scholarly journals Treatment Patterns of Type 2 Diabetes Assessed Using a Common Data Model Based on Electronic Health Records of 2000–2019

2021 ◽  
Vol 36 (36) ◽  
Author(s):  
Kyung Ae Lee ◽  
Heung Yong Jin ◽  
Yu Ji Kim ◽  
Yong-Jin Im ◽  
Eun-Young Kim ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Electronic Health Records ◽  
Data Model ◽  
Treatment Patterns ◽  
Common Data Model ◽  
Health Records ◽  
Model Based ◽  
Electronic Health

scholarly journals Trends in Medication Utilization and Glycemic Control Among Type 2 Diabetes Using a Common Data Model Based on Electronic Health Records From 2000 to 2019

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A481-A481
Author(s):  
Kyung Ae Lee ◽  
Heung Yong Jin ◽  
Seung Han Jeong ◽  
Jang Hyeon Kim ◽  
Yuji Kim ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Glycemic Control ◽  
Data Model ◽  
Treatment Patterns ◽  
Target Range ◽  
Common Data Model ◽  
Diabetes Research ◽  
Medication Utilization ◽  
Electronic Health

Abstract Analyzing the treatment patterns of type 2 diabetes (T2DM) in real practice helps to understand the flow of diabetes management and establish further management plans. Observational Health Data Sciences and Informatics (OHDSI) is an international collaboration created an international data network (Observational Medical Outcomes Partnership Common Data Model, OMOP-CDM). This study was aim to analyze treatment patterns of T2DM using the OMOP-CDM based on electronic health record (EHR) data and to assess whether CDM analysis was feasible to diabetes research. This is a retrospective, observational study using the EHR data of Jeonbuk National University Hospital (JNUH) transformed into OMOP-CDM. The data consisted of medical records of patients visits from January 2000 to December 2019. ATLAS ver. 2.7.6, an OHDSI’s open-source software is publicly available, was used for analysis. The 20 year old EHR data of a JNUH contain about 1.5 million patients. The proportion of adult patients treated for T2DM increased from 1,867 (1.6%) in 2000 to 9,972 (5.1%) in 2019. Sulfonylurea (SU) was the most prescribed drug (73%) followed by metformin (55%) in 2000. On the other hand, in 2019, metformin was the most prescribed (64%), and DPP-4 inhibitor prescription increased rapidly up to 55%, while the SU prescription rate decreased to 36%. The rate of insulin treatment ranged from 16% to 24%, which is higher than national surveyed based on health insurance data. Over time, monotherapy decreased while dual, triple, and quadruple combinations steadily increased. Dual combination was the most common with metformin and DPP-4 inhibitor, triple combination was the most with metformin, SU, and DPP-4 inhibitor in 2019. In analysis of annual HbA1c trends, the proportion of patients with HbA1c of 7% or lower increased (from 32.8% 2000 to 50.2% in 2019). Proportion of patients with HbA1c of 9% or more decreased from 30% to 12%. However, it was found that about half of T2DM patients still had HbA1c values above the target range. In addition, the number of patients who visited our emergency room for severe hypoglycemia did not decrease. Present study revealed that CDM analysis was feasible for diabetes research. Medication utilization patterns have changed significantly over the past 20 years with a shift towards newer drugs. Despite these changes and clinical efforts, improvement in glycemic control is still a challenge and hypoglycemic is still a problem to overcome.

Predicting the onset of type 2 diabetes using wide and deep learning with electronic health records

2019 ◽  
Vol 182 ◽  
pp. 105055 ◽  
Author(s):  
Binh P. Nguyen ◽  
Hung N. Pham ◽  
Hop Tran ◽  
Nhung Nghiem ◽  
Quang H. Nguyen ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Deep Learning ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health

scholarly journals Identification of patients with hemoglobin SS/Sβ0 thalassemia disease and pain crises within electronic health records

2018 ◽  
Vol 2 (11) ◽  
pp. 1172-1179 ◽  
Author(s):  
Ashima Singh ◽  
Javier Mora ◽  
Julie A. Panepinto
Keyword(s):  
Electronic Health Records ◽  
Acute Care ◽  
Sensitivity And Specificity ◽  
Data Model ◽  
High Sensitivity ◽  
Common Data Model ◽  
Health Records ◽  
Key Points ◽  
Electronic Health

Key Points The algorithms have high sensitivity and specificity to identify patients with hemoglobin SS/Sβ0 thalassemia and acute care pain encounters. Codes conforming to common data model are provided to facilitate adoption of algorithms and standardize definitions for EHR-based research.

scholarly journals A Distribution-based Method for Assessing The Differences between Clinical Trial Target Populations and Patient Populations in Electronic Health Records

2014 ◽  
Vol 05 (02) ◽  
pp. 463-479 ◽  
Author(s):  
P. Ryan ◽  
Y. Zhang ◽  
F. Liu ◽  
J. Gao ◽  
J.T. Bigger ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Electronic Health Records ◽  
Real World ◽  
World Population ◽  
Health Records ◽  
Target Populations ◽  
Electronic Health

SummaryObjective: To improve the transparency of clinical trial generalizability and to illustrate the method using Type 2 diabetes as an example.Methods: Our data included 1,761 diabetes clinical trials and the electronic health records (EHR) of 26,120 patients with Type 2 diabetes who visited Columbia University Medical Center of New-York Presbyterian Hospital. The two populations were compared using the Generalizability Index for Study Traits (GIST) on the earliest diagnosis age and the mean hemoglobin A1c (HbA1c) values.Results: Greater than 70% of Type 2 diabetes studies allow patients with HbA1c measures between 7 and 10.5, but less than 40% of studies allow HbA1c<7 and fewer than 45% of studies allow HbA1c>10.5. In the real-world population, only 38% of patients had HbA1c between 7 and 10.5, with 12% having values above the range and 52% having HbA1c<7. The GIST for HbA1c was 0.51. Most studies adopted broad age value ranges, with the most common restrictions excluding patients >80 or <18 years. Most of the real-world population fell within this range, but 2% of patients were <18 at time of first diagnosis and 8% were >80. The GIST for age was 0.75. Conclusions: We contribute a scalable method to profile and compare aggregated clinical trial target populations with EHR patient populations. We demonstrate that Type 2 diabetes studies are more generalizable with regard to age than they are with regard to HbA1c. We found that the generalizability of age increased from Phase 1 to Phase 3 while the generalizability of HbA1c decreased during those same phases. This method can generalize to other medical conditions and other continuous or binary variables. We envision the potential use of EHR data for examining the generaliz-ability of clinical trials and for defining population-representative clinical trial eligibility criteria.Citation: Weng C, Li Y, Ryan P, Zhang Y, Liu F, Gao J, Bigger JT, Hripcsak G. A distribution-based method for assessing the differences between clinical trial target populations and patient populations in electronic health records. Appl Clin Inf 2014; 5: 463–479 http://dx.doi.org/10.4338/ACI-2013-12-RA-0105

scholarly journals Substance use and mental diagnoses among adults with and without type 2 diabetes: Results from electronic health records data

2015 ◽  
Vol 156 ◽  
pp. 162-169 ◽  
Author(s):  
Li-Tzy Wu ◽  
Udi E. Ghitza ◽  
Bryan C. Batch ◽  
Michael J. Pencina ◽  
Leoncio Flavio Rojas ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Substance Use ◽  
Electronic Health Records ◽  
Health Records ◽  
Electronic Health

scholarly journals Epidemiology and determinants of non-diabetic hyperglycaemia and its conversion to type 2 diabetes mellitus, 2000–2015: cohort population study using UK electronic health records

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e040201 ◽  
Author(s):  
Rathi Ravindrarajah ◽  
David Reeves ◽  
Elizabeth Howarth ◽  
Rachel Meacock ◽  
Claudia Soiland-Reyes ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Electronic Health Records ◽  
Secondary Outcome ◽  
Health Records ◽  
Conversion Rates ◽  
Electronic Health ◽  
Over Time

ObjectivesTo study the characteristics of UK individuals identified with non-diabetic hyperglycaemia (NDH) and their conversion rates to type 2 diabetes mellitus (T2DM) from 2000 to 2015, using the Clinical Practice Research Datalink.DesignCohort study.SettingsUK primary Care Practices.ParticipantsElectronic health records identified 14 272 participants with NDH, from 2000 to 2015.Primary and secondary outcome measuresBaseline characteristics and conversion trends from NDH to T2DM were explored. Cox proportional hazards models evaluated predictors of conversion.ResultsCrude conversion was 4% within 6 months of NDH diagnosis, 7% annually, 13% within 2 years, 17% within 3 years and 23% within 5 years. However, 1-year conversion fell from 8% in 2000 to 4% in 2014. Individuals aged 45–54 were at the highest risk of developing T2DM (HR 1.20, 95% CI 1.15 to 1.25— compared with those aged 18–44), and the risk reduced with older age. A body mass index (BMI) above 30 kg/m2 was strongly associated with conversion (HR 2.02, 95% CI 1.92 to 2.13—compared with those with a normal BMI). Depression (HR 1.10, 95% CI 1.07 to 1.13), smoking (HR 1.07, 95% CI 1.03 to 1.11—compared with non-smokers) or residing in the most deprived areas (HR 1.17, 95% CI 1.11 to 1.24—compared with residents of the most affluent areas) was modestly associated with conversion.ConclusionAlthough the rate of conversion from NDH to T2DM fell between 2010 and 2015, this is likely due to changes over time in the cut-off points for defining NDH, and more people of lower diabetes risk being diagnosed with NDH over time. People aged 45–54, smokers, depressed, with high BMI and more deprived are at increased risk of conversion to T2DM.

scholarly journals Rationale, design and population description of the CREDENCE study: cardiovascular risk equations for diabetes patients from New Zealand and Chinese electronic health records

2021 ◽  
Author(s):  
Jingyuan Liang ◽  
Romana Pylypchuk ◽  
Xun Tang ◽  
Peng Shen ◽  
Xiaofei Liu ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Risk ◽  
New Zealand ◽  
Electronic Health Records ◽  
Developing Country ◽  
Risk Prediction ◽  
Health Records ◽  
Cardiovascular Risk Prediction ◽  
Electronic Health

AbstractThe cardiovascular risk equations for diabetes patients from New Zealand and Chinese electronic health records (CREDENCE) study is a unique prospectively designed investigation of cardiovascular risk in two large contemporary cohorts of people with type 2 diabetes from New Zealand (NZ) and China. The study was designed to derive equivalent cardiovascular risk prediction equations in a developed and a developing country, using the same epidemiological and statistical methodology. Two similar cohorts of people with type 2 diabetes were identified from large general population studies in China and New Zealand, which had been generated from longitudinal electronic health record systems. The CREDENCE study aims to determine whether cardiovascular risk prediction equations derived in patients with type 2 diabetes in a developed country are applicable in a developing country, and vice versa, by deriving and validating equivalent diabetes-specific cardiovascular risk prediction models from the two countries. Baseline data in CREDENCE was collected from October 2004 in New Zealand and from January 2010 in China. In the first stage of CREDENCE, a total of 93,207 patients (46,649 from NZ and 46,558 from China) were followed until December 31st 2018. Median follow-up was 7.0 years (New Zealand) and 5.7 years (China). There were 5926 (7.7% fatal) CVD events in the New Zealand cohort and 3650 (8.8% fatal) in the Chinese cohort. The research results have implications for policy makers, clinicians and the public and will facilitate personalised management of cardiovascular risk in people with type 2 diabetes worldwide.

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