Cardiovascular risk prediction model for Omanis with type 2 diabetes

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.

Development and validation of a novel 10-year cardiovascular risk prediction nomogram for the United Arab Emirates national population

OBJECTIVES

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality among United Arab Emirates (UAE) nationals. Recent studies have shown that current tools are poor in predicting the risk of incident ASCVD in Emiratis. To improve ASCVD risk prediction in this high-risk population, this study sought to develop and validate a novel and practical 10-year ASCVD risk nomogram using risk factors known to be significant in UAE nationals.

DESIGN

A 10-year retrospective cohort study.

SETTING

Outpatient clinics at a large public tertiary care hospital in Al-Ain, UAE.

PARTICIPANTS

Emiratis aged ≥18 years without prior cardiovascular disease (CVD) who had presented to Tawam Hospital's clinics between 1 April 2008 and 31 December 2008, were included. Patients' data were collected retrospectively until 31 January 2020.

EXPOSURE

Cox proportional hazards models were developed to estimate the 10-year ASCVD risk.

PRIMARY OUTCOME MEASURE

Model discrimination and calibration were assessed using the Harrell C-statistic and the Greenwood-Nam-D'Agostino (GND) χ² test, respectively. Receiver operating characteristic curve analysis was used to determine the optimal cut-off point of the nomogram for elevated ASCVD risk.

RESULTS

The study included 1245 patients, of whom 117 developed ASCVD within 10 years. The ASCVD risk nomogram comprised age, sex, family history of CVD, hypertension treatment, systolic blood pressure, total cholesterol, glycosylated haemoglobin A1c and estimated glomerular filtration rate. The Harrell C-statistic was 0.826 and the GND χ² was 2.83 (p=0.830), which indicated good discrimination and calibration of the nomogram model, respectively. The optimal cut-off point was determined to be 10% (sensitivity=79%; specificity=77%). The nomogram can be freely accessed as an online calculator at (https://ascvdriskuae.shinyapps.io/ASCVDrisk/).

CONCLUSIONS

The developed nomogram provides an accurate prognostic tool for 10-year ASCVD risk prediction in UAE nationals. These findings may help guide future research on CVD prevention in this high-risk 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.