Public Health Surveillance to Advance Public Health

Birkhead

Klompas

Shah

2015

Annu. Rev. Public Health

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279

203

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Public health surveillance conducted by health departments in the United States has improved in completeness and timeliness owing to electronic laboratory reporting. However, the collection of detailed clinical information about reported cases, which is necessary to confirm the diagnosis, to understand transmission, or to determine disease-related risk factors, is still heavily dependent on manual processes. The increasing prevalence and functionality of electronic health record (EHR) systems in the United States present important opportunities to advance public health surveillance. EHR data have the potential to further increase the breadth, detail, timeliness, and completeness of public health surveillance and thereby provide better data to guide public health interventions. EHRs also provide a unique opportunity to expand the role and vision of current surveillance efforts and to help bridge the gap between public health practice and clinical medicine.

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“…These included demographic variables (age, race/ethnicity), type of diabetes, body mass index, hemoglobin A1c, estimated glomerular filtration rate (eGFR), utilization (recent hospitalization, emergency department, and severe hypoglycemic event), important comorbidities (retinopathy, atherosclerotic cardiovascular disease, depression, heart failure), and medication use (insulin, metformin, and number of classes of glucose lowering medications). Diabetes type was defined using a modification of an algorithm developed by Klompas et al (11). For BMI, hemoglobin A1c, and serum creatinine, we used the most recent value in the 2 years preceding the initial index date.…”

Section: Subjects Materials and Methodsmentioning

confidence: 99%

Predicting the 6-month risk of severe hypoglycemia among adults with diabetes: Development and external validation of a prediction model

Schroeder

Goodrich

et al. 2017

Journal of Diabetes and its Complications

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Aims To develop and externally validate a prediction model for the six-month risk of a severe hypoglycemic event among individuals with pharmacologically treated diabetes. Methods The development cohort consisted of 31,674 Kaiser Permanente Colorado members with pharmacologically treated diabetes (2007–2015). The validation cohorts consisted of 38,764 Kaiser Permanente Northwest members and 12,035 HealthPartners members. Variables were chosen that would be available in electronic health records. We developed 16-variable and 6-variable models, using a Cox counting model process that allows for the inclusion of multiple six-month observation periods per person. Results Across the three cohorts, there were 850,992 six-month observation periods, and 10,448 periods with at least one severe hypoglycemic event. The 6-variable model contained age, diabetes type, HgbA1c, eGFR, history of a hypoglycemic event in the prior year, and insulin use. Both prediction models performed well, with good calibration and c-statistics of 0.84 and 0.81 for the 16-variable and 6-variable models, respectively. In the external validation cohorts, the c-statistics were 0.80–0.84. Conclusions We developed and validated two prediction models for predicting the six-month risk of hypoglycemia. The 16-variable model had slightly better performance than the 6-variable model, but in some practice settings, use of the simpler model may be preferred.

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Automated Detection and Classification of Type 1 Versus Type 2 Diabetes Using Electronic Health Record Data

Cited by 179 publications

References 16 publications

Preventable Major Cardiovascular Events Associated With Uncontrolled Glucose, Blood Pressure, and Lipids and Active Smoking in Adults With Diabetes With and Without Cardiovascular Disease: A Contemporary Analysis

Preventable Major Cardiovascular Events Associated With Uncontrolled Glucose, Blood Pressure, and Lipids and Active Smoking in Adults With Diabetes With and Without Cardiovascular Disease: A Contemporary Analysis

Uses of Electronic Health Records for Public Health Surveillance to Advance Public Health

Predicting the 6-month risk of severe hypoglycemia among adults with diabetes: Development and external validation of a prediction model

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