scholarly journals Driving Type 2 Diabetes Risk Scores into Clinical Practice: Performance Analysis in Hospital Settings

2019 ◽  
Vol 8 (1) ◽  
pp. 107 ◽  
Author(s):  
Antonio Martinez-Millana ◽  
María Argente-Pla ◽  
Bernardo Valdivieso Martinez ◽  
Vicente Traver Salcedo ◽  
Juan Merino-Torres
Keyword(s):  
Type 2 Diabetes ◽  
High Risk ◽  
Electronic Health Records ◽  
Bayesian Network ◽  
Risk Score ◽  
Diabetes Risk ◽  
Risk Scores ◽  
Health Records ◽  
Electronic Health

Electronic health records and computational modelling have paved the way for the development of Type 2 Diabetes risk scores to identify subjects at high risk. Unfortunately, few risk scores have been externally validated, and their performance can be compromised when routine clinical data is used. The aim of this study was to assess the performance of well-established risk scores for Type 2 Diabetes using routinely collected clinical data and to quantify their impact on the decision making process of endocrinologists. We tested six risk models that have been validated in external cohorts, as opposed to model development, on electronic health records collected from 2008-2015 from a population of 10,730 subjects. Unavailable or missing data in electronic health records was imputed using an existing validated Bayesian Network. Risk scores were assessed on the basis of statistical performance to differentiate between subjects who developed diabetes and those who did not. Eight endocrinologists provided clinical recommendations based on the risk score output. Due to inaccuracies and discrepancies regarding the exact date of Type 2 Diabetes onset, 76 subjects from the initial population were eligible for the study. Risk scores were useful for identifying subjects who developed diabetes (Framingham risk score yielded a c-statistic of 85%), however, our findings suggest that electronic health records are not prepared to massively use this type of risk scores. Use of a Bayesian Network was key for completion of the risk estimation and did not affect the risk score calculation (p > 0.05). Risk score estimation did not have a significant effect on the clinical recommendation except for starting pharmacological treatment (p = 0.004) and dietary counselling (p = 0.039). Despite their potential use, electronic health records should be carefully analyzed before the massive use of Type 2 Diabetes risk scores for the identification of high-risk subjects, and subsequent targeting of preventive actions.

scholarly journals Selecting the optimal risk threshold of diabetes risk scores to identify high-risk individuals for diabetes prevention: a cost-effectiveness analysis

2019 ◽  
Vol 57 (4) ◽  
pp. 447-454 ◽  
Author(s):  
Kristin Mühlenbruch ◽  
Xiaohui Zhuo ◽  
Barbara Bardenheier ◽  
Hui Shao ◽  
Michael Laxy ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cost Effectiveness ◽  
High Risk ◽  
Risk Score ◽  
Cost Effectiveness Analysis ◽  
Diabetes Risk ◽  
Population Characteristics ◽  
Risk Scores ◽  
Effectiveness Analysis

Abstract Aims Although risk scores to predict type 2 diabetes exist, cost-effectiveness of risk thresholds to target prevention interventions are unknown. We applied cost-effectiveness analysis to identify optimal thresholds of predicted risk to target a low-cost community-based intervention in the USA. Methods We used a validated Markov-based type 2 diabetes simulation model to evaluate the lifetime cost-effectiveness of alternative thresholds of diabetes risk. Population characteristics for the model were obtained from NHANES 2001–2004 and incidence rates and performance of two noninvasive diabetes risk scores (German diabetes risk score, GDRS, and ARIC 2009 score) were determined in the ARIC and Cardiovascular Health Study (CHS). Incremental cost-effectiveness ratios (ICERs) were calculated for increasing risk score thresholds. Two scenarios were assumed: 1-stage (risk score only) and 2-stage (risk score plus fasting plasma glucose (FPG) test (threshold 100 mg/dl) in the high-risk group). Results In ARIC and CHS combined, the area under the receiver operating characteristic curve for the GDRS and the ARIC 2009 score were 0.691 (0.677–0.704) and 0.720 (0.707–0.732), respectively. The optimal threshold of predicted diabetes risk (ICER < $50,000/QALY gained in case of intervention in those above the threshold) was 7% for the GDRS and 9% for the ARIC 2009 score. In the 2-stage scenario, ICERs for all cutoffs ≥ 5% were below $50,000/QALY gained. Conclusions Intervening in those with ≥ 7% diabetes risk based on the GDRS or ≥ 9% on the ARIC 2009 score would be cost-effective. A risk score threshold ≥ 5% together with elevated FPG would also allow targeting interventions cost-effectively.

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

Comparison of IDRS and ADA Diabetes Risk Assessment Tools Reveals Different Rsk Score in Same Population: A Cross-Sectional Analysis in a Tertiary Care Hospital

2021 ◽  
Vol 5 (1) ◽  
pp. 10-18
Author(s):  
N. Akter ◽  
N.K. Qureshi
Keyword(s):  
Type 2 Diabetes ◽  
Risk Assessment ◽  
High Risk ◽  
Risk Score ◽  
Diabetes Risk ◽  
Assessment Tools ◽  
Cross Sectional ◽  
Risk Assessment Tools ◽  
Predicted Risk

Background: To identify individuals at high risk of developing type2 diabetes (T2DM), use of a validated risk-assessment tool is currently recommended. Nevertheless, recent studies have shown that risk scores that are developed in the same country can lead to different results of an individual. The Objective of study was to reveal whether two different risk-assessment tools predict similar or dissimilar high-risk score in same population. Method: This cross-sectional analytical study was carried upon 336 non-diabetic adults visiting the outpatient department (OPD) of Medicine, MARKS Medical College & Hospital, Bangladesh from October 2018 to March 2019. Woman having previous history of Gestational Diabetes Mellitus (GDM) were also included. Both the Indian Diabetes risk Score (IDRS) and the American Diabetes (ADA) Risk Score questionnaire were used to collect the data on demographic and clinical characteristics, different risk factors of an individual subject, and to calculate predicted risk score for developing T2DM. Results: Among 336 subjects, 53.6% were female. The mean (±SD) age of the study subjects was 38.25±1.12 years. The average IDRS predicted risk score of developing T2DM was more in female subjects than male [p<0.05]. Whereas the ADA predicted increased risk score of developing type 2 diabetes was more in male subjects than female (p<0.05). IDRS categorized 37.2 % of individuals at high risk for developing diabetes; [p=0.10], while the ADA risk tool categorized 20.2% subjects in high risk group; [p<0.001]. Conclusions: The results indicate that risk for developing type 2 diabetes varies considerably according to the scoring system used. To adequately prevent T2DM, risk scoring systems must be validated for each population considered.

scholarly journals A non-exercise method to determine cardiorespiratory fitness identifies females predicted to be at ‘high risk’ of type 2 diabetes

2016 ◽  
Vol 14 (1) ◽  
pp. 47-54 ◽  
Author(s):  
Benjamin J Gray ◽  
Jeffrey W Stephens ◽  
Daniel Turner ◽  
Michael Thomas ◽  
Sally P Williams ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
High Risk ◽  
Risk Score ◽  
Exercise Testing ◽  
Diabetes Risk ◽  
Testing Method ◽  
Metabolic Equivalents

This study examined the relationship between cardiorespiratory fitness determined by a non-exercise testing method for estimating fitness and predicted risk of developing type 2 diabetes mellitus using five risk assessments/questionnaires (Leicester Diabetes Risk Score, QDiabetes, Cambridge Risk Score, Finnish Diabetes Risk Score and American Diabetes Association Diabetes Risk Test). Retrospective analysis was performed on 330 female individuals with no prior diagnosis of cardiovascular disease or type 2 diabetes mellitus who participated in the Prosiect Sir Gâr workplace initiative in Carmarthenshire, South Wales. Non-exercise testing method for estimating fitness (expressed as metabolic equivalents) was calculated using a validated algorithm, and females were grouped accordingly into fitness quintiles <6.8 metabolic equivalents (Quintile 1), 6.8–7.6 metabolic equivalents (Quintile 2), 7.6–8.6 metabolic equivalents (Quintile 3), 8.6–9.5 metabolic equivalents (Quintile 4), >9.5 metabolic equivalents (Quintile 5). Body mass index, waist circumference, and HbA1c all decreased between increasing non-exercise testing method for estimating fitness quintiles ( p < 0.05), as did risk prediction scores in each of the five assessments/questionnaires ( p < 0.05). The proportion of females in Quintile 1 predicted at ‘high risk’ was between 20.9% and 81.4%, depending on diabetes risk assessment used, compared to none of the females in Quintile 5. A calculated non-exercise testing method for estimating fitness <6.8 metabolic equivalents could help to identify females at ‘high risk’ of developing type 2 diabetes mellitus as predicted using five risk assessments/questionnaires.

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.

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