Comparison of validation and application on various cardiovascular disease mortality risk prediction models in Chinese rural population

The predictive accuracy of coronary heart disease risk prediction models in rural Northwestern China

Cardiovascular risk models developed may have limitations when applied to rural Chinese. This study validated and compared the Framingham Risk Score (FRS) and Prediction for Atherosclerotic Cardiovascular Disease Risk in China (PAR) models in predicting 10-year risk of coronary heart disease (CHD) in a rural cohort in Ningxia, China from 2008 to 2019. The FRS and PAR models were validated by estimating predicted events, C index, calibration χ2 and plots. 1381 adults without CHD at baseline were followed up for 9.75 years on average. 168 CHD cases were observed. The FRS and PAR underestimated CHD events by 22 % and 46 % for the total population, while overestimated for males by 152 % and 78 %, respectively. The C index was slightly higher for PAR than FRS. Both models showed weak calibration with chi-square values above 20 (p < 0.001). Bland-Altman plots indicated FRS predicted higher CHD risk than PAR, lacking consistency. Overall, FRS and PAR demonstrated limited performance in predicting 10-year CHD risk in this rural population. PAR had slightly better discrimination than FRS, but require further improvement in calibration and individual risk estimation to suit the rural population in Northwest China.

Evidence supporting the choice of a new cardiovascular risk equation for Australia

This article reviews the risk equations recommended for use in international cardiovascular disease (CVD) primary prevention guidelines and assesses their suitability for use in Australia against a set of a priori defined selection criteria. The review and assessment were commissioned by the National Heart Foundation of Australia on behalf of the Australian Chronic Disease Prevention Alliance to inform recommendations on CVD risk estimation as part of the 2023 update of the Australian CVD risk assessment and management guidelines. Selected international risk equations were assessed against eight selection criteria: development using contemporary data; inclusion of established cardiovascular risk factors; inclusion of ethnicity and deprivation measures; prediction of a broad selection of fatal and non-fatal CVD outcomes; population representativeness; model performance; external validation in an Australian dataset; and the ability to be recalibrated or modified. Of the ten risk prediction equations reviewed, the New Zealand PREDICT equation met seven of the eight selection criteria, and met additional usability criteria aimed at assessing the ability to apply the risk equation in practice in Australia.

Associations between abdominal obesity and the risk of stroke in Chinese older patients with obstructive sleep apnea: Is there an obesity paradox?

Background and purpose

Abdominal obesity (AO) is a well-known independent risk factor for stroke in the general population although it remains unclear in the case of the elderly, especially in Chinese older patients with obstructive sleep apnea (OSA), considering the obesity paradox. This study aimed to investigate the association between AO and stroke among Chinese older patients with OSA.

Methods

Data were collected from January 2015 to October 2017, and 1,290 older patients (age 60–96 years) with OSA (apnea–hypopnea index ≥ 5 events/h on polysomnography) were consecutively enrolled from sleep centers at six hospitals, evaluated for AO defined as waist circumference (WC) using the standardized criteria for the Chinese population, and followed up prospectively for a median period of 42 months. Logistic regression and Cox regression analyses were used to determine the cross-sectional and longitudinal associations between AO and stroke risk in these participants and different groups of the severity of OSA.

Results

Participants with AO had a higher prevalence of stroke at baseline. A higher incidence of stroke during a median follow-up period of 42 months in participants with AO than in participants without AO (12.4% vs. 6.8% and 8.3% vs. 2.4%, respectively; both P &lt; 0.05) was predicted. Cross-sectional analysis revealed an association between AO and stroke (odds ratio [OR]1.96, 95% confidence interval [CI] 1.31–2.91), which was stronger among participants with moderate OSA only (OR 2.16, 95%CI 1.05–4.43). Cox regression analysis showed that, compared to participants without AO, participants with AO had a higher cumulative incidence of stroke (hazard ratio [HR] 2.16, 95% CI 1.12–4.04) during a median follow-up of 42 months, and this association was observed in patients with severe OSA only (HR 3.67, 95% CI 1.41–9.87) but not for individuals with mild OSA (HR = 1.84, 95% CI 0.43–6.23) and moderate OSA (HR = 1.98, 95% CI 0.73–6.45).

Conclusion

The risk of stroke is associated with AO among Chinese older patients who have OSA, both at baseline and during follow-up, and the strength of the association varied by OSA severity. Active surveillance for early detection of AO could facilitate the implementation of stroke-preventive interventions in the Chinese older OSA population.

Prediction of Five-Year Cardiovascular Disease Risk in People with Type 2 Diabetes Mellitus: Derivation in Nanjing, China and External Validation in Scotland, UK

Background

To use routinely collected data to develop a five-year cardiovascular disease (CVD) risk prediction model for Chinese adults with type 2 diabetes with validation of its performance in a population of European ancestry.

Methods

People with incident type 2 diabetes and no history of CVD at diagnosis of diabetes between 2008 and 2017 were included in derivation and validation cohorts. The derivation cohort was identified from a pseudonymized research extract of data from the First Affiliated Hospital of Nanjing Medical University (NMU). Five-year risk of CVD was estimated using basic and extended Cox proportional hazards regression models including 6 and 11 predictors respectively. The risk prediction models were internally validated and externally validated in a Scottish population-based cohort with CVD events identified from linked hospital records. Discrimination and calibration were assessed using Harrell's C-statistic and calibration plots, respectively.

Results

Mean age of the derivation and validation cohorts were 58.4 and 59.2 years, respectively, with 53.5% and 56.9% men. During a median follow-up time of 4.75 [2.67, 7.42] years, 18,827 (22.25%) of the 84,630 people in the NMU-Diabetes cohort and 8,763 (7.31%) of the Scottish cohort of 119,891 people developed CVD. The extended model had a C-statistic of 0.723 [0.721-0.724] in internal validation and 0.716 [0.713-0.719] in external validation.

Conclusions

It is possible to generate a risk prediction model with moderate discriminative power in internal and external validation derived from routinely collected Chinese hospital data. The proposed risk score could be used to improve CVD prevention in people with diabetes.

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.

Development and validation of prediction models for hypertension risks in rural Chinese populations

Background

Various hypertension predictive models have been developed worldwide; however, there is no existing predictive model for hypertension among Chinese rural populations.

Methods

This is a 6-year population-based prospective cohort in rural areas of China. Data was collected in 2007-2008 (baseline survey) and 2013-2014 (follow-up survey) from 8319 participants ranging in age from 35 to 74 years old. Specified gender hypertension predictive models were established based on multivariate Cox regression, Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), and Classification and Regression Tree (CART) in the training set. External validation was conducted in the testing set. The estimated models were assessed by discrimination and calibration, respectively.

Results

During the follow-up period, 432 men and 604 women developed hypertension in the training set. Assessment for established models in men suggested men office-based model (M1) was better than others. C-index of M1 model in the testing set was 0.771 (95% confidence Interval (CI) = 0.750, 0.791), and calibration χ² = 6.3057 (P = 0.7090). In women, women office-based model (W1) and ANN were better than the other models assessed. The C-indexes for the W1 model and the ANN model in the testing set were 0.765 (95% CI = 0.746, 0.783) and 0.756 (95% CI = 0.737, 0.775) and the calibrations χ² were 6.7832 (P = 0.1478) and 4.7447 (P = 0.3145), respectively.

Conclusions

Not all machine-learning models performed better than the traditional Cox regression models. The W1 and ANN models for women and M1 model for men have better predictive performance which could potentially be recommended for predicting hypertension risk among rural populations.

Predicting long-term ischemic events using routine clinical parameters in patients with coronary artery disease: The OPT-CAD risk score

BACKGROUND

The prognosis of patients with coronary artery disease (CAD) at hospital discharge was constantly varying, and postdischarge risk of ischemic events remain a concern. However, risk prediction tools to identify risk of ischemia for these patients has not yet been reported.

AIMS

We sought to develop a scoring system for predicting long-term ischemic events in CAD patients receiving antiplatelet therapy that would be beneficial in appropriate personalized decision-making for these patients.

METHODS

In this prospective Optimal antiPlatelet Therapy for Chinese patients with Coronary Artery Disease (OPT-CAD, NCT01735305) registry, a total of 14 032 patients with CAD receiving at least one kind of antiplatelet agent were enrolled from 107 centers across China, from January 2012 to March 2014. The risk scoring system was developed in a derivation cohort (enrolled initially 10 000 patients in the database) using a logistic regression model and was subsequently tested in a validation cohort (the last 4032 patients). Points in risk score were assigned based on the multivariable odds ratio of each factor. Ischemic events were defined as the composite of cardiac death, myocardial infarction or stroke.

RESULTS

Ischemic events occurred in 342 (3.4%) patients in the derivation cohort and 160 (4.0%) patients in the validation cohort during 1-year follow-up. The OPT-CAD score, ranging from 0-257 points, consist of 10 independent risk factors, including age (0-71 points), heart rates (0-36 points), hypertension (0-20 points), prior myocardial infarction (16 points), prior stroke (16 points), renal insufficient (21 points), anemia (19 points), low ejection fraction (22 points), positive cardiac troponin (23 points) and ST-segment deviation (13 points). In predicting 1-year ischemic events, the area under receiver operating characteristics curve were 0.73 and 0.72 in derivation and validation cohort, respectively. The incidences of ischemic events in low- (0-90 points), medium- (91-150 points) and high-risk (≥151 points) patients were 1.6%, 5.5%, and 15.0%, respectively. Compared to GRACE score, OPT-CAD score had a better discrimination in predicting ischemic events and all-cause mortality (ischemic events: 0.72 vs 0.65, all-cause mortality: 0.79 vs 0.72, both P < .001).

CONCLUSIONS

Among CAD patients, a risk score based on 10 baseline clinical variables performed better than the GRACE risk score in predicting long-term ischemic events. However, further research is needed to assess the value of the OPT-CAD score in guiding the management of antiplatelet therapy for patients with CAD.

East meets West: the influence of racial, ethnic and cultural risk factors on cardiac surgical risk model performance

Objective

To explore the impact of racial and ethnic diversity on the performance of cardiac surgical risk models, the Chinese SinoSCORE was compared with the Society of Thoracic Surgeons (STS) risk model in a diverse American population.

Methods

The SinoSCORE risk model was applied to 13 969 consecutive coronary artery bypass surgery patients from twelve American institutions. SinoSCORE risk factors were entered into a logistic regression to create a 'derived' SinoSCORE whose performance was compared with that of the STS risk model.

Results

Observed mortality was 1.51% (66% of that predicted by STS model). The SinoSCORE 'low-risk' group had a mortality of 0.15%±0.04%, while the medium-risk and high-risk groups had mortalities of 0.35%±0.06% and 2.13%±0.14%, respectively. The derived SinoSCORE model had a relatively good discrimination (area under of the curve (AUC)=0.785) compared with that of the STS risk score (AUC=0.811; P=0.18 comparing the two). However, specific factors that were significant in the original SinoSCORE but that lacked significance in our derived model included body mass index, preoperative atrial fibrillation and chronic obstructive pulmonary disease.

Conclusion

SinoSCORE demonstrated limited discrimination when applied to an American population. The derived SinoSCORE had a discrimination comparable with that of the STS, suggesting underlying similarities of physiological substrate undergoing surgery. However, differential influence of various risk factors suggests that there may be varying degrees of importance and interactions between risk factors. Clinicians should exercise caution when applying risk models across varying populations due to potential differences that racial, ethnic and geographic factors may play in cardiac disease and surgical outcomes.