scholarly journals Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care setting

2020 ◽  
Author(s):  
K Dziopa ◽  
F W Asselbergs ◽  
J Gratton ◽  
N Chaturvedi ◽  
A F Schmidt
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Risk Scores ◽  
Routine Practice ◽  
Cvd Risk ◽  
C Statistic ◽  
Cardiovascular Risk Prediction ◽  
Multiple Variables

AbstractObjectiveTo compare performance of general and diabetes specific cardiovascular risk prediction scores in type 2 diabetes patients (T2DM).DesignCohort study.SettingScores were identified through a systematic review and included irrespective of predicted outcome, or inclusion of T2DM patients. Performance was assessed using data from routine practice.ParticipantsA contemporary representative sample of 203,172 UK T2DM patients (age ≥ 18 years).Main outcome measuresCardiovascular disease (CVD i.e., coronary heart disease and stroke) and CVD+ (including atrial fibrillation and heart failure).ResultsWe identified 22 scores: 11 derived in the general population, 9 in only T2DM patients, and 2 that excluded T2DM patients. Over 10 years follow-up, 63,000 events occurred. The RECODE score, derived in people with T2DM, performed best for both CVD (c-statistic 0.731 (0.728,0.734), and CVD+ (0.732 (0.729,0.735)). Overall, neither derivation population, nor original predicted outcome influenced performance. Calibration slopes (1 indicates perfect calibration) ranged from 0.38 (95%CI 0.37;0.39) to 1.05 (95%CI 1.03;1.07). A simple, population specific recalibration process considerably improved performance, ranging between 0.98 and 1.03. Risk scores performed badly in people with pre-existing CVD (c-statistic ∼0.55). Scores with more predictors did not perform scores better: for CVD+ QRISK3 (19 variables) c-statistic 0.69 (95%CI 0.68;0.69), compared to CHD Basic (8 variables) 0.71 (95%CI 0.70; 0.71).ConclusionsCVD risk prediction scores performed well in T2DM, irrespective of derivation population and of original predicted outcome. Scores performed poorly in patients with established CVD. Complex scores with multiple variables did not outperform simple scores. A simple population specific recalibration markedly improved score performance and is recommended for future use.

scholarly journals Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care setting

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
K D Dziopa ◽  
F W A Asselbergs ◽  
J G Gratton ◽  
N C Chaturvedi ◽  
A F S Schmidt
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
General Population ◽  
Risk Prediction ◽  
Risk Scores ◽  
Cvd Risk ◽  
C Statistic ◽  
Cardiovascular Risk Prediction

Abstract   People with type 2 diabetes (T2DM) remain at high risk for cardiovascular disease (CVD) CVD treatment initiation and intensification are guided by risk prediction algorithms. The majority of CVD risk prediction tools have not been validated in T2DM. We compared the performance of general and diabetes specific cardiovascular risk prediction scores for cardiovascular disease (CVD ie coronary heart disease and stroke), CVD+ (including atrial fibrillation and heart failure), and their individual components, in type 2 diabetes patients (T2DM). Scores were identified through a systematic review and included irrespective of the type of predicted CVD, or inclusion of T2DM patients. Performance was assessed in a contemporary sample of 203,172 UK T2DM. We identified 22 scores: 11 derived in the general population, 9 in T2DM patients, and 2 excluded T2DM patients. Over 10 years follow-up, 63,000 events occurred. The RECODE score, derived in people with T2DM, performed best for both CVD (c-statistic 0.731 (0.728,0.734), and CVD+ (0.732 (0.729,0.735)). Calibration slopes (1 indicates perfect calibration) ranged from 0.38 (95% CI 0.37; 0.39) to 1.05 (95% CI 1.03; 1.07). A simple, population specific recalibration process considerably improved performance, now ranging between 0.98 and 1.03. Risk scores performed badly in people with pre-existing CVD (c-statistic ∼0.55). CVD risk prediction scores performed worse in T2DM than in the general population, irrespective of derivation population, and of original predicted outcome. Scores performed especially poorly in patients with established CVD. A simple population specific recalibration markedly improved score performance and is recommended for future use. FUNDunding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): NPIF programme

scholarly journals Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings

Diabetologia ◽  
2022 ◽  
Author(s):  
Katarzyna Dziopa ◽  
Folkert W. Asselbergs ◽  
Jasmine Gratton ◽  
Nishi Chaturvedi ◽  
Amand F. Schmidt
Keyword(s):  
Type 2 Diabetes ◽  
General Population ◽  
Risk Prediction ◽  
Risk Scores ◽  
P Value ◽  
Cvd Risk ◽  
Discriminative Ability ◽  
C Statistic ◽  
The Uk

Abstract Aims/hypothesis We aimed to compare the performance of risk prediction scores for CVD (i.e., coronary heart disease and stroke), and a broader definition of CVD including atrial fibrillation and heart failure (CVD+), in individuals with type 2 diabetes. Methods Scores were identified through a literature review and were included irrespective of the type of predicted cardiovascular outcome or the inclusion of individuals with type 2 diabetes. Performance was assessed in a contemporary, representative sample of 168,871 UK-based individuals with type 2 diabetes (age ≥18 years without pre-existing CVD+). Missing observations were addressed using multiple imputation. Results We evaluated 22 scores: 13 derived in the general population and nine in individuals with type 2 diabetes. The Systemic Coronary Risk Evaluation (SCORE) CVD rule derived in the general population performed best for both CVD (C statistic 0.67 [95% CI 0.67, 0.67]) and CVD+ (C statistic 0.69 [95% CI 0.69, 0.70]). The C statistic of the remaining scores ranged from 0.62 to 0.67 for CVD, and from 0.64 to 0.69 for CVD+. Calibration slopes (1 indicates perfect calibration) ranged from 0.38 (95% CI 0.37, 0.39) to 0.74 (95% CI 0.72, 0.76) for CVD, and from 0.41 (95% CI 0.40, 0.42) to 0.88 (95% CI 0.86, 0.90) for CVD+. A simple recalibration process considerably improved the performance of the scores, with calibration slopes now ranging between 0.96 and 1.04 for CVD. Scores with more predictors did not outperform scores with fewer predictors: for CVD+, QRISK3 (19 variables) had a C statistic of 0.68 (95% CI 0.68, 0.69), compared with SCORE CVD (six variables) which had a C statistic of 0.69 (95% CI 0.69, 0.70). Scores specific to individuals with diabetes did not discriminate better than scores derived in the general population: the UK Prospective Diabetes Study (UKPDS) scores performed significantly worse than SCORE CVD (p value <0.001). Conclusions/interpretation CVD risk prediction scores could not accurately identify individuals with type 2 diabetes who experienced a CVD event in the 10 years of follow-up. All 22 evaluated models had a comparable and modest discriminative ability. Graphical abstract

Contemporary model for cardiovascular risk prediction in people with type 2 diabetes

2011 ◽  
Vol 18 (3) ◽  
pp. 393-398 ◽  
Author(s):  
Andre Pascal Kengne ◽  
Anushka Patel ◽  
Michel Marre ◽  
Florence Travert ◽  
Michel Lievre ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Risk ◽  
Risk Prediction ◽  
Cardiovascular Risk Prediction

Cardiovascular risk prediction model for Omanis with type 2 diabetes

2018 ◽  
Vol 12 (2) ◽  
pp. 105-110 ◽  
Author(s):  
Abdul Hakeem Alrawahi ◽  
Patricia Lee ◽  
Zaher A.M. Al-Anqoudi ◽  
Muna Alrabaani ◽  
Ahmed Al-Busaidi ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Cardiovascular Risk ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Cardiovascular Risk Prediction

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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