Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database

OBJECTIVE A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes–related symptoms using a large nationwide electronic health record (EHR) database. Methods We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort (n = 1,136,301 patients) was identified using a rule-based phenotype method. A multistep procedure was then used to identify type 2 diabetes–related symptoms based on International Classification of Diseases, 9th and 10th revisions, diagnosis codes. Type 2 diabetes–related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. Results Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21–60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies. Conclusion To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes–related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified.

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Characterization of Symptoms and Symptom Clusters for Type 2 Diabetes Using a Large Nationwide Electronic Health Record Database

10.2337/figshare.17180819.v1 ◽

2022 ◽

Author(s):

Veronica Brady ◽

Meagan Whisenant ◽

Xueying Wang ◽

Vi K. Ly ◽

Gen Zhu ◽

...

Keyword(s):

Type 2 Diabetes ◽

Electronic Health Record ◽

Temporal Patterns ◽

International Classification Of Diseases ◽

Symptom Clusters ◽

Health Record ◽

Patient Reported ◽

Classification Of Diseases ◽

Electronic Health

Purpose. A variety of symptoms may be associated with type 2 diabetes and its complications. Symptoms in chronic diseases may be described in terms of prevalence, severity, and trajectory and often co-occur in groups, known as symptom clusters, which may be representative of a common etiology. The purpose of this study was to characterize type 2 diabetes–related symptoms using a large nationwide electronic health record (EHR) database. Methods. We acquired the Cerner Health Facts, a nationwide EHR database. The type 2 diabetes cohort ( n = 1,136,301 patients) was identified using a rule-based phenotype method. A multi-step procedure was then used to identify type 2 diabetes–related symptoms based on International Classification of Diseases, 9th and 10th revisions, diagnosis codes. Type 2 diabetes–related symptoms and co-occurring symptom clusters, including their temporal patterns, were characterized based the longitudinal EHR data. Results. Patients had a mean age of 61.4 years, 51.2% were female, and 70.0% were White. Among 1,136,301 patients, there were 8,008,276 occurrences of 59 symptoms. The most frequently reported symptoms included pain, heartburn, shortness of breath, fatigue, and swelling, which occurred in 21–60% of the patients. We also observed over-represented type 2 diabetes symptoms, including difficulty speaking, feeling confused, trouble remembering, weakness, and drowsiness/sleepiness. Some of these are rare and difficult to detect by traditional patient-reported outcomes studies. Conclusion. To the best of our knowledge, this is the first study to use a nationwide EHR database to characterize type 2 diabetes–related symptoms and their temporal patterns. Fifty-nine symptoms, including both over-represented and rare diabetes-related symptoms, were identified.

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Development of quality indicators for type 2 diabetes, extractable from the electronic health record of the general physician. A rand-modified Delphi method

Primary Care Diabetes ◽

10.1016/j.pcd.2019.05.002 ◽

2020 ◽

Vol 14 (1) ◽

pp. 75-84 ◽

Cited By ~ 2

Author(s):

Steve A. Van den Bulck ◽

Patrik Vankrunkelsven ◽

Geert Goderis ◽

Lien Broekx ◽

Kathleen Dreesen ◽

...

Keyword(s):

Type 2 Diabetes ◽

Electronic Health Record ◽

Quality Indicators ◽

Delphi Method ◽

Health Record ◽

General Physician ◽

Modified Delphi Method ◽

Modified Delphi ◽

Electronic Health

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Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting Approach

Artificial Intelligence in Medicine ◽

10.1016/j.artmed.2020.101847 ◽

2020 ◽

Vol 105 ◽

pp. 101847

Author(s):

Michele Bernardini ◽

Micaela Morettini ◽

Luca Romeo ◽

Emanuele Frontoni ◽

Laura Burattini

Keyword(s):

Type 2 Diabetes ◽

General Practitioner ◽

Electronic Health Record ◽

Diabetes Risk ◽

Health Record ◽

Temporal Prediction ◽

Risk Condition ◽

Electronic Health

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REAL-WORLD EVALUATION OF WEIGHT LOSS IN PATIENTS WITH TYPE 2 DIABETES MELLITUS TREATED WITH CANAGLIFLOZIN - AN ELECTRONIC HEALTH RECORD-BASED STUDY

Value in Health ◽

10.1016/j.jval.2016.03.1314 ◽

2016 ◽

Vol 19 (3) ◽

pp. A198 ◽

Cited By ~ 1

Author(s):

P. Lefebvre ◽

W. Chow ◽

D. Pilon ◽

B. Emond ◽

M. Lafeuille ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Weight Loss ◽

Type 2 Diabetes Mellitus ◽

Electronic Health Record ◽

Real World ◽

Health Record ◽

Electronic Health

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Real-world glycemic, blood pressure, and weight control in patients with type 2 diabetes mellitus treated with canagliflozin—an electronic health-record-based study

Current Medical Research and Opinion ◽

10.1080/03007995.2016.1183604 ◽

2016 ◽

Vol 32 (6) ◽

pp. 1151-1159 ◽

Cited By ~ 13

Author(s):

Patrick Lefebvre ◽

Dominic Pilon ◽

Marie-Noëlle Robitaille ◽

Marie-Hélène Lafeuille ◽

Wing Chow ◽

...

Keyword(s):

Diabetes Mellitus ◽

Blood Pressure ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Electronic Health Record ◽

Real World ◽

Weight Control ◽

Health Record ◽

Electronic Health

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Real-time surveillance for chronic conditions in Massachusetts using EHR data

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v9i1.7645 ◽

2017 ◽

Vol 9 (1) ◽

Author(s):

Noelle Cocoros ◽

John Menchaca ◽

Michael Klompas

Keyword(s):

Public Health ◽

Type 2 Diabetes ◽

Electronic Health Record ◽

Chronic Diseases ◽

The State ◽

Community Level ◽

Health Record ◽

Surveillance Systems ◽

Electronic Health

ObjectiveTo assess the feasibility of tracking the prevalence of chronicconditions at the state and community level over time using MDPHnet,a distributed network for querying electronic health record systemsIntroductionPublic health agencies and researchers have traditionally reliedon the Behavioral Risk Factor Surveillance System (BRFSS) andsimilar tools for surveillance of non-reportable conditions. Thesetools are valuable but the data are delayed by more than a year,limited in scope, and based only on participant self-report. Thesecharacteristics limit the utility of traditional surveillance systems forprogram monitoring and impact assessments. Automated surveillanceusing electronic health record (EHR) data has the potential to increasethe efficiency, breadth, accuracy, and timeliness of surveillance. Wesought to assess the feasibility and utility of public health surveillancefor chronic diseases using EHR data using MDPHnet. MDPHnet isa distributed data network that allows the Massachusetts Departmentof Public Health to query participating practices’ EHR data for thepurposes of public health surveillance (www.esphealth.org). Practicesretain the ability to approve queries on a case-by-case basis and thenetwork is updated daily.MethodsWe queried the quarterly prevalence of pediatric asthma, smoking,type 2 diabetes, obesity, overweight, and hypertension statewideand in 9 Massachusetts communities between January 1, 2012 andJuly 1, 2016. We selected these 9 communities because they wereparticipating in a state-funded initiative to decrease the prevalenceof one or more of these conditions. Conditions were defined usingalgorithms based upon vital signs, diagnosis codes, laboratorymeasures, prescriptions, and self-reported smoking status. Eligiblepatients were those with at least 1 encounter of any kind within the2 years preceding the start of each quarter. Results were adjusted forage, sex, and race / ethnicity using the 2010 Massachusetts censusdata.ResultsSurveillance data were available for 1.2 million people overall,approximately 20% of the state population. Coverage varied bycommunity with >28% coverage for 7 of the communities and11% coverage in the eighth. The ninth community had only 2%coverage and was dropped from further analyses. The race / ethnicitydistribution in MDPHnet data was comparable to census datastatewide and in most communities. Queries for all six conditionssuccessfully executed across the network for all time periods ofinterest. The prevalence of asthma among children under 10 yrs rosefrom 12% in January 2012 to 13% in July 2016. Current smoking inadults age≥20 rose from 14% in 2013 to 16% in 2016 (we excludedresults from 2012 due to changes in documentation propelled by theintroduction of meaningful use criteria). This is comparable to the15% rate of smoking per BRFSS in 20141. Obesity among adultsincreased slightly from 22% to 24% during the study period, resultsnearly identical to the most recent BRFSS results for Massachusetts(23% in 2014 and 24% in 2015)2. The prevalence of each conditionvaried widely across the communities under study. For example, forthe third quarter of 2016, the prevalence of asthma among childrenunder 10 ranged from 5% to 23% depending on the community,the prevalence of smoking among adults ranged from 11% to 35%,and the prevalence of type 2 diabetes among adults ranged from7% to 14%. We also examined differences in disease estimates byrace / ethnicity. Substantial racial / ethnic differences were evidentfor type 2 diabetes among adults, with whites having the lowestprevalence at 7% and blacks having the highest at 12% in the thirdquarter of 2016; this trend was consistent over the study period.ConclusionsOur study demonstrates that MDPHnet can provide theMassachusetts Department of Public Health with timely population-level estimates of chronic diseases for numerous conditions at boththe state and community level. MDPHnet surveillance providesprevalence estimates that align well with BRFSS and other traditionalsurveillance sources but is able to make surveillance more timelyand more efficient with more geographical specificity compared totraditional surveillance systems. Our ability to generate real-timetime-series data supports the use of MDPHnet as a source for project/program evaluation.

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Establishing an electronic health record–supported approach for outreach to and recruitment of persons at high risk of type 2 diabetes in clinical trials: The vitamin D and type 2 diabetes (D2d) study experience

Clinical Trials ◽

10.1177/1740774519839062 ◽

2019 ◽

Vol 16 (3) ◽

pp. 306-315 ◽

Cited By ~ 4

Author(s):

Vanita R Aroda ◽

Patricia R Sheehan ◽

Ellen M Vickery ◽

Myrlene A Staten ◽

Erin S LeBlanc ◽

...

Keyword(s):

Type 2 Diabetes ◽

Vitamin D ◽

Clinical Trials ◽

Electronic Health Record ◽

System Level ◽

Health Record ◽

Recruitment Methods ◽

Clinical Environments ◽

Electronic Health

Aims To establish recruitment approaches that leverage electronic health records in multicenter prediabetes/diabetes clinical trials and compare recruitment outcomes between electronic health record–supported and conventional recruitment methods. Methods Observational analysis of recruitment approaches in the vitamin D and type 2 diabetes (D2d) study, a multicenter trial in participants with prediabetes. Outcomes were adoption of electronic health record–supported recruitment approaches by sites, number of participants screened, recruitment performance (proportion screened who were randomized), and characteristics of participants from electronic health record–supported versus non–electronic health record methods. Results In total, 2423 participants were randomized: 1920 from electronic health record (mean age of 60 years, 41% women, 68% White) and 503 from non–electronic health record sources (mean age of 56.9 years, 58% women, 61% White). Electronic health record–supported recruitment was adopted by 21 of 22 sites. Electronic health record–supported recruitment was associated with more participants screened versus non–electronic health record methods (4969 vs 2166 participants screened), higher performance (38.6% vs 22.7%), and more randomizations (1918 vs 505). Participants recruited via electronic health record were older, included fewer women and minorities, and reported higher use of dietary supplements. Electronic health record–supported recruitment was incorporated in diverse clinical environments, engaging clinicians either at the individual or the healthcare system level. Conclusion Establishing electronic health record–supported recruitment approaches across a multicenter prediabetes/diabetes trial is feasible and can be adopted by diverse clinical environments.

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