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

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

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
K D Dziopa ◽  
F W A Asselbergs ◽  
J G Gratton ◽  
N C Chaturvedi ◽  
A F S Schmidt

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


2020 ◽  
Author(s):  
K Dziopa ◽  
F W Asselbergs ◽  
J Gratton ◽  
N Chaturvedi ◽  
A F Schmidt

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.


JAMIA Open ◽  
2020 ◽  
Author(s):  
Jackie Szymonifka ◽  
Sarah Conderino ◽  
Christine Cigolle ◽  
Jinkyung Ha ◽  
Mohammed Kabeto ◽  
...  

Abstract Objective Electronic health records (EHRs) have become a common data source for clinical risk prediction, offering large sample sizes and frequently sampled metrics. There may be notable differences between hospital-based EHR and traditional cohort samples: EHR data often are not population-representative random samples, even for particular diseases, as they tend to be sicker with higher healthcare utilization, while cohort studies often sample healthier subjects who typically are more likely to participate. We investigate heterogeneities between EHR- and cohort-based inferences including incidence rates, risk factor identifications/quantifications, and absolute risks. Materials and methods This is a retrospective cohort study of older patients with type 2 diabetes using EHR from New York University Langone Health ambulatory care (NYULH-EHR, years 2009–2017) and from the Health and Retirement Survey (HRS, 1995–2014) to study subsequent cardiovascular disease (CVD) risks. We used the same eligibility criteria, outcome definitions, and demographic covariates/biomarkers in both datasets. We compared subsequent CVD incidence rates, hazard ratios (HRs) of risk factors, and discrimination/calibration performances of CVD risk scores. Results The estimated subsequent total CVD incidence rate was 37.5 and 90.6 per 1000 person-years since T2DM onset in HRS and NYULH-EHR respectively. HR estimates were comparable between the datasets for most demographic covariates/biomarkers. Common CVD risk scores underestimated observed total CVD risks in NYULH-EHR. Discussion and conclusion EHR-estimated HRs of demographic and major clinical risk factors for CVD were mostly consistent with the estimates from a national cohort, despite high incidences and absolute risks of total CVD outcome in the EHR samples.


Genes ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 942 ◽  
Author(s):  
Nardos Abebe Werissa ◽  
Peter Piko ◽  
Szilvia Fiatal ◽  
Zsigmond Kosa ◽  
Janos Sandor ◽  
...  

Background: In a previous survey, an elevated fasting glucose level (FG) and/or known type 2 diabetes mellitus (T2DM) were significantly more frequent in the Roma population than in the Hungarian general population. We assessed whether the distribution of 16 single nucleotide polymorphisms (SNPs) with unequivocal effects on the development of T2DM contributes to this higher prevalence. Methods: Genetic risk scores, unweighted (GRS) and weighted (wGRS), were computed and compared between the study populations. Associations between GRSs and FG levels and T2DM status were investigated in separate and combined study populations. Results: The Hungarian general population carried a greater genetic risk for the development of T2DM (GRSGeneral = 15.38 ± 2.70 vs. GRSRoma = 14.80 ± 2.68, p < 0.001; wGRSGeneral = 1.41 ± 0.32 vs. wGRSRoma = 1.36 ± 0.31, p < 0.001). In the combined population models, GRSs and wGRSs showed significant associations with elevated FG (p < 0.001) and T2DM (p < 0.001) after adjusting for ethnicity, age, sex, body mass index (BMI), high-density Lipoprotein Cholesterol (HDL-C), and triglyceride (TG). In these models, the effect of ethnicity was relatively strong on both outcomes (FG levels: βethnicity = 0.918, p < 0.001; T2DM status: ORethnicity = 2.484, p < 0.001). Conclusions: The higher prevalence of elevated FG and/or T2DM among Roma does not seem to be directly linked to their increased genetic load but rather to their environmental/cultural attributes. Interventions targeting T2DM prevention among Roma should focus on harmful environmental exposures related to their unhealthy lifestyle.


Diabetes Care ◽  
2021 ◽  
pp. dc202049
Author(s):  
Yixuan He ◽  
Chirag M. Lakhani ◽  
Danielle Rasooly ◽  
Arjun K. Manrai ◽  
Ioanna Tzoulaki ◽  
...  

2016 ◽  
Vol 19 (3) ◽  
pp. 322-329 ◽  
Author(s):  
Kristi Läll ◽  
Reedik Mägi ◽  
Andrew Morris ◽  
Andres Metspalu ◽  
Krista Fischer

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Genevieve E Smith ◽  
Jonathan A Drezner ◽  
Camilo Fernandez ◽  
Gregory W Stewart

Introduction: Coronary artery calcium (CAC) is a robust predictor of coronary events in asymptomatic individuals with latent atherosclerotic cardiovascular disease (CVD). While evidence suggests CAC scoring may augment traditional CVD risk scores in clinical decision making, evidence is limited on the compared ability of CVD risk scores to identify the degree of coronary atherosclerosis as quantified by absolute CAC, particularly in former elite athlete populations. We investigated this in a cohort of retired National Football League (NFL) players. Methods: We analyzed data on 752 retired NFL players (aged 55.2 ± 9.0 years, 53.7% African-American] that underwent health screening and CAC scoring with the NFL Player Care Foundation. Three 10-year CVD risk scores were compared: Framingham Coronary Heart Disease (FCHD), Framingham CVD (FCVD), and Atherosclerotic CVD Risk Pooled Cohort Equations (PCE). Receiver operating characteristic curves were fitted in 3 models: FCHD (Model 1), FCVD (Model 2), and PCE (Model 3, used as reference based on 2013 AHA guidelines). Contrast analyses identified the model with highest discriminative ability (c statistic) versus CAC = 0 for each CAC score category: >0 and <100, 100-400, and >400. Results: Compared to PCE , FCVD exhibited the highest discriminative ability for CAC > 0 and < 100 ( c statistic 0.7071 vs 0.6706, p<0.0001), while FCHD had the lowest for both CAC 100-400 ( c statistic 0.7198 vs 0.7664, p=0.0165) and CAC >400 ( c statistic 0.7728 vs 0.8460, p<0.0001). No additional differences were identified (Figure 1). Conclusion: Traditional CVD risk scores differ in performance to predict absolute CAC among retired NFL players, underscoring a need for refinement of coronary event risk prediction models to enhance the ability of such models to identify, specifically, low CAC, as even low CAC burden confers increased risk compared to CAC absence. This may include accounting for elite athlete-specific characteristics.


2021 ◽  
Author(s):  
Sam Hodgson ◽  
Qin Qin Huang ◽  
Neneh Sallah ◽  
Chris J Griffiths ◽  
William Newman ◽  
...  

Background: Type 2 diabetes is a heterogeneous condition highly prevalent in British Pakistanis and Bangladeshis (BPB). The Genes & Health (G&H) cohort offers means to explore genetic determinants of disease in BPBs, combining genetic and lifelong health record data. Methods: We assessed whether common genetic loci associated with type 2 diabetes in European-ancestry individuals (EUR) replicate in G&H. We constructed a type 2 diabetes polygenic risk score (PRS) and combined it with a clinical risk instrument (QDiabetes) to build a novel, integrated risk tool (IRT). We compared IRT performance using net reclassification index (NRI) versus QDiabetes alone. We assessed the ability of the PRS to predict type 2 diabetes following gestational diabetes (GDM). We compared PRS distribution between type 2 diabetes subgroups identified by clinical features at diagnosis. Findings: Accounting for power, we replicated fewer loci associated with type 2 diabetes in G&H (n = 76/338, 22%) than would be expected if all EUR-ascertained loci were transferable (n = 95, 28%) (binomial p value = 0.01). In 13,648 patients free from type 2 diabetes followed up for 10 years, NRI was 3.2% for IRT versus QDiabetes (95% confidence interval 2.0 - 4.4%). IRT performance was best in reclassification of young adults deemed low risk by QDiabetes as high risk. PRS was independently associated with progression to type 2 diabetes after GDM (p = 0.028). Mean type 2 diabetes PRS differed between phenotypically-defined type 2 diabetes subgroups (p = 0.002). Interpretation: The type 2 diabetes PRS has broad potential clinical application in BPB, improving identification of type 2 diabetes risk (especially in the young), and characterisation of type 2 diabetes subgroups at diagnosis. Funding: Wellcome Trust, MRC, NIHR, and others. Full funding disclosed within.


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