+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

Developing a case definition for type 1 diabetes mellitus in a primary care electronic medical record database: an exploratory study



Developing a case definition for type 1 diabetes mellitus in a primary care electronic medical record database: an exploratory study



Cmaj Open 7(2): E246



Identifying cases of disease in primary care electronic medical records (EMRs) is important for surveillance, research, quality improvement and clinical care. We aimed to develop and validate a case definition for type 1 diabetes mellitus using EMRs. For this exploratory study, we used EMR data from the Southern Alberta Primary Care Network within the Canadian Primary Care Sentinel Surveillance Network (CPCSSN), for the period 2008 to 2016. For patients identified as having diabetes mellitus according to the existing CPCSSN case definition, we asked family physicians to confirm the diabetes subtype, to create the reference standard. We used 3 decision-tree classification algorithms and least absolute shrinkage and selection operator logistic regression to identify variables that correctly distinguished between type 1 and type 2 diabetes cases. We identified a total of 1309 people with type 1 or type 2 diabetes, 110 of whom were confirmed by their physicians as having type 1 diabetes. Two machine learning algorithms were useful in identifying these cases in the EMRs. The first algorithm used "type 1" text words or age less than 22 years at time of initial diabetes diagnosis; this algorithm had sensitivity 42.7% (95% confidence interval [CI] 33.5%-52.5%), specificity 99.3% (95% CI 98.6%-99.7%), positive predictive value 85.5% (95% CI 72.8%-93.1%) and negative predictive value 94.9% (95% CI 93.5%-96.1%). The second algorithm used a combination of free-text terms, insulin prescriptions and age; it had sensitivity 87.3% (95% CI 79.2%-92.6%), specificity 85.4% (95% CI 83.2%-87.3%), positive predictive value 35.6% (95% CI 29.9%-41.6%) and negative predictive value 98.6% (95% CI 97.7%-99.2%). We used machine learning to develop and validate 2 case definitions that achieve different goals in distinguishing between type 1 and type 2 diabetes in CPCSSN data. Further validation and testing with a larger and more diverse sample are recommended.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 066759266

Download citation: RISBibTeXText

PMID: 31061005

DOI: 10.9778/cmajo.20180142


Related references

Prevalence of obesity, type II diabetes mellitus, hyperlipidemia, and hypertension in the United States: findings from the GE Centricity Electronic Medical Record database. Population Health Management 13(3): 151-161, 2010

Diabetes and associated risk factors in patients referred for physical therapy in a national primary care electronic medical record database. Physical Therapy 88(11): 1408-1416, 2008

From patient care to research: a validation study examining the factors contributing to data quality in a primary care electronic medical record database. Bmc Family Practice 16: 11, 2015

Opportunities and Challenges in Developing a Cohort of Patients with Type 2 Diabetes Mellitus Using Electronic Primary Care Data. Plos one 11(11): E0162236, 2016

Using electronic medical records analysis to investigate the effectiveness of lifestyle programs in real-world primary care is challenging: a case study in diabetes mellitus. Journal of Clinical Epidemiology 65(7): 785-792, 2012

Use of primary care electronic medical record database in drug efficacy research on cardiovascular outcomes: comparison of database and randomised controlled trial findings. Bmj 338: B81, 2009

Identification of Dyslipidemic Patients Attending Primary Care Clinics Using Electronic Medical Record (EMR) Data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) Database. Journal of Medical Systems 41(3): 45, 2017

Prevalence of diagnosed type 2 diabetes mellitus in Greenland 2008: the impact of electronic database implementation on the quality of diabetes care. International Journal of Circumpolar Health 68(1): 34-41, 2009

The completeness of electronic medical record data for patients with Type 2 Diabetes in primary care and its implications for computer modelling of predicted clinical outcomes. Primary Care Diabetes 10(5): 352-359, 2016

Accuracy of pneumonia hospital admissions in a primary care electronic medical record database. Pharmacoepidemiology and Drug Safety 21(6): 659-665, 2012

Capture of osteoporosis and fracture information in an electronic medical record database from primary care. AMIA .. Annual Symposium Proceedings. AMIA Symposium 2014: 240-248, 2014

Identifying Data Elements to Measure Frailty in a Dutch Nationwide Electronic Medical Record Database for Use in Postmarketing Safety Evaluation: An Exploratory Study. Drug Safety 42(6): 713-719, 2019

End-user support for a primary care electronic medical record: a qualitative case study of a vendor's perspective. Informatics in Primary Care 20(3): 185-195, 2012

A novel method for studying the temporal relationship between type 2 diabetes mellitus and cancer using the electronic medical record. Bmc Medical Informatics and Decision Making 14: 38, 2014

An algorithm for identification and classification of individuals with type 1 and type 2 diabetes mellitus in a large primary care database. Clinical Epidemiology 8: 373-380, 2016