Prediction and classification of cardiovascular disease risk in older adults with diabetes

Risk Prediction Scores for Type 2 Diabetes Microvascular and Cardiovascular Complications Derived and Validated With Real-world Data From 2 Provinces: The DIabeteS COmplications (DISCO) Risk Scores

OBJECTIVES

Existing tools to predict the risk of complications among people with type 2 diabetes poorly discriminate high- from low-risk patients. Our aim in this study was to develop risk prediction scores for major type 2 diabetes complications using real-world clinical care data, and to externally validate these risk scores in a different jurisdiction.

METHODS

Using health-care administrative data and electronic medical records data, risk scores were derived using data from 25,088 people with type 2 diabetes from the Canadian province of Ontario, followed between 2002 and 2017. Scores were developed for major clinically important microvascular events (treatment for retinopathy, foot ulcer, incident end-stage renal disease), cardiovascular disease events (acute myocardial infarction, heart failure, stroke, amputation), and mortality (cardiovascular, noncardiovascular, all-cause). They were then externally validated using the independent data of 11,416 people with type 2 diabetes from the province of Manitoba.

RESULTS

The 10 derived risk scores had moderate to excellent discrimination in the independent validation cohort, ranging from 0.705 to 0.977. Their calibration to predict 5-year risk was excellent across most levels of predicted risk, albeit with some displaying underestimation at the highest levels of predicted risk.

CONCLUSIONS

The DIabeteS COmplications (DISCO) risk scores for major type 2 diabetes complications were derived and externally validated using contemporary real-world clinical data. As a result, they may be more accurate than other risk prediction scores derived using randomized trial data. The use of more accurate risk scores in clinical practice will help improve personalization of clinical care for patients with type 2 diabetes.

The implications of competing risks and direct treatment disutility in cardiovascular disease and osteoporotic fracture: risk prediction and cost effectiveness analysis

Background

Clinical guidelines commonly recommend preventative treatments for people above a risk threshold. Therefore, decision-makers must have faith in risk prediction tools and model-based cost-effectiveness analyses for people at different levels of risk. Two problems that arise are inadequate handling of competing risks of death and failing to account for direct treatment disutility (i.e. the hassle of taking treatments). We explored these issues using two case studies: primary prevention of cardiovascular disease using statins and osteoporotic fracture using bisphosphonates.

Objectives

Externally validate three risk prediction tools [QRISK®3, QRISK®-Lifetime, QFracture-2012 (ClinRisk Ltd, Leeds, UK)]; derive and internally validate new risk prediction tools for cardiovascular disease [competing mortality risk model with Charlson Comorbidity Index (CRISK-CCI)] and fracture (CFracture), accounting for competing-cause death; quantify direct treatment disutility for statins and bisphosphonates; and examine the effect of competing risks and direct treatment disutility on the cost-effectiveness of preventative treatments.

Design, participants, main outcome measures, data sources

Discrimination and calibration of risk prediction models (Clinical Practice Research Datalink participants: aged 25-84 years for cardiovascular disease and aged 30-99 years for fractures); direct treatment disutility was elicited in online stated-preference surveys (people with/people without experience of statins/bisphosphonates); costs and quality-adjusted life-years were determined from decision-analytic modelling (updated models used in National Institute for Health and Care Excellence decision-making).

Results

CRISK-CCI has excellent discrimination, similar to that of QRISK3 (Harrell's c = 0.864 vs. 0.865, respectively, for women; and 0.819 vs. 0.834, respectively, for men). CRISK-CCI has systematically better calibration, although both models overpredict in high-risk subgroups. People recommended for treatment (10-year risk of ≥ 10%) are younger when using QRISK-Lifetime than when using QRISK3, and have fewer observed events in a 10-year follow-up (4.0% vs. 11.9%, respectively, for women; and 4.3% vs. 10.8%, respectively, for men). QFracture-2012 underpredicts fractures, owing to under-ascertainment of events in its derivation. However, there is major overprediction among people aged 85-99 years and/or with multiple long-term conditions. CFracture is better calibrated, although it also overpredicts among older people. In a time trade-off exercise (n = 879), statins exhibited direct treatment disutility of 0.034; for bisphosphonates, it was greater, at 0.067. Inconvenience also influenced preferences in best-worst scaling (n = 631). Updated cost-effectiveness analysis generates more quality-adjusted life-years among people with below-average cardiovascular risk and fewer among people with above-average risk. If people experience disutility when taking statins, the cardiovascular risk threshold at which benefits outweigh harms rises with age (≥ 8% 10-year risk at 40 years of age; ≥ 38% 10-year risk at 80 years of age). Assuming that everyone experiences population-average direct treatment disutility with oral bisphosphonates, treatment is net harmful at all levels of risk.

Limitations

Treating data as missing at random is a strong assumption in risk prediction model derivation. Disentangling the effect of statins from secular trends in cardiovascular disease in the previous two decades is challenging. Validating lifetime risk prediction is impossible without using very historical data. Respondents to our stated-preference survey may not be representative of the population. There is no consensus on which direct treatment disutilities should be used for cost-effectiveness analyses. Not all the inputs to the cost-effectiveness models could be updated.

Conclusions

Ignoring competing mortality in risk prediction overestimates the risk of cardiovascular events and fracture, especially among older people and those with multimorbidity. Adjustment for competing risk does not meaningfully alter cost-effectiveness of these preventative interventions, but direct treatment disutility is measurable and has the potential to alter the balance of benefits and harms. We argue that this is best addressed in individual-level shared decision-making.

Study registration

This study is registered as PROSPERO CRD42021249959.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 15/12/22) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 4. See the NIHR Funding and Awards website for further award information.

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis

BACKGROUND

Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).

METHODS

We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.

RESULTS

Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.

CONCLUSIONS

Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Precision Prognostics for Cardiovascular Disease in Type 2 Diabetes: A Systematic Review and Meta-analysis

Background Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D). Methods We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Results Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Conclusions Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Precision prognostics for the development of complications in diabetes

Individuals with diabetes face higher risks for macro- and microvascular complications than their non-diabetic counterparts. The concept of precision medicine in diabetes aims to optimise treatment decisions for individual patients to reduce the risk of major diabetic complications, including cardiovascular outcomes, retinopathy, nephropathy, neuropathy and overall mortality. In this context, prognostic models can be used to estimate an individual's risk for relevant complications based on individual risk profiles. This review aims to place the concept of prediction modelling into the context of precision prognostics. As opposed to identification of diabetes subsets, the development of prediction models, including the selection of predictors based on their longitudinal association with the outcome of interest and their discriminatory ability, allows estimation of an individual's absolute risk of complications. As a consequence, such models provide information about potential patient subgroups and their treatment needs. This review provides insight into the methodological issues specifically related to the development and validation of prediction models for diabetes complications. We summarise existing prediction models for macro- and microvascular complications, commonly included predictors, and examples of available validation studies. The review also discusses the potential of non-classical risk markers and omics-based predictors. Finally, it gives insight into the requirements and challenges related to the clinical applications and implementation of developed predictions models to optimise medical decision making.

Prediction of Five-Year Cardiovascular Disease Risk in People with Type 2 Diabetes Mellitus: Derivation in Nanjing, China and External Validation in Scotland, UK

Background

To use routinely collected data to develop a five-year cardiovascular disease (CVD) risk prediction model for Chinese adults with type 2 diabetes with validation of its performance in a population of European ancestry.

Methods

People with incident type 2 diabetes and no history of CVD at diagnosis of diabetes between 2008 and 2017 were included in derivation and validation cohorts. The derivation cohort was identified from a pseudonymized research extract of data from the First Affiliated Hospital of Nanjing Medical University (NMU). Five-year risk of CVD was estimated using basic and extended Cox proportional hazards regression models including 6 and 11 predictors respectively. The risk prediction models were internally validated and externally validated in a Scottish population-based cohort with CVD events identified from linked hospital records. Discrimination and calibration were assessed using Harrell's C-statistic and calibration plots, respectively.

Results

Mean age of the derivation and validation cohorts were 58.4 and 59.2 years, respectively, with 53.5% and 56.9% men. During a median follow-up time of 4.75 [2.67, 7.42] years, 18,827 (22.25%) of the 84,630 people in the NMU-Diabetes cohort and 8,763 (7.31%) of the Scottish cohort of 119,891 people developed CVD. The extended model had a C-statistic of 0.723 [0.721-0.724] in internal validation and 0.716 [0.713-0.719] in external validation.

Conclusions

It is possible to generate a risk prediction model with moderate discriminative power in internal and external validation derived from routinely collected Chinese hospital data. The proposed risk score could be used to improve CVD prevention in people with diabetes.

Prediction models of diabetes complications: a scoping review

BACKGROUND

Diabetes often places a large burden on people with diabetes (hereafter 'patients') and the society, that is, in part attributable to its complications. However, evidence from models predicting diabetes complications in patients remains unclear. With the collaboration of patient partners, we aimed to describe existing prediction models of physical and mental health complications of diabetes.

METHODS

Building on existing frameworks, we systematically searched for studies in Ovid-Medline and Embase. We included studies describing prognostic prediction models that used data from patients with pre-diabetes or any type of diabetes, published between 2000 and 2020. Independent reviewers screened articles, extracted data and narratively synthesised findings using established reporting standards.

RESULTS

Overall, 78 studies reported 260 risk prediction models of cardiovascular complications (n=42 studies), mortality (n=16), kidney complications (n=14), eye complications (n=10), hypoglycaemia (n=8), nerve complications (n=3), cancer (n=2), fracture (n=2) and dementia (n=1). Prevalent complications deemed important by patients such as amputation and mental health were poorly or not at all represented. Studies primarily analysed data from older people with type 2 diabetes (n=54), with little focus on pre-diabetes (n=0), type 1 diabetes (n=8), younger (n=1) and racialised people (n=10). Per complication, predictors vary substantially between models. Studies with details of calibration and discrimination mostly exhibited good model performance.

CONCLUSION

This rigorous knowledge synthesis provides evidence of gaps in the landscape of diabetes complication prediction models. Future studies should address unmet needs for analyses of complications n> and among patient groups currently under-represented in the literature and should consistently report relevant statistics. SCOPING REVIEW REGISTRATION: https://osf.io/fjubt/.

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

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.

Cardiovascular risk in patients with type 2 diabetes: A systematic review of prediction models

AIMS

To identify all cardiovascular disease risk prediction models developed in patients with type 2 diabetes or in the general population with diabetes as a covariate updating previous studies, describing model performance and analysing both their risk of bias and their applicability METHODS: A systematic search for predictive models of cardiovascular risk was performed in PubMed. The CHARMS and PROBAST guidelines for data extraction and for the assessment of risk of bias and applicability were followed. Google Scholar citations of the selected articles were reviewed to identify studies that conducted external validations.

RESULTS

The titles of 10,556 references were extracted to ultimately identify 19 studies with models developed in a population with diabetes and 46 studies in the general population. Within models developed in a population with diabetes, only six were classified as having a low risk of bias, 17 had a favourable assessment of applicability, 11 reported complete model information, and also 11 were externally validated.

CONCLUSIONS

There exists an overabundance of cardiovascular risk prediction models applicable to patients with diabetes, but many have a high risk of bias due to methodological shortcomings and independent validations are scarce. We recommend following the existing guidelines to facilitate their applicability.

Plasma Cotinine Is Positively Associated with Homocysteine in Smokers but Not in Users of Smokeless Tobacco

Plasma total homocysteine (tHcy) is a risk marker, and smoking is an established risk factor for cardiovascular disease. It is unclear if the effect of smoked tobacco on homocysteine is mediated by nicotine or other combustion products in smoked tobacco. Snus (moist smokeless tobacco) is high nicotine-containing tobacco, and little is known about the effect of snus on plasma homocysteine. Therefore, we studied, in a cross-section of subjects (n = 1375) from the Northern Sweden Health and Disease Study, with strictly defined current smokers (n = 194) and snus users (n = 47), the impact of tobacco exposure on tHcy, assessed by self-reported tobacco habits and plasma cotinine concentrations. The snus users had higher cotinine concentrations than the smokers. Cotinine, creatinine, methylmalonic acid, and the methylenetetrahydrofolate reductase genotype (MTHFR) T allele were positively associated with tHcy among the smokers, but not among the snus users. No association was observed between tHcy and the number of cigarettes/day. There was a positive association between cotinine and tHcy in the smokers, but not among the snus users. This indicates that substances other than nicotine in tobacco smoke could be responsible for the differential effects on homocysteine status. Self-reported smoking should be complemented by a cotinine assay whenever possible.

Quantifying the Risk Continuum for Cardiovascular Death in Adults with Type 2 Diabetes

OBJECTIVES

In type 2 diabetes (T2D), the most common causes of death are cardiovascular (CV) related, accounting for >50% of deaths in some reports. As novel diabetes therapies reduce CV death risk, identifying patients with T2D at highest CV death risk allows for cost-effective prioritization of these therapies. Accordingly, the primary goal of this study was to quantify the risk continuum for CV death in a real-world T2D population as a means to identify patients with the greatest expected benefit from cardioprotective antidiabetes therapies.

METHODS

This retrospective study included T2D patients receiving services through an integrated health-care system and used data generated through electronic medical records (EMRs). Quantifying the risk continuum entailed developing a prediction model for CV death, creating an integer risk score based on the final prediction model and estimating future CV death risk according to risk score ranking.

RESULTS

Among 59,180 patients with T2D followed for an average of 7.5 years, 15,691 deaths occurred, 6,033 (38%) of which were CV related. The EMR-based prediction model included age, established CV disease and risk factors and glycemic indices (c statistic = 0.819). The 10% highest-risk patients according to prediction model elements had an annual CV death risk of ∼5%; the 25% highest-risk patients had an annual risk of ∼2%.

CONCLUSIONS

This study incorporated a prediction modelling approach to quantify the risk continuum for CV death in T2D. Prospective application allows us to rank individuals with T2D according to their CV death risk, and may guide prioritization of novel diabetes therapies with cardioprotective properties.

Levels of ankle-brachial index and the risk of diabetes mellitus complications

OBJECTIVE

We sought to compare the association of categorized ankle-brachial index (ABI) with mortality and complications of diabetes in persons with no symptoms of peripheral arterial disease (PAD) and in primary cardiovascular disease prevention.

RESEARCH DESIGN AND METHODS

This is a retrospective cohort study of persons with type 2 diabetes aged 35-85 years, from 2006 to 2011. Data were obtained from the Sistema d'Informació per al Desenvolupament de la Investigació en Atenció Primària (SIDIAP^Q). Participants had an ABI measurement that was classified into six categories. For each category of ABI, we assessed the incidence of mortality; macrovascular complications of diabetes: acute myocardial infarction (AMI), ischemic stroke, and a composite of these two; and microvascular complications of this metabolic condition: nephropathy, retinopathy, and neuropathy. We also estimated the HRs for these outcomes by ABI category using Cox proportional hazards models.

RESULTS

Data from 34 689 persons with type 2 diabetes were included. The mean age was 66.2; 51.5% were men; and the median follow-up was 6.0 years. The outcome with the highest incidence was nephropathy, with 24.4 cases per 1000 person-years in the reference category of 1.1≤ABI≤1.3. The incidences in this category for mortality and AMI were 15.4 and 4.1, respectively. In the Cox models, low ABI was associated with increased risk and was significant from ABI lower than 0.9; below this level, the risk kept increasing steeply. High ABI (over 1.3) was also associated with significant increased risk for most outcomes.

CONCLUSIONS

The studied categories of ABI were associated with different risks of type 2 diabetes complications in persons asymptomatic for PAD, who were in primary cardiovascular prevention. These findings could be useful to optimize preventive interventions according to the ABI category in this population.

Predicting the risk of stroke among patients with type 2 diabetes: a systematic review and meta-analysis of C-statistics

OBJECTIVE

Stroke is a major cause of disability and death worldwide. People with diabetes are at a twofold to fivefold increased risk for stroke compared with people without diabetes. This study systematically reviews the literature on available stroke prediction models specifically developed or validated in patients with diabetes and assesses their predictive performance through meta-analysis.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

A detailed search was performed in MEDLINE, PubMed and EMBASE (from inception to 22 April 2019) to identify studies describing stroke prediction models.

ELIGIBILITY CRITERIA

All studies that developed stroke prediction models in populations with diabetes were included.

DATA EXTRACTION AND SYNTHESIS

Two reviewers independently identified eligible articles and extracted data. Random effects meta-analysis was used to obtain a pooled C-statistic.

RESULTS

Our search retrieved 26 202 relevant papers and finally yielded 38 stroke prediction models, of which 34 were specifically developed for patients with diabetes and 4 were developed in general populations but validated in patients with diabetes. Among the models developed in those with diabetes, 9 reported their outcome as stroke, 23 reported their outcome as composite cardiovascular disease (CVD) where stroke was a component of the outcome and 2 did not report stroke initially as their outcome but later were validated for stroke as the outcome in other studies. C-statistics varied from 0.60 to 0.92 with a median C-statistic of 0.71 (for stroke as the outcome) and 0.70 (for stroke as part of a composite CVD outcome). Seventeen models were externally validated in diabetes populations with a pooled C-statistic of 0.68.

CONCLUSIONS

Overall, the performance of these diabetes-specific stroke prediction models was not satisfactory. Research is needed to identify and incorporate new risk factors into the model to improve models' predictive ability and further external validation of the existing models in diverse population to improve generalisability.

Prognostic impact of the ankle-brachial index on the development of micro- and macrovascular complications in individuals with type 2 diabetes: the Rio de Janeiro Type 2 Diabetes Cohort Study

AIMS/HYPOTHESIS

The prognostic importance of the ankle-brachial index (ABI) in individuals with diabetes is controversial. We aimed to evaluate the relationship between the ABI and the occurrence of micro- and macrovascular complications in individuals with type 2 diabetes.

METHODS

The ABI was measured at baseline in 668 individuals with type 2 diabetes, and the individuals were followed-up for a median of 10 years. Multivariate Cox analysis was used to examine associations between the ABI and the occurrence of microvascular (retinopathy, microalbuminuria, renal function deterioration and peripheral neuropathy) and macrovascular (total cardiovascular events, major adverse cardiovascular events [MACE] and cardiovascular mortality) complications, and all-cause mortality. The improvement in risk stratification was assessed using the C statistic and the integrated discrimination improvement (IDI) index.

RESULTS

During follow-up, 168 individuals had a cardiovascular event (140 MACE) and 191 individuals died (92 cardiovascular deaths); 156 individuals newly developed or experienced worsening diabetic retinopathy, 194 achieved the renal composite outcome (122 with newly developed microalbuminuria and 93 with deteriorating renal function) and 95 newly developed or experienced worsening peripheral neuropathy. The ABI, either analysed as a continuous or as a categorical variable, was significantly associated with all macrovascular and mortality outcomes, except for non-cardiovascular mortality. Individuals with a baseline ABI of ≤0.90 had a 2.1-fold increased risk of all-cause mortality (95% CI 1.3, 3.5; p = 0.004), a 2.7-fold excess risk of cardiovascular mortality (95% CI 1.4, 5.4; p = 0.004) and a 2.5-fold increased risk of MACE (95% CI 1.5, 4.4; p = 0.001). The ABI improved risk discrimination over classical risk factors, with relative IDIs ranging from 6.3% (for all-cause mortality) to 31% (for cardiovascular mortality). In addition, an ABI of ≤0.90 was associated with the development or worsening of peripheral neuropathy (2.1-fold increased risk [95% CI 1.1, 4.3]; p = 0.033), but not with retinopathy or renal outcomes.

CONCLUSIONS/INTERPRETATION

A low ABI is associated with excess risk of adverse cardiovascular outcomes, mortality and peripheral neuropathy development or worsening, and improves cardiovascular risk stratification. The ABI should therefore be routinely evaluated in individuals with type 2 diabetes.

Performance of Cardiovascular Disease Risk Scores in People Diagnosed With Type 2 Diabetes: External Validation Using Data From the National Scottish Diabetes Register

OBJECTIVE

To evaluate the performance of five cardiovascular disease (CVD) risk scores developed in diabetes populations and compare their performance to QRISK2.

RESEARCH DESIGN AND METHODS

A cohort of people diagnosed with type 2 diabetes between 2004 and 2016 was identified from the Scottish national diabetes register. CVD events were identified using linked hospital and death records. Five-year risk of CVD was estimated using each of QRISK2, ADVANCE (Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation), Cardiovascular Health Study (CHS), New Zealand Diabetes Cohort Study (NZ DCS), Fremantle Diabetes Study, and Swedish National Diabetes Register (NDR) risk scores. Discrimination and calibration were assessed using the Harrell C statistic and calibration plots, respectively.

RESULTS

The external validation cohort consisted of 181,399 people with type 2 diabetes and no history of CVD. There were 14,081 incident CVD events within 5 years of follow-up. The 5-year observed risk of CVD was 9.7% (95% CI 9.6, 9.9). C statistics varied between 0.66 and 0.67 for all risk scores. QRISK2 overestimated risk, classifying 87% to be at high risk for developing CVD within 5 years; ADVANCE underestimated risk, and the Swedish NDR risk score calibrated well to observed risk.

CONCLUSIONS

None of the risk scores performed well among people with newly diagnosed type 2 diabetes. Using these risk scores to predict 5-year CVD risk in this population may not be appropriate.

Clinical Utility of Carotid Ultrasonography in the Prediction of Cardiovascular Events in Patients with Diabetes: A Combined Analysis of Data Obtained in Five Longitudinal Studies

Aim: It remains unclear whether measures used in carotid ultrasonography such as the intima–media thickness (IMT) and ultrasonic tissue characterization of the carotid using the gray-scale median (GSM) can add prognostic information beyond the conventional cardiovascular risk markers in pati ents with diabetes.

Methods: This study employed a combined analysis of data obtained in five longitudinal studies including a total of 3263 patients with diabetes but without apparent cardiovascular disease (CVD) at baseline. The associations between carotid ultrasonography measures and the first occurrence of CVD (488 cases), which were defined as cardiovascular death, coronary artery diseases, stroke, or peripheral artery disease, were analyzed.

Results: Common carotid artery (CCA)-mean-IMT, CCA-max-IMT, Max-IMT, plaque-GSM, and the presence of low-GSM echolucent plaques at baseline were prognostic factors for CVD even after adjustment for conventional risk factors. Time-dependent receiver-operating-characteristic (ROC) curve analysis indicated that the use of CCA-mean-IMT, CCA-max-IMT, and Max-IMT in addition to the conventional risk factors improved significantly the prediction of occurrence of CVD. Increments in the CCA-mean-IMT (hazard ratio [HR] 2.37 for every 0.1-mm/year increment [95% confidence interval [CI]: 1.63–3.47], p < 0.001), Max-IMT (HR 1.51 for every 0.1-mm/year increment [95% CI: 1.07–2.14], p = 0.020), and Mean-GSM (HR 0.22 for every 10-U/year increment [95% CI: 0.06–0.76], p = 0.016) during the observation period were also prognostic factors for CVD even after adjusting for the baseline value of the respective measure.

Conclusions: Addition of carotid ultrasonography measures to conventional risk factors significantly improved the stratification of patients by cardiovascular risk. Changes over time in carotid ultrasonography measures may be used as therapeutic outcome measures.

Abbreviations: CAC, coronary artery calcium; CCA, common carotid artery; CVD, cardiovascular disease; DM, diabetes mellitus; FRS,Framingham Risk Score; GSM, gray-scale median; HR, hazard ratio; IMT, intima–media thickness; PAD, peripheral artery disease

Diabetes, diabetes severity, and coronary heart disease risk equivalence: REasons for Geographic and Racial Differences in Stroke (REGARDS)

BACKGROUND

Evidence is mixed regarding whether diabetes confers equivalent risk of coronary heart disease (CHD) as prevalent CHD. We investigated whether diabetes and severe diabetes are CHD risk equivalents.

METHODS

At baseline, participants in the REasons for Geographic and Racial Differences in Stroke (REGARDS) study (black and white US adults ≥45 years old recruited in 2003-2007) were categorized as having prevalent CHD only (self-reported or electrocardiogram evidence; n = 3,043), diabetes only (self-reported or elevated glucose; n = 4,012), diabetes and prevalent CHD (n = 1,529), and neither diabetes nor prevalent CHD (n = 17,155). Participants with diabetes using insulin and/or with albuminuria (urinary albumin-to-creatinine ratio ≥30 mg/g) were categorized as having severe diabetes. Participants were followed up through 2011 for CHD events (myocardial infarction or fatal CHD).

RESULTS

During a mean follow-up of 5 years, 1,385 CHD events occurred. The hazard ratios of CHD events comparing participants with diabetes only, diabetes, and prevalent CHD and neither diabetes nor prevalent CHD with those with prevalent CHD were 0.65 (95% CI 0.54-0.77), 1.54 (95% CI 1.30-1.83), and 0.41 (95% CI 0.35-0.47), respectively, after adjustment for demographics and risk factors. Compared with participants with prevalent CHD, the hazard ratio of CHD events for participants with severe diabetes was 0.88 (95% CI 0.72-1.09).

CONCLUSIONS

Participants with diabetes had lower risk of CHD events than did those with prevalent CHD. However, participants with severe diabetes had similar risk to those with prevalent CHD. Diabetes severity may need consideration when deciding whether diabetes is a CHD risk equivalent.

Diabetes in older people: new insights and remaining challenges

Diabetes in ageing communities imposes a substantial personal and public health burden by virtue of its high prevalence, its capacity to cause disabling vascular complications, the emergence of new non-vascular complications, and the effects of frailty. In this Review, we examine the current state of knowledge about diabetes in older people (aged ≥ 75 years) and discuss how recognition of the effect of frailty and disability is beginning to lead to new management approaches. A multidimensional and multidisciplinary assessment process is essential to obtain information on medical, psychosocial, and functional capabilities, and also on how impairments of these functions could limit activities. Major aims of diabetes care include maintenance of independence, functional status, and quality of life by reduction of symptom and medicine burden, and active identification of risks. Linking of therapeutic targets to individual functional status is mandatory and very tight glucose control is often not necessary. Hypoglycaemia remains an important avoidable iatrogenic event. Quality diabetes care in older people remains an important challenge for health professionals.

Nursing home nurses conceptualize how to care for residents with cardiac vulnerability

BACKGROUND

With ageing, older people face cardiovascular problems as the major cause of disability and death. Although immediate medical attention is a major factor in determining outcomes of cardiac problems, lack of personnel (i.e. registered nurse, certified nursing assistant and home care aide) in nursing homes without residing doctor limits the awareness of such problems, thus making it difficult to initiate timely and appropriate intervention.

AIM

The aim of this study was to conceptualize critical care for nursing home residents with cardiac vulnerability and develop practical knowledge in nursing practice.

METHODS

Conventional content analysis was performed on date from interviews with 30 nurses from 10 nursing homes in South Korea between July and November 2010.

RESULTS

The analysis revealed three major cardiac problems resulting from residents' cardiac vulnerability: angina, myocardial infarction (MI) and cardiogenic shock. Through content analysis, we extracted 6 themes and 21 subthemes for nurses' conceptualization of critical care for nursing home residents with cardiac vulnerability. In nursing homes without a residing doctor, nurses assessed the physical, functional and cognitive conditions along with the responses and symptoms of residents when emergency situations related to the cardiac problems occurred. Moreover, with a lack of infrastructures of a hospital, nurses provided critical care to the elderly by using personal practice strategies based on their personal experience in facilities along with practical knowledge of nurses while following the management principles of emergencies.

CONCLUSIONS AND RELEVANCE TO CLINICAL PRACTICE

We found that nurses conceptualized critical nursing care for cardiac problems at nursing homes, which are different from those of general hospitals. The results of this study will provide basis for the development of care guidelines and educational materials that can be used by novice nurses or nursing students.

The validation of cardiovascular risk scores for patients with type 2 diabetes mellitus

OBJECTIVE

Various cardiovascular prediction models have been developed for patients with type 2 diabetes. Their predictive performance in new patients is mostly not investigated. This study aims to quantify the predictive performance of all cardiovascular prediction models developed specifically for diabetes patients.

DESIGN AND METHODS

Follow-up data of 453, 1174 and 584 type 2 diabetes patients without pre-existing cardiovascular disease (CVD) in the EPIC-NL, EPIC-Potsdam and Secondary Manifestations of ARTerial disease cohorts, respectively, were used to validate 10 prediction models to estimate risk of CVD or coronary heart disease (CHD). Discrimination was assessed by the c-statistic for time-to-event data. Calibration was assessed by calibration plots, the Hosmer-Lemeshow goodness-of-fit statistic and expected to observed ratios.

RESULTS

There was a large variation in performance of CVD and CHD scores between different cohorts. Discrimination was moderate for all 10 prediction models, with c-statistics ranging from 0.54 (95% CI 0.46 to 0.63) to 0.76 (95% CI 0.67 to 0.84). Calibration of the original models was poor. After simple recalibration to the disease incidence of the target populations, predicted and observed risks were close. Expected to observed ratios of the recalibrated models ranged from 1.06 (95% CI 0.81 to 1.40) to 1.55 (95% CI 0.95 to 2.54), mainly driven by an overestimation of risk in high-risk patients.

CONCLUSIONS

All 10 evaluated models had a comparable and moderate discriminative ability. The recalibrated, but not the original, prediction models provided accurate risk estimates. These models can assist clinicians in identifying type 2 diabetes patients who are at low or high risk of developing CVD.

Clinical utility of brachial-ankle pulse wave velocity in the prediction of cardiovascular events in diabetic patients

Background

Brachial-ankle pulse wave velocity (baPWV) is a method to estimate arterial stiffness, which reflects the stiffness of both the aorta and peripheral artery; it would be applicable to general practice, since its measurementis automated. The aim of this study was to evaluate whether baPWV can be predictors of future cardiovascular events (CVE) in diabetic patients.

Methods

We prospectively evaluated the association between baPWV or carotid intima-media thickness (carotid IMT) at baseline and new onset of CVE in 1040 type 2 diabetic patients without CVE. The predictability of baPWV and/or carotid IMT for identifying patients at high risk for CVE was evaluated by time-dependent receiver-operating-characteristic (ROC) curve analysis.

Results

During a median follow-up of 7.5 years, 113 had new CVD events. The cumulative incidence rates of CVE were significantly higher in patients with high baPWV values (≥1550 cm/s) as compared to those with low baPWV values (<1550 cm/s) (p < 0.001, log-rank test). Similarly, the cumulative incidence rate of CVE was significantly higher in patients with higher maximum carotid IMT (maxIMT) values (≥1.0 mm) as compared to those with lower maxIMT values (<1.0 mm) (p < 0.001, log-rank test). Subjects with both “high PWV” and “high IMT” had a significantly higher risk of developing CVE as compared to those with either “high PWV” or “high IMT,” as well as those with neither. A multivariate Cox proportional hazards regression model revealed that both baPWV (HR = 1.30, [95%CI: 1.07-1.57]; p = 0.009) and maxIMT (HR = 1.20, [95%CI: 1.01-1.41]; p = 0.033) were independent predictors for CVE, even after adjustment for the conventional risk factors. Time-dependent ROC curve analyses revealed that the addition of maxIMT to the Framingham risk score resulted in significant increase in AUC (from 0.60 [95%CI: 0.54-0.67] to 0.63 [95%CI: 0.60-0.82]; p = 0.01). Notably, the addition of baPWV to the Framingham risk score and maxIMT resulted in further and significant (p = 0.02) increase in AUC (0.72 [95%CI: 0.67-0.78]).

Conclusions

Evaluation of baPWV, in addition to carotid IMT and conventional risk factors, improved the ability to identify the diabetic individuals with high risk for CVE.

Electronic supplementary material

The online version of this article (doi:10.1186/s12933-014-0128-5) contains supplementary material, which is available to authorized users.

Measurement of subclinical carotid atherosclerosis may help in predicting risk for stroke in patients with diabetes

Diabetes is one of the most important risk factor for stroke and cardiovascular disease (CVD), especially in young patients. The control of classical vascular risk factors failed in terms of prevention of stroke in patients with diabetes. In addiction, in these patients the glycemic control showed a benefit on microvascular disease but lacked an established benefit in macrovascular disease. Therefore, implementations of effective stroke prevention strategies appear necessary in patients with diabetes. Ultrasound surrogate or intermediate markers of carotid atherosclerosis include carotid intima-media thickness (cIMT), carotid plaque (CP), and carotid stiffness (STIFF) have been demonstrated to increase in patients with diabetes and to be able to predict risk for stroke. In this editorial we discuss the opportunity to prevent the onset of vascular disease in their "preclinical or subclinical" stage in patients with higher risk for stroke such as diabetic patients.