Applicability of the Existing CVD Risk Assessment Tools to Type II Diabetics in Oman: A Review

External validation of a cardiovascular risk model for Omani patients with type 2 diabetes mellitus: a retrospective cohort study

OBJECTIVES

To externally validate a recently developed cardiovascular disease (CVD) risk model for Omanis with type 2 diabetes mellitus (T2DM).

DESIGN

Retrospective cohort study.

SETTING

Nine primary care centres in Muscat Governorate, Oman.

PARTICIPANTS

A total of 809 male and female adult Omani patients with T2DM free of CVD at baseline were selected using a systematic random sampling strategy.

OUTCOME MEASURES

Data regarding CVD risk factors and outcomes were collected from the patients' electronic medical records between 29 August 2020 and 2 May 2021. The ability of the model to discriminate CVD risk was assessed by calculating the area under the curve (AUC) of the receiver-operating characteristic curve. Calibration of the model was evaluated using a Hosmer-Lemeshow χ2 test and the Brier score.

RESULTS

The incidence of CVD events over the 5-year follow-up period was 4.6%, with myocardial infarction being most frequent (48.6%), followed by peripheral arterial disease (27%) and non-fatal stroke (21.6%). A cut-off risk value of 11.8% demonstrated good sensitivity (67.6%) and specificity (66.5%). The area under the curve (AUC) was 0.7 (95% CI 0.60 to 0.78) and the Brier score was 0.01. However, the overall mean predicted risk was greater than the overall observed risk (11.8% vs 4.6%) and the calibration graph showed a relatively significant difference between predicted and observed risk levels in different subgroups.

CONCLUSIONS

Although the model slightly overestimated the CVD risk, it demonstrated good discrimination. Recalibration of the model is required, after which it has the potential to be applied to patients presenting to diabetic care centres elsewhere in Oman.

Development of a Cardiovascular Disease Risk Prediction Model: A Preliminary Retrospective Cohort Study of a Patient Sample in Saudi Arabia

Saudi Arabia has an alarmingly high incidence of cardiovascular disease (CVD) and its associated risk factors. To effectively assess CVD risk, it is essential to develop tailored models for diverse regions and ethnicities using local population variables. No CVD risk prediction model has been locally developed. This study aims to develop the first 10-year CVD risk prediction model for Saudi adults aged 18 to 75 years. The electronic health records of Saudi male and female patients aged 18 to 75 years, who were seen in primary care settings between 2002 and 2019, were reviewed retrospectively via the Integrated Clinical Information System (ICIS) database (from January 2002 to February 2019). The Cox regression model was used to identify the risk factors and develop the CVD risk prediction model. Overall, 451 patients were included in this study, with a mean follow-up of 12.05 years. Thirty-five (7.7%) patients developed a CVD event. The following risk factors were included: fasting blood sugar (FBS) and high-density lipoprotein cholesterol (HDL-c), heart failure, antihyperlipidemic therapy, antithrombotic therapy, and antihypertension therapy. The Bayesian information criterion (BIC) score was 314.4. This is the first prediction model developed in Saudi Arabia and the second in any Arab country after the Omani study. We assume that our CVD predication model will have the potential to be used widely after the validation study.

Agreement between cardiovascular disease risk assessment tools: An application to the United Arab Emirates population

Introduction

Evidence regarding the performance of cardiovascular disease (CVD) risk assessment tools is limited in the United Arab Emirates (UAE). Therefore, we assessed the agreement between various externally validated CVD risk assessment tools in the UAE.

Methods

A secondary analysis of the Abu Dhabi Screening Program for Cardiovascular Risk Markers (AD-SALAMA) data, a large population-based cross-sectional survey conducted in Abu Dhabi, UAE during the period 2009 until 2015, was performed in July 2019. The analysis included 2,621 participants without type 2 Diabetes and without history of cardiovascular diseases. The CVD risk assessment tools included in the analysis were the World Health Organization for Middle East and North Africa Region (WHO-MENA), the systematic coronary risk evaluation for high risk countries (SCORE-H), the pooled cohort risk equations for white (PCRE-W) and African Americans (PCRE-AA), the national cholesterol education program Framingham risk score (FRAM-ATP), and the laboratory Framingham risk score (FRAM-LAB).

Results

The overall concordance coefficient was 0.50. The agreement between SCORE-H and PCRE-W, PCRE-AA, FRAM-LAB, FRAM-ATP and WHO-MENA, were 0.47, 0.39, 0.0.25, 0.42 and 0.18, respectively. PCRE-AA classified the highest proportion of participants into high-risk category of CVD (16.4%), followed by PCRE-W (13.6%), FRAM-LAB (6.9%), SCORE-H (4.5%), FRAM-ATP (2.7%), and WHO-MENA (0.4%).

Conclusions

We found a poor agreement between various externally validated CVD risk assessment tools when applied to a large data collected in the UAE. This poses a challenge to choose any of these tools for clinical decision-making regarding the primary prevention of CVD in the country.

Validation of the cardiovascular risk model developed for Omanis with type 2 diabetes

AIM

The first cardiovascular risk prediction model in the Arab world was recently developed for Omanis with type 2 diabetes mellitus. This study aims to validate the newly developed model.

MATERIALS AND METHODS

A retrospective cohort study design was applied in this study. The model was validated in two samples; the model derivation sample and a separate validation sample, consisting of 1314 and 405 diabetics respectively. All patients were free of cardiovascular disease at the baseline (2009-2010) and were followed up until: the first cardiovascular event occurred; the patient died; or up to December 2015. All data were retrieved from the patients' medical records in a primary care setting.

RESULTS

In both the derivation and validation samples, the model showed good discrimination, with an area under the receiver operating curve of 0.73 (95% CI; 0.69-0.77) and 0.70 (95% CI: 0.59-0.75) respectively. Calibration of the model was satisfactory and the actual difference between the mean predicted and observed risk in different risk groups ranged from 0.7%-3.1% and 0.1%-4.2% in the derivation and validation samples respectively.

CONCLUSION

The recently developed cardiovascular disease risk assessment model for Omanis with type 2 diabetes achieved adequate overall validity. The model showed good discrimination and acceptable calibration; it therefore has the potential to be used in local clinical settings. However, further validation and comparison studies are needed to judge the generalizability and superiority of the model over other tools currently used in Oman.

Cardiovascular risk prediction model for Omanis with type 2 diabetes

AIM

To date, no cardiovascular risk assessment tool has been developed specifically for any Arabian population including Omanis. This study aims to develop a suitable cardiovascular risk prediction model in the form of a statistical equation, for Omanis with type 2 diabetes.

MATERIALS AND METHODS

A sample of 2039 patients with type 2 diabetes selected from primary care settings in the Aldakhiliyah Province of Oman were involved in a retrospective cohort study. All patients were free of cardiovascular disease at baseline (in 2009-2010) and were followed up until: 1) their first cardiovascular event occurred; 2) the patient died, or 3) the end of the data collection in December 2015.

RESULTS

Among the total sample, 192 cardiovascular disease events were recorded within a mean follow-up period of 5.3-year. The 5-year probability of a cardiovascular event was given as 1-0.9991^Exp∑XiBi, where Exp ∑XiBi (hazard ratio)=Exp (0.038 age+0.052 DM duration+0.102 HbA1c+0.201 total cholesterol+0.912 albuminuria [1 if present]+0.166 hypertension [1 if present]+0.005 BMI).

CONCLUSION

The first cardiovascular risk prediction tool in the Arab world was developed in this study. It may be used to estimate the 5-year cardiovascular risk among Omanis with type 2 diabetes in order to plan patient management and preventive measures. However, further validation studies are required to determine the accuracy of the model.

Comparison of Machine Learning Approaches Toward Assessing the Risk of Developing Cardiovascular Disease as a Long-Term Diabetes Complication

The estimation of long-term diabetes complications risk is essential in the process of medical decision making. Guidelines for the management of Type 2 Diabetes Mellitus (T2DM) advocate calculating the Cardiovascular Disease (CVD) risk to initiate appropriate treatment. The objective of this study is to investigate the use of sophisticated machine learning techniques toward the development of personalized models able to predict the risk of fatal or nonfatal CVD incidence in T2DM patients. The important challenge of handling the unbalanced nature of the available dataset is addressed by applying novel ensemble strategies. Hybrid Wavelet Neural Networks (HWNNs) and Self-Organizing Maps (SOMs) constitute the primary models for building ensembles following a subsampling approach. Different methods for combining the decisions of the primary models are applied and comparatively assessed. Data from the 5-year follow up of 560 patients with T2DM are used for development and evaluation purposes. The highest discrimination performance (Area Under the Curve (AUC): 71.48%) is achieved by taking into account both the HWNN- and SOM- based primary models' outputs. The proposed method is superior to the Binomial Linear Regression (BLR) model justifying the need to apply more sophisticated techniques in order to produce reliable CVD risk scores.

Guidelines in lower-middle income countries

Guidelines include recommendations intended to optimize patient care; used appropriately, they make healthcare consistent and efficient. In most lower-middle income countries (LMICs), there is a paucity of well-designed guidelines; as a result, healthcare workers depend on guidelines developed in Higher Income Countries (HICs). However, local guidelines are more likely to be implemented because they are applicable to the specific environment; and consider factors such as availability of resources, specialized skills and local culture. If guidelines developed in HICs are to be implemented in LMICs, developers need to incorporate local experts in their development. Involvement of local stakeholders may improve the rates of implementation by identifying and removing barriers to implementation in LMICs. Another option is to encourage local experts to adapt them for use in LMICs; these guidelines may recommend strategies different from those used in HICs, but will be aimed at achieving the best practicable standard of care. Infrastructural deficits in LMICs could be improved by learning from and building on the successful response to the human immunodeficiency virus/acquired immunodeficiency syndrome pandemic through interactions between HICs and LMICs. Similarly, collaborations between postgraduate medical colleges in both HICs and LMICs may help specialist doctors training in LMICs develop skills required for guideline development and implementation.

Cardiovascular Disease Incidence and Risk Factor Patterns among Omanis with Type 2 Diabetes: A Retrospective Cohort Study

OBJECTIVES

Cardiovascular disease (CVD) represents the leading cause of morbidity and mortality among patients with type 2 diabetes mellitus (T2DM). Its incidence and risk factor patterns vary widely across different diabetic populations. This study aims to assess the incidence and risk factor patterns of CVD events among Omanis with T2DM.

METHODS

A sample of 2 039 patients with T2DM from a primary care setting, who were free of CVD at beseline (2009-2010) were involved in a retrospective cohort study. Socio-demographic data and traditional risk factor assessments at the baseline were retrieved from medical records, after which the first CVD outcomes (coronary heart disease, stroke, and peripheral arterial disease) were traced from the baseline to December 2015, with a median follow-up period of 5.6 years.

RESULTS

The overall cumulative incidence of CVD was 9.4% with an incidence density of 17.6 per 1000 person-years. Prevalence of poor glycemic control, hypertension, obesity, dyslipidemia, albuminuria, and current smoking were 40.0%, 56.3%, 39.0%, 77.3%, 18.7%, and 7.8%, respectively. The univariate survival analysis showed a significant association between CVD and the following factors: age, diabetes duration, body mass index, glycemic control, hypertension, total serum cholesterol, and albuminuria.

CONCLUSIONS

This study revealed high incidence of CVD and high prevalence of its traditional risk factors among Omanis with T2DM. In addition, compared to global studies, important differences in the prevalence of some risk factors and their patterns in the univariate association with the cardiovascular outcome have been observed.