Lifetime Risk of Incident Coronary Heart Disease, Stroke, and Cardiovascular Disease: The Japan Public Health Center-Based Prospective Study

AIM

The constellation of cardiovascular disease (CVD) risk factors greatly impacts the lifetime risk (LTR) of incident CVD, but the LTR has not been thoroughly evaluated in the Japanese population.

METHODS

We conducted a prospective study involving a total of 25,896 individuals 40-69 years old without a history of CVD in 1995 (Cohort I) and 1993-1994 (Cohort II) in Japan. CVD risk factors (blood pressure, non-high-density lipoprotein [HDL] cholesterol levels, smoking status, and glucose concentrations) were used to stratify them by risk. The sex-specific LTR of incident coronary heart disease, stroke, atherosclerotic CVD, and total CVD were estimated for participants 45 years old in the 4 risk categories with the cumulative incidence rate, adjusting for the competing risk of death.

RESULTS

We found apparent differences in the LTR of total CVD according to the risk stratification. Individuals with ≥ 2 of the risk factors of blood pressure ≥ 140/90 mmHg or treated, non-HDL cholesterol level ≥ 170 mg/dL or treated, current smoker, and diabetes had substantially higher adjusted LTRs of CVD than those in other groups, with a LTR of 26.5% (95% confidence interval, 24.0%-29.0%) for men and 15.3% (13.1%-17.5%) for women at 45 years. The LTR of incident stroke was the highest among CVDs, and the presence of hypertension and diabetes mellitus strongly influenced the LTR of total CVD.

CONCLUSION

The impact of risk accumulation on LTR of CVD was greater in men, and 1 in 4 men with ≥ 2 major risk factors at 45 years of age developed CVD in their lifetime.

Gender differences in risk factors for ischemic stroke: a longitudinal cohort study in East China

OBJECTIVES

Epidemiological studies of stroke and its risk factors can help develop strategies to prevent stroke. We aimed to explore the current gender-specific prevalence of stroke and associated risk factors.

METHODS

Data were collected using a structured precoded questionnaire designed by the Stroke Screening and Prevention Programme of the National Health and Wellness Commission Stroke Prevention and Control Project Committee, between June 2020 and November 2021. A total of 7394 residents took part in the study, 187 of whom had a stroke. The baseline information of each participant was obtained and included in this study. The chi-square test and Kruskal-Wallis tests were used to examine the relationship between these indicators and stroke, and then multivariate logistic regression was used to construct the prediction scale between different genders.

RESULTS

of 7394 participants,4571 (61.82%) were female. The overall prevalence of stroke patients in the study population was 2.53%, Multivariate analysis found that residence status (OR = 0.43, p = 0.002) 、HCY (OR = 0.962, p = 0.000)、Previous TIA (OR = 0.200, p = 0.002) 、Hypertension (OR = 0.33, p = 0.000) and Dyslipidemia (OR = 0.668, p = 0.028) were significant predictors of stroke. there are gender differences in the traditional risk factors for stroke, and women have more risk factors. ROC analysis confirmed the accuracy of the stroke risk model, and the AUC of the stroke risk model for the general population was 0.79 with p < 0.05. In the gender model, the female AUC was 0.796 (p < 0.05). and the male AUC was 0.786 with p < 0.05.

CONCLUSION

The prevalence of stroke in adults aged 40 years and above is high in eastern China were high. management of risk factors can effectively prevent the occurrence of most strokes. more attention should be paid to gender differences associated with stroke.

Prediction of the 4-Year Incidence Risk of Ischemic Stroke in Healthy Japanese Adults: The Fukushima Health Database

AIM

Estimating the risk of developing ischemic stroke (IS) may assist health professionals in motivating individuals to modify their risk behavior.

METHODS

A predictive model was derived from 178,186 participants from Fukushima Health Database, aged 40-74 years, who attended the health checkup in 2014 and completed at least one annual health checkup by 2018 (Cohort I). Cox proportional hazard regression model was used to build a 4-year prediction model, thus the risk scores were based on the regression coefficients. External validation for the risk scores was conducted in another cohort of 46,099 participants following between 2015 and 2019 (Cohort II).

RESULTS

The 4-year cumulated incidence rate of IS was 179.80/100,000 person-years in Cohort I. The predictive model included age, sex, blood pressure, hypertension treatment, diabetes, low- and high-density lipoprotein cholesterol, smoking, walking pace, and body weight change of 3 kg within one year. Risk scores were interpreted based on the Cohort I predictive model function. The Harrell's C-statistics of the discrimination ability of the risk score model (95% confidence interval) was 0.744 (0.729-0.759) in Cohort I and 0.770 (0.743-0.797) in Cohort II. The overall agreement of the risk score probability of IS incidence for the observed/expected case ratio and 95% CI was 0.98 (0.92-1.05) in Cohort I and 1.08 (0.95-1.22) in Cohort II.

CONCLUSIONS

The 4-year risk prediction model revealed a good performance for IS incidence, and risk scores could be used to estimate individual incidence risk of IS. Updated models with additional confirmed risk variables may be needed.

Real-time continuous glucose monitoring vs. self-monitoring of blood glucose: cost-utility in South Korean type 2 diabetes patients on intensive insulin

AIMS

This study investigated the cost-utility of real-time continuous glucose monitoring (rt-CGM) versus self-monitoring of blood glucose (SMBG) in people with type 2 diabetes (T2D) receiving intensive insulin therapy in South Korea.

METHODS

The IQVIA Core Diabetes Model (CDM v9.5) was used, with clinical effectiveness data obtained from a large-scale real world study. Costs were obtained from South Korean sources and inflated to 2022 South Korean Won (KRW). A South Korean payer perspective was adopted over a lifetime horizon, with future costs and effects discounted at 4.5% per annum. Baseline characteristics included a mean baseline HbA1c level of 8.6% (71 mmol/mol), and a mean age of 64.4 years. A willingness-to-pay (WTP) threshold of KRW 46.0 million was used.

RESULTS

Rt-CGM led to an increase of 0.683 quality-adjusted life years (QALYs) versus SMBG (7.526 QALYs for rt-CGM versus 6.843 QALYs for SMBG). An increase in costs of KRW 16.4 million (from KRW 90.4 million to KRW 106.8 million) was associated with rt-CGM. The incremental cost-utility ratio was KRW 24.0 million per QALY gained, significantly lower than the KRW 46 million threshold.

CONCLUSIONS

For individuals with T2D managed by intensive insulin therapy in South Korea, rt-CGM is cost-effective relative to SMBG.

Development and validation of ischemic heart disease and stroke prognostic models using large-scale real-world data from Japan

BACKGROUND

Previous cardiovascular risk prediction models in Japan have utilized prospective cohort studies with concise data. As the health information including health check-up records and administrative claims becomes digitalized and publicly available, application of large datasets based on such real-world data can achieve prediction accuracy and support social implementation of cardiovascular disease risk prediction models in preventive and clinical practice. In this study, classical regression and machine learning methods were explored to develop ischemic heart disease (IHD) and stroke prognostic models using real-world data.

METHODS

IQVIA Japan Claims Database was searched to include 691,160 individuals (predominantly corporate employees and their families working in secondary and tertiary industries) with at least one annual health check-up record during the identification period (April 2013-December 2018). The primary outcome of the study was the first recorded IHD or stroke event. Predictors were annual health check-up records at the index year-month, comprising demographic characteristics, laboratory tests, and questionnaire features. Four prediction models (Cox, Elnet-Cox, XGBoost, and Ensemble) were assessed in the present study to develop a cardiovascular disease risk prediction model for Japan.

RESULTS

The analysis cohort consisted of 572,971 invididuals. All prediction models showed similarly good performance. The Harrell's C-index was close to 0.9 for all IHD models, and above 0.7 for stroke models. In IHD models, age, sex, high-density lipoprotein, low-density lipoprotein, cholesterol, and systolic blood pressure had higher importance, while in stroke models systolic blood pressure and age had higher importance.

CONCLUSION

Our study analyzed classical regression and machine learning algorithms to develop cardiovascular disease risk prediction models for IHD and stroke in Japan that can be applied to practical use in a large population with predictive accuracy.

Development of new scores for atherosclerotic cardiovascular disease using specific medical examination items: the Suita Study

BACKGROUND

We previously developed risk models predicting stroke, coronary heart disease (CHD), and cardiovascular disease (CVD) among Japanese people from the Suita Study. Yet, applying these models at the national level was challenging because some of the included risk factors differed from those collected in the Japanese governmental health check-ups, such as Tokutei-Kenshin. We, therefore, conducted this study to develop new risk models for stroke, CHD, and atherosclerotic CVD (ASCVD), based on data from the Suita Study. The new models used traditional cardiovascular risk factors similar to those in the Japanese governmental health check-ups.

METHODS

We included 7,413 participants, aged 30-84 years, initially free from stroke and CHD. All participants received baseline health examinations, including a questionnaire assessing their lifestyle and medical history, medical examination, and blood and urine analysis. The risk factors of stroke, CHD, and ASCVD (cerebral infarction or CHD) were determined using the multivariable-adjusted Cox regression. The models' performance was assessed using the C-statistics for discrimination and the Hosmer-Lemeshow for calibration. We also developed three simple scores (zero to 100) that could predict the 10-year incidence of stroke, CHD, and ASCVD.

RESULTS

Within 110,428 person-years (median follow-up = 16.6 years), 410 stroke events, 288 CHD events, and 527 ASCVD events were diagnosed. Age, smoking, hypertension, and diabetes were associated with stroke, CHD, and ASCVD risk. Men and those with decreased high-density lipoproteins or increased low-density lipoproteins showed a higher risk of CHD and ASCVD. Urinary proteins were associated with an increased risk of stroke and ASCVD. The C-statistic values of the risk models were >0.750 and the p-values of goodness-of-fit were >0.30. The 10-year incidence of stroke, CVD, and ASCVD events was 3.8%, 3.5%, and 5.7% for scores 45-54, 10.3%, 11.8%, and 19.6% for scores 65-74, and 27.7%, 23.5%, and 60.5% for scores ≥85, respectively.

CONCLUSIONS

We developed new Suita risk models for stroke, CHD, and ASCVD using variables similar to those in the Japanese governmental health check-ups. We also developed new risk scores to predict incident stroke, CHD, and ASCVD within 10 years.

Development of stroke predictive model in community-dwelling population: A longitudinal cohort study in Southeast China

Background

Stroke has been the leading cause of death and disability in the world. Early recognition and treatment of stroke could effectively limit brain damage and vastly improve outcomes. This study aims to develop a highly accurate prediction model of stroke with a list of lifestyle behaviors and clinical characteristics to distinguish high-risk groups in the community-dwelling population.

Methods

Participants in this longitudinal cohort study came from the community-dwelling population in Suzhou between November 2018 and June 2019. A total of 4,503 residents participated in the study, while stroke happened to 22 participants in the 2-year follow-up period. Baseline information of each participant was acquired and enrolled in this study. T-test, Chi-square test, and Fisher's exact test were used to examine the relationship of these indexes with stroke, and a prediction scale was constructed by multivariate logistic regression afterward. Receiver operating characteristic analysis was applied to testify to the prediction accuracy.

Results

A highly accurate prediction model of stroke was constructed by age, gender, exercise, meat and vegetarian diet, BMI, waist circumference, systolic blood pressure, Chinese visceral adiposity index, and waist-height ratio. Two additional prediction models for overweight and non-overweight individuals were formulated based on crucial risk factors, respectively. The stroke risk prediction models for community-dwelling and overweight populations had accuracies of 0.79 and 0.82, severally. Gender and exercise were significant predictors (χ² > 4.57, p < 0.05) in the community-dwelling population model, while homocysteine (χ² = 4.95, p < 0.05) was significant in the overweight population model.

Conclusion

The predictive models could predict 2-year stroke with high accuracy. The models provided an effective tool for identifying high-risk groups and supplied guidance for improving prevention and treatment strategies in community-dwelling population.

Cardiovascular Risk Prediction Using Machine Learning in a Large Japanese Cohort

Background: Cardiovascular disease (CVD) screening entails precise event prediction to orient risk stratification, resource allocation, and insurance policy. We used random survival forests (RSF) to identify markers of incident CVD among Japanese adults enrolled in an employer-mandated screening program. Methods and Results: We examined biomarker, health history, medication use, and lifestyle data from 155,108 adults aged ≥40 years. The occurrence of coronary artery disease (CAD) or atherosclerotic CVD (ASCVD) events was examined over 6 years of follow-up. The analysis used RSF to identify predictors, then investigated simplified RSF models with fewer predictors for individual-level risk prediction. Data were split into training (70%) and test (30%) datasets. At baseline, the median patient age was 47 years (interquartile range 41-56 years), with 65% males. In all, 1,642 CAD and 2,164 ASCVD events were observed. RSF identified history of heart disease, age, self-reported blood pressure medication, HbA1c, fasting blood sugar, and high-density lipoprotein as important markers of both endpoints. RSF analyses with only the top 20 predictors demonstrated good performance, with areas under the curve of >84% for CAD and >82% for ASCVD in test data across 6 years. Conclusions: We present a machine learning technique for accurate assessment of cardiovascular risk using employer-mandated annual health checkup information. The algorithm produces individual-level risk curves over time, empowering clinicians to efficiently implement prevention strategies in a low-risk population.

Development and Validation of a Risk Prediction Model for Atherosclerotic Cardiovascular Disease in Japanese Adults: The Hisayama Study

AIM

To develop and validate a new risk prediction model for predicting the 10-year risk of atherosclerotic cardiovascular disease (ASCVD) in Japanese adults.

METHODS

A total of 2,454 participants aged 40-84 years without a history of cardiovascular disease (CVD) were prospectively followed up for 24 years. An incident ASCVD event was defined as the first occurrence of coronary heart disease or atherothrombotic brain infarction. A Cox proportional hazards regression model was used to construct the prediction model. In addition, a simplified scoring system was translated from the developed prediction model. The model performance was evaluated using Harrell's C statistics, a calibration plot with the Greenwood-Nam-D'Agostino test, and a bootstrap validation procedure.

RESULTS

During a median of a 24-year follow-up, 270 participants experienced the first ASCVD event. The predictors of the ASCVD events in the multivariable Cox model included age, sex, systolic blood pressure, diabetes, serum high-density lipoprotein cholesterol, serum low-density lipoprotein cholesterol, proteinuria, smoking habits, and regular exercise. The developed models exhibited good discrimination with negligible evidence of overfitting (Harrell's C statistics: 0.786 for the multivariable model and 0.789 for the simplified score) and good calibrations (the Greenwood-Nam-D'Agostino test: P=0.29 for the multivariable model, 0.52 for the simplified score).

CONCLUSION

We constructed a risk prediction model for the development of ASCVD in Japanese adults. This prediction model exhibits great potential as a tool for predicting the risk of ASCVD in clinical practice by enabling the identification of specific risk factors for ASCVD in individual patients.

Development and preliminary validation of a machine learning system for thyroid dysfunction diagnosis based on routine laboratory tests

Background

Approximately 2.4 million patients in Japan would benefit from treatment for thyroid disease, including Graves’ disease and Hashimoto’s disease. However, only 450,000 of them are receiving treatment, and many patients with thyroid dysfunction remain largely overlooked. In this retrospective study, we aimed to develop and conduct preliminary testing on a machine learning method for screening patients with hyperthyroidism and hypothyroidism who would benefit from prompt medical treatment.

Methods

We collected electronic medical records and medical checkup data from four hospitals in Japan. We applied four machine learning algorithms to construct classification models to distinguish patients with hyperthyroidism and hypothyroidism from control subjects using routine laboratory tests. Performance evaluation metrics such as sensitivity, specificity, and the area under receiver operating characteristic (AUROC) were obtained. Techniques such as feature importance were further applied to understand the contribution of each feature to the machine learning output.

Results

The results of cross-validation and external evaluation indicated that we achieved high classification accuracies (AUROC = 93.8% for hyperthyroidism model and AUROC = 90.9% for hypothyroidism model). Serum creatinine (S-Cr), mean corpuscular volume (MCV), and total cholesterol were the three features that were most strongly correlated with the hyperthyroidism model, and S-Cr, lactic acid dehydrogenase (LDH), and total cholesterol were correlated with the hypothyroidism model.

Conclusions

We demonstrated the potential of machine learning approaches for diagnosing the presence of thyroid dysfunction from routine laboratory tests. Further validation, including prospective clinical studies, is necessary prior to application of our method in the clinic.

The Lifelong Health Support 10: a Japanese prescription for a long and healthy life

BACKGROUND

Although the age-adjusted incidence and mortality of cancer and cardiovascular disease (CVD) have been decreasing steadily in Japan, both diseases remain major contributors to morbidity and mortality along with the aging society. Herein, we aim to provide a prescription of 10 health tips for long and healthy life named the "Lifelong Health Support 10 (LHS10)."

METHOD

The LHS10 was developed by the preventive medicine specialists at the National Cerebral and Cardiovascular Center in Suita, where it has been used for health guidance to prevent CVD, cancer, and cognitive decline in addition to their major risk factors such as hypertension, diabetes, and obesity. It consisted of the lifestyle modification recommendations of the 2014 Japanese Society of Hypertension guidelines and the 2017 Japan Atherosclerosis Society Guidelines for preventing atherosclerotic CVD. Further, it came in line with other international lifestyle modification guidelines. In this narrative review, we summarized the results of several Japanese epidemiological studies investigating the association between the LHS10 items and the risk of cancer, CVD, and other chronic diseases including dementia, diabetes, and chronic kidney disease.

RESULTS

The LHS10 included avoiding smoking and secondhand smoke exposure, engaging in physical activity, refraining from excessive alcohol drinking, reducing fried foods and sugary soft drinks, cutting salt in food, consuming more vegetables, fruits, fish, soy foods, and fibers, and maintaining proper body weight. All items of the LHS10 were shown to reduce the risk of cancer, CVD, and other chronic diseases.

CONCLUSIONS

The LHS10 can be a helpful tool for health guidance.

Developing a Stroke Risk Prediction Model Using Cardiovascular Risk Factors: The Suita Study

INTRODUCTION

Stroke remains a major cause of death and disability in Japan and worldwide. Detecting individuals at high risk for stroke to apply preventive approaches is recommended. This study aimed to develop a stroke risk prediction model among urban Japanese using cardiovascular risk factors.

METHODS

We followed 6,641 participants aged 30-79 years with neither a history of stroke nor coronary heart disease. The Cox proportional hazard model estimated the risk of stroke incidence adjusted for potential confounders at the baseline survey. The model's performance was assessed using the receiver operating characteristic curve and the Hosmer-Lemeshow statistics. The internal validity of the risk model was tested using derivation and validation samples. Regression coefficients were used for score calculation.

RESULTS

During a median follow-up duration of 17.1 years, 372 participants developed stroke. A risk model including older age, current smoking, increased blood pressure, impaired fasting blood glucose and diabetes, chronic kidney disease, and atrial fibrillation predicted stroke incidence with an area under the curve = 0.76 and p value of the goodness of fit = 0.21. This risk model was shown to be internally valid (p value of the goodness of fit in the validation sample = 0.64). On a risk score from 0 to 26, the incidence of stroke for the categories 0-5, 6-7, 8-9, 10-11, 12-13, 14-15, and 16-26 was 1.1%, 2.1%, 5.4%, 8.2%, 9.0%, 13.5%, and 18.6%, respectively.

CONCLUSION

We developed a new stroke risk model for the urban general population in Japan. Further research to determine the clinical practicality of this model is required.

Do neuroticism and extraversion personality traits influence disease-specific risk factors for mortality from cancer and cardiovascular disease in a Japanese population?

OBJECTIVE

Although many reports discussing the associations between personality traits and mortality have been published internationally, those evaluating the relationships among personality, risk factors, and mortality of cancer and CVD are limited.

METHODS

In a prospective cohort study, we assessed the relationship of neuroticism and extraversion traits with mortality from cancer and cardiovascular disease (CVD) in 11,554 Japanese residents (male, n = 4995; female, n = 6559), and whether the risk factors that have been validated in the Japanese population mediated the relationship. The baseline survey was conducted between February 2004 and August 2007, and the participants were followed until the date of death or December 31, 2013.

RESULTS

Neuroticism was positively associated with risk factors for cancer and negatively associated with the risk score for CVD in both sexes. The relationship between extraversion and cancer risk factors differed depending on the factors, and a positive association between extraversion and the CVD risk score was observed only in men. Among cancer mortality, CVD mortality, and mortality due to other causes, cancer mortality showed remarkably negative association with neuroticism in women; unadjusted hazard ratio for the highest tertile versus the lowest tertile was 0.41 (95% confidence interval [CI], 0.23-0.73). While the logistic regression coefficients changed 19% after adjustment for age, it changed no more than 19% after adjustment for age and risk factors.

CONCLUSION

While neuroticism was negatively associated with cancer mortality in women, the mediating effect of the risk factors was small.

A systematic review of the status and methodological considerations for estimating risk of first ever stroke in the general population

AIMS

The methodological quality of development, validation, and modification of those models have not been evaluated via a thoroughly literature review. This study aims to describe the overall status and evaluate the methodological quality of risk prediction models for stroke incidence in the general population.

METHODS

We searched the database of EMBASE and MEDLINE by the combination of subject words and key words to collect the research on stroke risk prediction model in the general population. The retrieval time was from the establishment of the database to September 2019. It should be mentioned that risk of bias for each model was assessed, and data on population characteristics and model performance was also extracted.

RESULTS

The search screened 11,386 peer-reviewed publications and 57 citation searching, of which 48 were included in the review, describing the development of 51 prediction models, 47 external validation models, and 12 modification models. Among 51 development models, the predicted outcome concentrated on fatal or non-fatal stroke (n = 37, 73%). Thirty-nine development models (76%) were without internal validation. C-statistic or AUC was adopted for discrimination in 80% models, and Hosmer-Lemeshow test (n = 25, 49%) was also performed for calibration. Twenty-six development models (53%) were externally validated, among which only 2 (8%) were validated by independent researchers. Risk prediction performance was improved when models were modified by adding novel risk factors, such as the internal carotid artery plaque and intima-media thickness.

CONCLUSION

Models for predicting stroke occurrence need further external validation, recalibration, or modification in different populations, to help interpret those models in the practice of stroke prevention.

Validation of 10-Year Stroke Prediction Scores in a Community-Based Cohort of Chinese Older Adults

A stroke prediction model based on the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) project was developed. We compared its predictive ability with the revised Framingham Stroke Risk Score (R-FSRS) for 5-year stroke incidence in a community cohort of Chinese adults, namely the Beijing Longitudinal Study of Aging (BLSA). Calibration, discrimination, and recalibration were used to compare the predictive ability between the two prediction models. Category-less net reclassification improvement (NRI) and integrated discrimination improvement (IDI) values were also assessed. During a mean follow-up duration of 5.1 years, 106 incidents of fatal or non-fatal strokes occurred among 1,203 participants aged 55–84 years. The R-FSRS applied to our cohort underestimated the 5-year risk for stroke in men and women. China-PAR performed better than the R-FSRS in terms of calibration (men, R-FSRS: χ²-value 144.2 [P < 0.001], China-PAR: 10.4 [P = 0.238]; women, R-FSRS: 280.1 [P < 0.001], China-PAR: 12.5 [P = 0.129]). In terms of discrimination, R-FSRS and China-PAR models performed modestly in our cohort (C-statistic 0.603 [95% CI: 0.560–0.644] for men using China-PAR and 0.568 [95% CI: 0.524–0.610] using the R-FSRS; the corresponding numbers for women were 0.602 [95% CI: 0.564–0.639] and 0.575 [95% CI: 0.537–0.613). The recalibrated China-PAR model significantly improved the discrimination in C statistics and produced higher category-less NRI and IDI for stroke incidence than the R-FSRS. Although China-PAR fairly estimated stroke risk in our cohort, it did not sufficiently identify adults at high risk of stroke. Caution would be exercised by practitioners in applying the original China-PAR to Chinese older adults. Further studies are needed to develop an adequate prediction model based on the recalibrated China-PAR or to find new risk markers which could upgrade this model.

Estimation of 10-Year Risk of Death from Coronary Heart Disease, Stroke, and Cardiovascular Disease in a Pooled Analysis of Japanese Cohorts: EPOCH-JAPAN

AIMS

We aimed to develop and validate risk prediction models to estimate the absolute 10-year risk of death from coronary heart disease (CHD), stroke, and cardiovascular disease (CVD).

METHODS

We evaluated a total of 44,869 individuals aged 40-79 years from eight Japanese prospective cohorts to derive coefficients of risk equations using cohort-stratified Cox proportional hazard regression models. Discrimination (C-index) of the equation was examined in each cohort and summarised using random-effect meta-analyses. Calibration of the equation was assessed using Hosmer-Lemeshow chi-squared statistic.

RESULTS

Within a median follow-up of 12.7 years, we observed 765 deaths due to CVD (276 CHDs and 489 strokes). After backward selection, age, sex, current smoking, systolic blood pressure (SBP), proteinuria, prevalent diabetes mellitus, the ratio of total cholesterol to high-density lipoprotein cholesterol (TC/HDLC), interaction terms of age by SBP, and age by current smoking were retained as predictors for CHD. Sex was excluded in the stroke equation. We did not consider TC/HDLC as a risk factor for the stroke and CVD equations. The pooled C-indices for CHD, stroke, and CVD were 0.83, 0.80, and 0.81, respectively, and the corresponding p-values of the Hosmer-Lemeshow tests were 0.18, 0.003, and 0.25, respectively.

CONCLUSIONS

Risk equations in the present study can adequately estimate the absolute 10-year risk of death from CHD, stroke, and CVD. Future work will evaluate the system as an education and risk communication tool for primary prevention of CHD and stroke.

Cardiovascular and cerebrovascular diseases risk associated with the incidence of presenteeism and the costs of presenteeism

Objectives

The objective of this study was to estimate a risk of cardiovascular and cerebrovascular diseases for each worker and to determine whether this risk is associated with the incidence and costs of presenteeism, absenteeism, and medical/drug treatments.

Methods

Established risk equations were used to estimate the 10‐year probability of developing coronary artery disease and ischemic stroke in male workers aged 40‐65 years who were recruited from four pharmaceutical companies in Japan. The incidence of presenteeism was defined as existence of presenteeism for the past a month, and the incidence of absenteeism was defined as existence of sick‐leave for the past three months by a self‐administered questionnaire. Each cost was calculated based on the human capital method. Data on medical/drug treatments were collected from health insurance claims.

Results

The risks were calculated for 6047 workers. Individuals at moderate and high risk of coronary artery disease had a significantly higher rate of presenteeism and absenteeism than workers at low risk. Workers at moderate and high risk of ischemic stroke also had a significantly higher rate of presenteeism and absenteeism than workers at low risk. Mean costs for absenteeism and medical/drug treatments increased with the risk of developing coronary artery disease or ischemic stroke, while costs for presenteeism did not.

Conclusions

To prevent the costs of presenteeism, workers not only at high risk but also at low and moderate risk of developing cardiovascular and cerebrovascular diseases should receive health care services.

Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis

OBJECTIVE

Cardiometabolic risk prediction algorithms are common in clinical practice. Young people with psychosis are at high risk for developing cardiometabolic disorders. We aimed to examine whether existing cardiometabolic risk prediction algorithms are suitable for young people with psychosis.

METHODS

We conducted a systematic review and narrative synthesis of studies reporting the development and validation of cardiometabolic risk prediction algorithms for general or psychiatric populations. Furthermore, we used data from 505 participants with or at risk of psychosis at age 18 years in the ALSPAC birth cohort, to explore the performance of three algorithms (QDiabetes, QRISK3 and PRIMROSE) highlighted as potentially suitable. We repeated analyses after artificially increasing participant age to the mean age of the original algorithm studies to examine the impact of age on predictive performance.

RESULTS

We screened 7820 results, including 110 studies. All algorithms were developed in relatively older participants, and most were at high risk of bias. Three studies (QDiabetes, QRISK3 and PRIMROSE) featured psychiatric predictors. Age was more strongly weighted than other risk factors in each algorithm. In our exploratory analysis, calibration plots for all three algorithms implied a consistent systematic underprediction of cardiometabolic risk in the younger sample. After increasing participant age, calibration plots were markedly improved.

CONCLUSION

Existing cardiometabolic risk prediction algorithms cannot be recommended for young people with or at risk of psychosis. Existing algorithms may underpredict risk in young people, even in the face of other high-risk features. Recalibration of existing algorithms or a new tailored algorithm for the population is required.

Non-High-Density Lipoprotein Cholesterol and Risk of Stroke Subtypes and Coronary Heart Disease: The Japan Public Health Center-Based Prospective (JPHC) Study

Aim: A positive association between non-high-density lipoprotein cholesterol (non-HDL-C) and coronary heart disease (CHD) has been established; however, associations between non-HDL-C and stroke subtypes have not been determined.

Methods: We conducted a prospective study of 30,554 individuals aged 40–69 yrs with no history of cardiovascular disease (CVD) in Japan. Sex-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for the incidence of stroke subtypes and CHD were estimated according to quintiles of non-HDL-C, using Cox proportional hazard models adjusted for other established risk factors.

Results: We identified 1,705 stroke and 296 CHD events over a median 15 yrs of follow-up. The fractional polynomials analysis revealed a U-shaped association between non-HDL-C and stroke risk in men. When analyzed for stroke subtypes, the data revealed an inverse relationship between non-HDL-C and intracerebral hemorrhage (ICH), primarily with lobar ICH, and a positive association between non-HDL-C and large-artery occlusive infarction in men [adjusted HR 0.55 (95% CI, 0.35–0.87) and 2.05 (95% CI, 1.07–3.93) for the highest and lowest quintile of non-HDL-C, respectively]. The lowest risk of ICH in women was observed in the fourth quintile, and the lowest risk of embolic infarction was observed in the third quintile. In contrast, non-HDL-C was positively associated with CHD in both sexes.

Conclusions: In Japanese men, lower non-HDL-C levels were associated with a decreased risk of large-artery occlusive infarction and an increased risk of ICH, particularly lobar ICH.

Performance of atherosclerotic cardiovascular risk prediction models in a rural Northern Chinese population: Results from the Fangshan Cohort Study

BACKGROUND

Performance of Pooled Cohort Equations (PCEs) for atherosclerotic cardiovascular disease (ASCVD) risks varied across populations. Whether the recently developed Prediction for ASCVD Risk in China (China-PAR) model could accurately predict cardiovascular risks in real practice remains unclear.

METHODS

A population-based cohort study in rural Beijing in the "stroke belt" in North China was used to externally validate PCE and China-PAR models for 5-year ASCVD risk prediction. Expected 5-year prediction risk using China-PAR model was compared with PCE (white). The models were assessed for calibration, discrimination, and reclassification.

RESULTS

Among 11,169 adults aged 40 to 79 years over a median 6.44 years of follow-up, 1,921 participants developed a first ASCVD event during total 70,951 person-years. China-PAR model fairly predicted ASCVD risk in men but overestimated by 29.4% risk in women (calibration χ² = 81.4, P < .001). Underestimations were shown by PCE as 76.2% in men and 88.2% in women with poor calibration (both P < .001). However, discrimination was similar in both models: C-statistics in men were 0.685 (95% CI 0.660-0.710) for China-PAR and 0.675 (95% CI 0.649-0.701) for PCE; C-statistics in women were 0.711 (95% CI 0.694-0.728) for China-PAR and 0.714 (95% CI 0.697-0.731) for PCE. Moreover, China-PAR did not substantially improve accuracy of reclassification compared with PCE.

CONCLUSIONS

China-PAR outperformed PCE in 5-year ASCVD risk prediction in this rural Northern Chinese population at average population risk level, fairly predicted risk in men, but overestimated risk in women; however, China-PAR did not meaningfully improve the accuracy of discrimination and reclassification at individual risk level.

New risk prediction model of coronary heart disease in participants with and without diabetes: Assessments of the Framingham risk and Suita scores in 3-year longitudinal database in a Japanese population

The Framingham Risk Score (FRS) has been reported to predict coronary heart disease (CHD), but its assessment has been unsuccessful in Asian population. We aimed to assess FRS and Suita score (a Japanese CHD prediction model) in a Japanese nation-wide annual health check program, participants aged 40-79 years were followed up longitudinally from 2008 to 2011. Of 35,379 participants analyzed, 1,234 had new-onset CHD. New-onset CHD was observed in diabetic men [6.00%], non-diabetic men [3.96%], diabetic women [5.51%], and non-diabetic women [2.86%], respectively. Area under the curve (AUC) of receiver operating characteristic (ROC) curve for CHD prediction were consistently low in Suita score (TC), FRS (TC) and NCEP-ATPIII FRS (TC), suggesting that these scores have only a limited power. ROC, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) and Hosmer-Lemeshow goodness-of-fit test did not show clear differences between Suita score (TC) and FRS (TC). New models combining waist circumference ≥85 cm in men or proteinuria ≥1+ in women to Suita score (TC) was superior in diabetic men and women. New models could be useful to predict 3-year risk of CHD at least in Japanese population especially in diabetic population.

Development and validation of modified risk prediction models for cardiovascular disease and its subtypes: The Hisayama Study

BACKGROUND AND AIMS

Predicting cardiovascular events is of practical benefit for disease prevention. The aim of this study was to develop and evaluate an updated risk prediction model for cardiovascular diseases and its subtypes.

METHODS

A total of 2462 community residents aged 40-84 years were followed up for 24 years. A Cox proportional hazards regression model was used to develop risk prediction models for cardiovascular diseases, and separately for stroke and coronary heart diseases. The risk assessment ability of the developed model was evaluated, and a bootstrapping method was used for internal validation. The predicted risk was translated into a simplified scoring system. A decision curve analysis was used to evaluate clinical usefulness.

RESULTS

The multivariable model for cardiovascular diseases included age, sex, systolic blood pressure, hemoglobin A1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, smoking habits, and regular exercise as predictors. The models for stroke and coronary heart diseases incorporated both shared and unique variables. The developed models showed good discrimination with little evidence of overfitting (optimism-corrected Harrell's C statistics 0.726-0.777) and calibrations (Hosmer-Lemeshow test, p = 0.44-0.90). The decision curve analysis revealed that the predicted risk-based decision-making would have higher net benefit than either a CVD intervention strategy for all individuals or no individuals.

CONCLUSIONS

The developed risk prediction models showed a good performance and satisfactory internal validity, which may help understand individual risk and setting personalized goals, and promote risk stratification in public health strategies for CVD prevention.

Comparing the Consistency and Performance of Various Coronary Heart Disease Prediction Models for Primary Prevention Using a National Representative Cohort in Taiwan

BACKGROUND

Predicting future coronary artery disease (CAD) risk by model-based approaches can facilitate identification of high-risk individuals for prevention and management. Therefore, we compared the consistency and performance of various CAD models for primary prevention using 1 external validation dataset from a national representative cohort in Taiwan.Methods and Results:The 10 CAD prediction models were assessed in a validation cohort of 3559 participants (≥35 years old, 53.5% women) from a Taiwanese national representative cohort that was followed up for a median 9.70 (interquartile range, 9.63-9.74) years; 63 cases were documented as developing CAD events. The overall κ value was 0.51 for all 10 models, with a higher value for women than for men (0.53 for women, 0.40 for men). In addition, the areas under the receiver operating characteristics curves ranged from 0.804 (95% confidence interval, 0.758-0.851) to 0.847 (95% confidence interval, 0.805-0.889). All non-significant chi-square values indicated good calibration ability.

CONCLUSIONS

Our study demonstrated these 10 CAD prediction models for primary prevention were feasible and validated for use in Taiwanese subjects. Further studies of screening and management are warranted.

The strength of the multivariable associations of major risk factors predicting coronary heart disease mortality is homogeneous across different areas of the Seven Countries Study during 50-year follow-up

OBJECTIVES

To compare the magnitude of multivariable coefficients and hazard ratios of four cardiovascular risk factors across five worldwide regions of the Seven Countries Study in predicting 50-year coronary deaths.

MATERIAL AND METHODS

A total of 13 cohorts of middle-aged men at entry (40-59 years old) were enrolled in the mid-1900s from five relatively homogeneous groups of cohorts (areas): USA, Finland and Zutphen - the Netherlands, Italy and Greece, Serbia, Japan for a total of 10,368 middle-aged men. The major risk factors measured at baseline were age, number of cigarettes smoked, systolic blood pressure and serum cholesterol. Cox proportional hazards models were solved for 50-year (45 years for Serbia) deaths from coronary heart disease (CHD), and the multivariable coefficients were compared for heterogeneity.

RESULTS

The highest levels of risk factors and CHD death rates were found in Finland and Zutphen - the Netherlands and the lowest in Japan. All four risk factors were predictive for long-term CHD mortality in all regions, except serum cholesterol in Japan where the mean levels and CHD events were lowest. Tests of heterogeneity of coefficients for single risk factors in predicting CHD mortality were non-significant across the five areas. The same analyses for the first 25 years of follow-up produced similar findings.

CONCLUSIONS

The strength of the multivariable associations of four major traditional CHD risk factors with long-term CHD mortality appears to be relatively homogeneous across areas, pending needed further evidence.

Ability of B-Type Natriuretic Peptide Testing to Predict Cardioembolic Stroke in the General Population - Comparisons With C-Reactive Protein and Urinary Albumin

BACKGROUND

The ability of cardiovascular biomarkers to predict the incidence of stroke subtypes remains ill-defined in the general population.Methods and Results:The blood levels of B-type natriuretic peptide (BNP) and high-sensitivity C-reactive protein (hs-CRP) and urinary albumin corrected by urinary creatinine (UACR) were determined in a general population (n=13,575). The ability to predict the incidence of ischemic stroke subtypes (lacunar, atherothrombotic, cardioembolic) for each biomarker was assessed based on the area under the receiver-operating characteristic curve (AUC-ROC) and using Cox proportional hazard modeling. The predictive abilities of UACR and hs-CRP for any subtype of ischemic event were found to be suboptimal. However, the ability of BNP to predict the incidence of cardioembolic stroke was excellent (AUC-ROC=0.81). When BNP was added to established stroke risk factors, the ability to predict cardioembolic stroke in terms of the AUC-ROC significantly improved (4-year follow-up, P=0.018; 8-year follow-up, P=0.009). Furthermore, when BNP was added to the JPHC score, the ability to predict cardioembolic stroke was significantly improved (net reclassification improvement=0.968, P<0.0001: integrated discrimination improvement=0.039, P<0.05).

CONCLUSIONS

In the general population, plasma BNP was an excellent biomarker for predicting the incidence of cardioembolic stroke when used alone or in combination with established stroke risk factors.

Atherosclerotic Cardiovascular Risk in Japan

Increased global mortality is associated with atherosclerosis, which appears to be independent of race. Cardiovascular disease is one of the leading causes of mortality and morbidity in Japan. Atherosclerosis, an inflammatory disease characterized by abnormal lipid accumulation and inflammation in the arterial wall, is the main underlying cause of cardiovascular disease. Numerous cardiovascular risk scores have been developed and are used to prioritize patients' treatment needs. The predictive performance of risk scores established in Western nations needs to be examined in Japanese populations. For secondary prevention, it is imperative to control hypertension, hyperlipidemia, diabetes mellitus, smoking, and local interventions. In this review, we present a historical overview of atherosclerotic risk research and the risk factors for atherosclerosis in Japan and compare the situation in Japan with that in Western nations. In addition, we discuss relevant cardiovascular risk assessment tools in the context of clinical practice in Japan.

Results of a Prospective Study of Acute Coronary Syndrome Hospitalization After Enactment of a Smoking Ban in Public Places in Hyogo Prefecture - Comparison With Gifu, a Prefecture Without a Public Smoking Ban

BACKGROUND

Hyogo Prefecture is the 2nd prefecture in Japan, after Kanagawa, to enact a ban with penal code on smoking in public places, although the restriction is partial.Methods and Results:This study included consecutive patients with acute coronary syndrome (ACS) who were admitted to 33 major hospitals in the Hyogo District during the 12 months before implementation of the legislation and during the 24 months thereafter. Consecutive patients with ACS from Gifu Prefecture who were admitted to 20 major hospitals were enrolled as geographical controls. The number of ACS admissions did not change from the years 2012-2015 in both Hyogo District (1,774 in the pre-year, 1,784 in the 1st year, and 1,720 in the 2nd year) and Gifu Prefecture (1,226 in the pre-year, 1,174 in the 1st year, and 1,206 in the 2nd year). However, a clear reduction was observed in the subanalysis for Kobe City (895 in the preceding year, 830 (-7.3%) in the 1st year, and 792 (-11.5%) in the 2nd year), where adherence to the smoking ban was higher than in other Hyogo districts.

CONCLUSIONS

The primary endpoint did not show a significant change. However, the subanalysis showed a significant decrease in ACS admissions in Kobe City. These results suggest that ACS reduction may depend on the degree of adherence to a smoking ban. (Circ J 2016; 80: 2528-2532).

Validation of the Korean Genome Epidemiology Study Risk Score to Predict Incident Hypertension in a Large Nationwide Korean Cohort

BACKGROUND

This study aimed to validate the Korean Genome Epidemiology Study (KoGES) risk score to predict the 4-year risk of hypertension (HT) in a large nationwide sample, and compare its discrimination and calibration with the Framingham and blood pressure (BP)-only models.

METHODS AND RESULTS

This study analyzed 69,918 subjects without HT at baseline from the National Sample Cohort in the National Health Insurance Service database. We compared the Framingham, KoGES, and BP-only models for discrimination using area under the receiver-operating characteristic curves (AROC), calibration using goodness-of-fit tests, and reclassification ability using the continuous net reclassification improvement (NRI) and integrated discrimination improvement. Of 69,918 subjects, 18.6% developed HT during the follow-up. AROC was significantly higher for the KoGES (0.733) than for the Framingham (0.729) or BP-only (0.707) model. Recalibrated Framingham model underestimated HT incidence in all deciles (P<0.001). BP-only model overestimated risk in the lower deciles (P<0.001). KoGES model accurately predicted risk in all except the highest decile (χ(2)=14.85, P=0.062). The KoGES model led to a significant improvement in risk reclassification compared with the Framingham and BP-only models (NRI, 0.354; 95% confidence interval [CI], 0.343-0.365 and 0.542; 95% CI, 0.523-0.561, respectively).

CONCLUSIONS

In this validation study, the KoGES model demonstrated better discrimination, calibration, and reclassification ability than either the Framingham or BP-only model. The KoGES model may help identify Korean individuals at high risk for HT. (Circ J 2016; 80: 1578-1582).