Cardiovascular disease risk score prediction models for women and its applicability to Asians

Risk prediction models for atherosclerotic cardiovascular disease: A systematic assessment with particular reference to Qatar

Background: Atherosclerotic cardiovascular disease (ASCVD) is a common disease in the State of Qatar and results in considerable morbidity, impairment of quality of life and mortality. The American College of Cardiology/American Heart Association Pooled Cohort Equations (PCE) is currently used in Qatar to identify those at high risk of ASCVD. However, it is unclear if this is the optimal ASCVD risk prediction model for use in Qatar's ethnically diverse population.

Aims: This systematic review aimed to identify, assess the methodological quality of and compare the properties of established ASCVD risk prediction models for the Qatari population.

Methods: Two reviewers performed head-to-head comparisons of established ASCVD risk calculators systematically. Studies were independently screened according to predefined eligibility criteria and critically appraised using Prediction Model Risk Of Bias Assessment Tool. Data were descriptively summarized and narratively synthesized with reporting of key statistical properties of the models.

Results: We identified 20,487 studies, of which 41 studies met our eligibility criteria. We identified 16 unique risk prediction models. Overall, 50% (n = 8) of the risk prediction models were judged to be at low risk of bias. Only 13% of the studies (n = 2) were judged at low risk of bias for applicability, namely, PREDICT and QRISK3.Only the PREDICT risk calculator scored low risk in both domains.

Conclusions: There is no existing ASCVD risk calculator particularly well suited for use in Qatar's ethnically diverse population. Of the available models, PREDICT and QRISK3 appear most appropriate because of their inclusion of ethnicity. In the absence of a locally derived ASCVD for Qatar, there is merit in a formal head-to-head comparison between PCE, which is currently in use, and PREDICT and QRISK3.

Predicting Postoperative Complications in Cancer Patients: A Survey Bridging Classical and Machine Learning Contributions to Postsurgical Risk Analysis

Simple Summary

Structured survey on the predictive analysis of postoperative complications in oncology, bridging classic risk scores with machine learning advances, and further establishing principles to guide the design of cohort studies and the predictive modeling of postsurgical risks.

Abstract

Postoperative complications can impose a significant burden, increasing morbidity, mortality, and the in-hospital length of stay. Today, the number of studies available on the prognostication of postsurgical complications in cancer patients is growing and has already created a considerable set of dispersed contributions. This work provides a comprehensive survey on postoperative risk analysis, integrating principles from classic risk scores and machine-learning approaches within a coherent frame. A qualitative comparison is offered, taking into consideration the available cohort data and the targeted postsurgical outcomes of morbidity (such as the occurrence, nature or severity of postsurgical complications and hospitalization needs) and mortality. This work further establishes a taxonomy to assess the adequacy of cohort studies and guide the development and assessment of new learning approaches for the study and prediction of postoperative complications.

Association between antenatal blood pressure and 5-year postpartum retinal arteriolar structural and functional changes

Objective

Studies have shown that hypertensive disorders of pregnancy (HDP) are associated with both postpartum retinal microvascular changes and cardiovascular (CV) risks. However, the underlying mechanism of HDP transitioning to microvascular and macrovascular changes remains unknown, due to the interaction between microvasculature and CV risks. In this study, we examined whether associations between antenatal systolic blood pressure (SBP) and postpartum retinal arteriolar changes are independent of postpartum CV risks.

Methods

We included 276 Singaporean mothers attending both baseline index pregnancy (2009–2010) and 5-year postpartum follow-up visits (2014–2015). We measured SBP at baseline. At follow-up, we assessed retinal microvascular structure and function with retinal photography and dynamic vessel analyser, together with CV risks using a validated 2008 Framingham Risk Score (FRS). We performed a traditional four-step mediation analysis using linear regression by adjusting for a series of baseline characteristics: age, ethnicity, college degree, prepregnancy body mass index and gestational diabetes mellitus diagnosis at baseline.

Results

We found that each 10 mm Hg increase in baseline SBP was associated with reduced arteriolar calibre (−1.3 µm; 95% CI −3.0 to 0.2) and fractal dimension (−0.4 degrees of freedom (df); −1.0 to 0.2), and significantly with increased arteriolar constriction (0.5%; 0.001 to 1.0) at 5-year postpartum. Even though baseline SBP was associated with postpartum FRS, the latter was not associated with any retinal arteriolar measures. Therefore, no further mediation analysis was required.

Conclusion

Our study suggested that elevated SBP during pregnancy was associated with suboptimal retinal arteriolar structure and function independent of postpartum CV risks.

Estimation of cardiovascular risk based on total cholesterol versus total cholesterol/high-density lipoprotein within different ethnic groups: The HELIUS study

Aims

European guidelines recommend estimating cardiovascular disease risk using the Systematic COronary Risk Evaluation (SCORE) algorithm. Two versions of SCORE are available: one based on the total cholesterol/high-density lipoprotein cholesterol ratio, and one based on total cholesterol alone. Cardiovascular risk classification between the two algorithms may differ, particularly among ethnic minority groups with a lipid profile different from the ethnic majority groups among whom the SCORE algorithms were validated. Thus in this study we determined whether discrepancies in cardiovascular risk classification between the two SCORE algorithms are more common in ethnic minority groups relative to the Dutch.

Methods

Using HELIUS study data (Amsterdam, The Netherlands), we obtained data from 7572 participants without self-reported prior cardiovascular disease of Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish and Moroccan ethnic origin. For both SCORE algorithms, cardiovascular risk was estimated and used to categorise participants as low (<1%), medium (1–5%), high (5–10%) or very high (≥10%) risk. Odds of differential cardiovascular risk classification were determined by logistic regression analyses.

Results

The percentage of participants classified differently between the algorithms ranged from 8.7% to 12.4% among ethnic minority men versus 11.4% among Dutch men, and from 1.9% to 5.5% among ethnic minority women versus 6.2% among Dutch women. Relative to the Dutch, only Turkish and Moroccan women showed significantly different (lower) odds of differential cardiovascular risk classification.

Conclusion

We found no indication that discrepancies in cardiovascular risk classification between the two SCORE algorithms are consistently more common in ethnic minority groups than among ethnic majority groups.

A Novel Risk Score to the Prediction of 10-year Risk for Coronary Artery Disease Among the Elderly in Beijing Based on Competing Risk Model

The study aimed to construct a risk prediction model for coronary artery disease (CAD) based on competing risk model among the elderly in Beijing and develop a user-friendly CAD risk score tool. We used competing risk model to evaluate the risk of developing a first CAD event. On the basis of the risk factors that were included in the competing risk model, we constructed the CAD risk prediction model with Cox proportional hazard model. Time-dependent receiver operating characteristic (ROC) curve and time-dependent area under the ROC curve (AUC) were used to evaluate the discrimination ability of the both methods. Calibration plots were applied to assess the calibration ability and adjusted for the competing risk of non-CAD death. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to quantify the improvement contributed by the new risk factors. Internal validation of predictive accuracy was performed using 1000 times of bootstrap re-sampling. Of the 1775 participants without CAD at baseline, 473 incident cases of CAD were documented for a 20-year follow-up. Time-dependent AUCs for men and women at t = 10 years were 0.841 [95% confidence interval (95% CI): 0.806-0.877], 0.804 (95% CI: 0.768-0.839) in Fine and Gray model, 0.784 (95% CI: 0.738-0.830), 0.733 (95% CI: 0.692-0.775) in Cox proportional hazard model. The competing risk model was significantly superior to Cox proportional hazard model on discrimination and calibration. The cut-off values of the risk score that marked the difference between low-risk and high-risk patients were 34 points for men and 30 points for women, which have good sensitivity and specificity. A sex-specific multivariable risk factor algorithm-based competing risk model has been developed on the basis of an elderly Chinese cohort, which could be applied to predict an individual's risk and provide a useful guide to identify the groups at a high risk for CAD among the Chinese adults over 55 years old.