Cardiovascular risk estimated by UKPDS risk engine algorithm in diabetes

Kavarić, Nebojša; Klisić, Aleksandra; Ninić, Ana

doi:10.1515/med-2018-0086

Cited by 10 publications

(7 citation statements)

References 40 publications

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“…Finally, the UKPDS Risk Engine was developed using data collected between 1977 and 1991 from individuals with newly diagnosed T2DM aged 25–65, and may not reflect the current landscape of medical practices and burden of disease seen in individuals with long-standing T2DM, especially those with established ASCVD [ 5 ]. For example, the UKPDS score does not include current medications, such as insulin use, which is incorporated in our risk score and reflects current standard-of-care treatment regimens for those with advanced T2DM [ 32 ]. Similarly, the UKPDS Risk Engine was designed to predict long-term MACE risk (up to 20+ years).…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a model to predict cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke in patients with type 2 diabetes mellitus and established atherosclerotic cardiovascular disease

Stevens

Segar²,

Pandey

et al. 2022

Cardiovasc Diabetol

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Background Among individuals with atherosclerotic cardiovascular disease (ASCVD), type 2 diabetes mellitus (T2DM) is common and confers increased risk for morbidity and mortality. Differentiating risk is key to optimize efficiency of treatment selection. Our objective was to develop and validate a model to predict risk of major adverse cardiovascular events (MACE) comprising the first event of cardiovascular death, myocardial infarction (MI), or stroke for individuals with both T2DM and ASCVD. Methods Using data from the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS), we used Cox proportional hazards models to predict MACE among participants with T2DM and ASCVD. All baseline covariates collected in the trial were considered for inclusion, although some were excluded immediately because of large missingness or collinearity. A full model was developed using stepwise selection in each of 25 imputed datasets, and comprised candidate variables selected in 20 of the 25 datasets. A parsimonious model with a maximum of 10 degrees of freedom was created using Cox models with least absolute shrinkage and selection operator (LASSO), where the adjusted R-square was used as criterion for selection. The model was then externally validated among a cohort of participants with similar criteria in the ACCORD (Action to Control Cardiovascular Risk in Diabetes) trial. Discrimination of both models was assessed using Harrell’s C-index and model calibration by the Greenwood-Nam-D’Agostino statistic based on 4-year event rates. Results Overall, 1491 (10.2%) of 14,671 participants in TECOS and 130 (9.3%) in the ACCORD validation cohort (n = 1404) had MACE over 3 years’ median follow-up. The final model included 9 characteristics (prior stroke, age, chronic kidney disease, prior MI, sex, heart failure, insulin use, atrial fibrillation, and microvascular complications). The model had moderate discrimination in both the internal and external validation samples (C-index = 0.65 and 0.61, respectively). The model was well calibrated across the risk spectrum—from a cumulative MACE rate of 6% at 4 years in the lowest risk quintile to 26% in the highest risk quintile. Conclusion Among patients with T2DM and prevalent ASCVD, this 9-factor risk model can quantify the risk of future ASCVD complications and inform decision making for treatments and intensity.

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Section: Discussionmentioning

confidence: 99%

Development and validation of a model to predict cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke in patients with type 2 diabetes mellitus and established atherosclerotic cardiovascular disease

Stevens

Segar²,

Pandey

et al. 2022

Cardiovasc Diabetol

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show abstract

“…Regarding the tools that at the moment are used for the evaluation of cardiovascular risk, there are various algorithms that can be used [ 24 ], though not all have been validated in patients with T2DM. One of the most widely used models for cardiovascular risk assessment in patients with T2DM remains the United Kingdom Prospective Diabetes Study (UKPDS) risk engine [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Associations of Chronic Diabetes Complications and Cardiovascular Risk with the Risk of Obstructive Sleep Apnea in Patients with Type 2 Diabetes

Timofticiuc

Vladu

Ștefan³

et al. 2022

JCM

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Background: Type 2 diabetes mellitus (T2DM) is associated with increased mortality and morbidity, including cardiovascular diseases and obstructive sleep apnea (OSA). The aim of this study was to assess the associations between cardiovascular risk, chronic diabetes complications and the risk of OSA in adult patients with T2DM. Methods: The study included 529 patients with T2DM in whom moderate-to-severe OSA risk was assessed using the STOP-Bang questionnaire, dividing the subjects into two groups: group 1: STOP-Bang score <5, and group 2: STOP-Bang score ≥5, respectively. In all the subjects, cardiovascular risk was assessed using the UKPDS risk engine. Statistical analysis was performed using SPSS 26.0, the results being statistically significant if p value was <0.05. Results: 59% of the subjects scored ≥5 on the STOP-Bang questionnaire. We recorded statistically significant differences between the two groups regarding diabetes duration, HbA1c, HOMA-IR, albuminuria, as well as cardiovascular risk at 10 years for both coronary heart disease (CHD) and stroke (p < 0.05). Furthermore, through logistic regression, adjusting for confounding factors, we demonstrated that the STOP-Bang score ≥ 5 is a risk factor for 10-year fatal and nonfatal CHD risk. Conclusions: It is extremely important to screen and diagnose OSA in patients with T2DM, in order to improve the primary and secondary prevention of cardiovascular events in these patients.

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“…Diabetic dyslipidemia characterized by abnormal serum lipid (elevated triglyceride, low-density lipoprotein cholesterol, and low high-density lipoprotein cholesterol) leads to CVD risk in patients with DM [ 4 ]. To prevent adverse cardiovascular outcomes in T2DM patients, assessment of CVD plays a pivotal role [ 5 ]. The proportion of CKD attributable to diabetes alone ranges from 12 to 55%.…”

Section: Introductionmentioning

confidence: 99%

Implication of Salivary Biochemical Parameters for Diagnosis and Prognosis of Type 2 Diabetes Mellitus

Shrestha

Pokhrel

Poudel

et al. 2022

International Journal of Analytical Chemistry

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Background. Clinical laboratory diagnosis and prognosis for diabetes mellitus is performed using blood as a major specimen; however, saliva may represent as an alternative noninvasive specimen of choice. This study aims to evaluate salivary biochemical parameters in diabetic and healthy individuals to substantiate saliva’s role in the diagnosis and prognosis of type 2 diabetes mellitus (T2DM). Methods. This case-control study included 150 T2DM patients and 150 apparently healthy individuals. Socio-demographic data and anthropometric measurements were recorded using a standard questionnaire. Correlation between salivary and blood levels for each parameter was determined using Pearson correlation. Linear regression was performed to estimate the blood levels of the parameters from their salivary levels. Receiver operating characteristics (ROC) analysis was done to determine the diagnostic ability of salivary glucose and establish a sensitivity, specificity, and cut-off value. Results. Salivary glucose, TC, LDL-C, urea, and creatinine were significantly higher in people with diabetes than in the control population ( p < 0.05 ). A significant positive correlation was found between salivary and blood parameters including glucose, TC, TG, LDL-C, urea, and creatinine except for HDL-C in both case and control groups. The linear relationship for each parameter, except glucose in case population and HDL-C in case, control, and the total population was observed between blood and saliva. ROC analysis gave a cut-off value of 1.9 mg/dl for salivary glucose with 71.4% sensitivity and 72.3% specificity. Conclusion. Salivary estimation significantly reflects the blood parameters in this study, indicating that saliva can be a noninvasive specimen for the diagnosis and prognosis of T2DM.

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Cardiovascular risk estimated by UKPDS risk engine algorithm in diabetes

Cited by 10 publications

References 40 publications

Development and validation of a model to predict cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke in patients with type 2 diabetes mellitus and established atherosclerotic cardiovascular disease

Development and validation of a model to predict cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke in patients with type 2 diabetes mellitus and established atherosclerotic cardiovascular disease

Associations of Chronic Diabetes Complications and Cardiovascular Risk with the Risk of Obstructive Sleep Apnea in Patients with Type 2 Diabetes

Implication of Salivary Biochemical Parameters for Diagnosis and Prognosis of Type 2 Diabetes Mellitus

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