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

Leeuw, Joep van der; Dieren, Susan van; Beulens, Joline W.J.; Boeing, Heiner; Spijkerman, Annemieke M.W.; Graaf, Yolanda van der; A, D L van der; Nöthlings, Ute; Visseren, Frank L.J.; Rutten, Guy E.H.M.; Moons, Karel G.M.; Schouw, Yvonne T. van der; Peelen, Linda M.

doi:10.1136/heartjnl-2014-306068

Cited by 62 publications

(46 citation statements)

References 33 publications

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“…The c ‐statistic in the current study was larger than typically observed with CV prediction models for T2DM at 0.782, possibly explained by a diabetic cohort both with and without known cardiac conditions at baseline, and consideration of an endpoint more amenable to prediction (HF, rather than MI or ischemic stroke) . Not surprisingly, multiple CV‐related phenotypes (CAD, atrial fibrillation), traditional CV risk factors (age, systolic blood pressure, smoking), and markers of kidney dysfunction (blood urea nitrogen, albumin) were independent predictors of HHF.…”

Section: Discussioncontrasting

confidence: 55%

“…Though T2DM is strongly associated with a broad range of CV outcomes, and several prediction models exist for predicting CV risk in T2DM, multiple arguments can be put forth for creating a prediction model specifically for HHF. 20,21 First, an HHF is typically associated with an exacerbation of an underlying cardiac disorder, distressing symptoms, and portends an increased risk of short-term mortality; thus, HHF is a serious health event warranting preventive focus. 14,22 Furthermore, T2DM is frequent among patients with an HHF, with up to 44% of hospital admissions related to HF reporting comorbid diabetes.…”

Section: Discussionmentioning

confidence: 99%

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A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus

Williams

Geba

Cordova

et al. 2019

Clinical Cardiology

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Background Antidiabetic therapies have shown disparate effects on hospitalization for heart failure (HHF) in clinical trials. This study developed a prediction model for HHF in type 2 diabetes mellitus (T2DM) using real world data to identify patients at high risk for HHF. Hypothesis Type 2 diabetics at high risk for HHF can be identified using information generated during usual clinical care. Methods This electronic medical record‐ (EMR‐) based retrospective cohort study included patients with T2DM free of HF receiving healthcare through a single, large integrated healthcare system. The primary endpoint was HHF, defined as a hospital admission with HF as the primary diagnosis. Cox regression identified the strongest predictors of HHF from 80 candidate predictors derived from EMRs. High risk patients were defined according to the 90th percentile of estimated risk. Results Among 54,452 T2DM patients followed on average 6.6 years, estimated HHF rates at 1, 3, and 5 years were 0.3%, 1.1%, and 2.0%. The final 9‐variable model included: age, coronary artery disease, blood urea nitrogen, atrial fibrillation, hemoglobin A1c, blood albumin, systolic blood pressure, chronic kidney disease, and smoking history (c = 0.782). High risk patients identified by the model had a >5% probability of HHF within 5 years. Conclusions The proposed model for HHF among T2DM demonstrated strong predictive capacity and may help guide therapeutic decisions.

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Section: Discussioncontrasting

confidence: 55%

Section: Discussionmentioning

confidence: 99%

A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus

Williams

Geba

Cordova

et al. 2019

Clinical Cardiology

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“…Metabolic activity is essential for life, and metabolic disorders have been found to be associated with many diseases including CHD [8]. Many serum biochemical indices, which reflect dynamic physiological and pathophysiological processes within the body, have been found to be associated with CHD.…”

Section: Introductionmentioning

confidence: 99%

The associations between serum biomarkers and stenosis of the coronary arteries

et al. 2016

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Serum biochemical indices reflect dynamic physiological and pathophysiological processes within the body, the associations between these markers and the number of stenotic coronary arteries have been rarely studied. 627 healthy controls and 1,049 coronary heart disease (CHD) patients were sequentially recruited in our hospital. The association patterns between serum biochemical markers and the numbers of stenotic coronary arteries were evaluated in a cross-sectional manner. Upon binary multiple logistic regression analysis, the risk factor patterns differed by gender. Age, high-density lipoprotein cholesterol (HDL) and homocysteine (HCY) were common risk factors for CHD in both males and females. Upon ordinal multiple logistic regression analysis, age, low-density lipoprotein cholesterol (LDL) and lipoprotein (Lp) (a) increased, and HDL decreased, as the number of stenotic coronary arteries increased in male patients. Age and Lp(a) were positively associated with the number of stenotic coronary arteries and total bilirubin (TBil) was negatively associated with the number of stenotic coronary arteries in female patients. Age and Lp(a) were common risk factors positively associated with the number of stenotic coronary arteries in both male and female patients. HDL and LDL were male-specific risk factors and TBil was a female-specific risk factor for an increasing number of stenotic coronary arteries. In conclusion, serum biomarker levels correlated with the number of stenotic coronary arteries and showed gender different patterns.

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“…Also it should be noted that existing prediction models for patients with type 2 diabetes mellitus are known to perform generally moderate, even after recalibration. 46 Moreover, it was recently shown that ARRs estimated with models that are internally developed in trial data result in limited bias compared with predictions based on existing external risk scores, given that the number of events per predictor does not exceed 10. 6 Also the fact that the newly developed model performs well in 2 external validation sets in which the patients differ considerably in baseline characteristics (Table 1) as well as in an observational cohort of patients with type 2 diabetes mellitus ( Figure I in the Data Supplement) is reassuring for the generalizability of this model to a broad range of patients with type 2 diabetes mellitus.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Model to Predict Absolute Vascular Risk Reduction by Moderate-Intensity Statin Therapy in Individual Patients With Type 2 Diabetes Mellitus

Kaasenbrood

Poulter

Sever

et al. 2016

Circ: Cardiovascular Quality and Outcomes

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Statin therapy is effective in preventing major cardiovascular events in patients with type 2 diabetes mellitus with an average relative risk reduction that is similar to the effect of statins in patients without type 2 diabetes mellitus.1 Based on this, guidelines recommend statin therapy for most patients with type 2 diabetes mellitus. 2,3 In clinical practice, decisionBackground-In this study, we aimed to translate the average relative effect of statin therapy from trial data to the individual patient with type 2 diabetes mellitus by developing and validating a model to predict individualized absolute risk reductions (ARR) of cardiovascular events. Methods and Results-Data of 2725 patients with type 2 diabetes mellitus from the Lipid Lowering Arm of the AngloScandinavian Cardiac Outcomes Trial (ASCOT-LLA) study (atorvastatin 10 mg versus placebo) were used for model derivation. The model was based on 8 clinical predictors including treatment allocation (statin/placebo). Ten-year individualized ARR on major cardiovascular events by statin therapy were calculated for each patient by subtracting the estimated on-treatment risk from the estimated off-treatment risk. Predicted 10-year ARR by statin therapy was <2% for 13% of the patients. About 30% had an ARR of >4% (median ARR, 3.2%; interquartile range, 2.5%-4.3%; 95% confidence interval for 3.2% ARR, -1.4% to 6.8%). Addition of treatment interactions did not improve model performance. Therefore, the wide distribution in ARR was a consequence of the underlying distribution in cardiovascular risk enrolled in these trials. External validation of the model was performed in data from the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT-LLT; pravastatin 40 mg versus usual care) and Collaborative Atorvastatin Diabetes Study (CARDS; atorvastatin 10 mg versus placebo) of 3878 and 2838 patients with type 2 diabetes mellitus, respectively. Model calibration was adequate in both external data sets, discrimination was moderate 0.64 [95% confidence interval,

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

Cited by 62 publications

References 33 publications

A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus

A risk prediction model for heart failure hospitalization in type 2 diabetes mellitus

The associations between serum biomarkers and stenosis of the coronary arteries

Development and Validation of a Model to Predict Absolute Vascular Risk Reduction by Moderate-Intensity Statin Therapy in Individual Patients With Type 2 Diabetes Mellitus

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