A Prediction Model with Lifestyle in Addition to Previously Known Risk Factors Improves Its Predictive Ability for Cardiovascular Death

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.

A prediction model with lifestyle factors improves the predictive ability for renal replacement therapy: a cohort of 442,714 participants Asian adults

Background

There are limited renal replacement therapy (RRT) prediction models with good performance in the general population. We developed a model that includes lifestyle factors to improve predictive ability for RRT in the population at large.

Methods

We used data collected between 1996 and 2017 from a medical screening in a cohort comprising 442 714 participants aged 20 years or over. After a median follow-up of 13 years, we identified 2212 individuals with end-stage renal disease (RRT, n: 2091; kidney transplantation, n: 121). We built three models for comparison: model 1: basic model, Kidney Failure Risk Equation with four variables (age, sex, estimated glomerular filtration rate and proteinuria); model 2: basic model + medical history + lifestyle risk factors; and model 3: model 2 + all significant clinical variables. We used the Cox proportional hazards model to construct a points-based model and applied the C statistic.

Results

Adding lifestyle factors to the basic model, the C statistic improved in model 2 from 0.91 to 0.94 (95% confidence interval: 0.94, 0.95). Model 3 showed even better C statistic value i.e., 0.95 (0.95, 0.96). With a cut-off score of 33, model 3 identified 3% of individuals with RRT risk in 10 years. This model detected over half of individuals progressing to RRT, which was higher than the sensitivity of cohort participants with stage 3 or higher chronic kidney disease (0.53 versus 0.48).

Conclusions

Our prediction model including medical history and lifestyle factors improved the predictive ability for end-stage renal disease in the general population in addition to chronic kidney disease population.

Adding Estimated Cardiorespiratory Fitness to the Framingham Risk Score and Mortality Risk in a Korean Population-Based Cohort Study

BACKGROUND

The added value of non-exercise-based estimation of cardiorespiratory fitness (eCRF) to cardiovascular disease (CVD) risk factors for mortality risk has not been examined in Korean populations.

METHODS

This population-based prospective cohort study examined the relationship of the 10-year Framingham risk score (FRS) for CVD risk and eCRF with all-cause and CVD mortality in a representative sample of Korean adults aged 30 years and older. Data regarding a total of 38,350 participants (16,505 men/21,845 women) were obtained from the 2007-2015 Korea National Health and Nutrition Examination Survey (KNHANES). All-cause and CVD mortality were the main outcomes. The 10-year FRS point sum and eCRF level were the main exposures.

RESULTS

All-cause and CVD mortality was positively correlated with the 10-year FRS point summation and inversely correlated with eCRF level in this study population. The protective of high eCRF against all-cause and CVD mortality was more prominent in the middle and high FRS category than in the low FRS category. Notably, the FRS plus eCRF model has better predictor power for estimating mortality risk compared to the FRS only model.

CONCLUSIONS

The current findings indicate that eCRF can be used as an alternative to objectively measured CRF for mortality risk prediction.

Differences in the Cardiovascular Risk Assessment in Cardiology Outpatients in Mali: Comparison between Framingham Body Mass Index-Based Tool and Low-Information World Health Organization Chart

Objective. This study aimed to compare 2 laborless tools, namely, the body mass index-based Framingham (bmi-Frm) and low-information WHO- (li-WHO-) based risk scores, and assess their agreement in outpatients in a cardiology department. Methodology. Data stem from a cross-sectional previous study performed from May to September 2016 in the Cardiology Department of University Hospital Gabriel Touré (UH-GT) in Bamako. All patients aged 40 and more were included in the study allowing the assessment of bmi-Frm and li-WHO prediction charts. The cardiovascular risk (CVR) was evaluated using a calculator prepared by D‘Agostino et al. for the bmi-Frm and the li-WHO chart for the Afro-D region of the WHO. The risk score for both ranged from <10 to ≥40. The data were entered in an ACCESS 2010 database, then processed by MS Excel 2010, and finally analysed using IBM SPSS Statistics 20. Continuous variables were presented as means and standard deviations, and categorical variables were presented as frequencies with percentages. $P<0.05$ was considered the statistical significance level. After sample description, the risk score was assessed using bmi-Frm and li-WHO prediction tools. Finally, a kappa test was performed to check for the interreliability of both methods. For weighted kappa, coefficients were given all five classes of risk groups in 0, 25 steps from 1 for total concordance to 0 for total discordance. Results. This study involved 793 outpatients, 63.7% being female, 35.1% of them younger than 50 years, 57.9% with no formal education, and 67.7% with no medical insurance. Means for age, body mass index (BMI), and systolic blood pressure (SBP) were, respectively, 53.81 ± 16.729 years, 25.29 ± 06.151 kg/m2, and 139.49 ± 27.110 mm Hg. Using the li-WHO prediction chart gives a much higher proportion of low-risk patients compared to bmi-Frm (83.6 vs. 37.7). Sociodemographic characteristics such as education or income level were not different in risk score neither for the bmi-Frm nor for the li-WHO risk score. The percentage of agreement between both tools was 40.4%, and agreement (kappa of 0.1 and weighted kappa of 0.2) was found to be slight. Conclusion. Using the bmi-Frm and li-WHO tool gives a similar risk estimation in younger female patients. Older patients must be evaluated using high-information tools with cholesterol, e.g., versions of the Framingham risk equation or WHO using cholesterol. These must be confirmed in further studies and compared to data from prospective studies

Lifestyle Changes and Weight Gain: A 2-Year Follow-up Study of Japanese Workers

OBJECTIVE

To examine age-dependent trends in weight and lifestyle changes in Japanese workers.

METHODS

Using annual health examination data, 60,143 eligible Japanese workers aged 20 to 59 years were examined for their 2-year changes in weight and smoking, eating, exercise, drinking, and sleep habits.

RESULTS

Young male workers aged 20 to 24 years showed the greatest weight gain and the highest incidence of unhealthy lifestyle habits. Multivariate analyses indicated that quitting "exercise less than two times/week," "walking less than 60 min/d," and "smoking everyday" contributed to weight gain to a considerable extent except in young female workers aged 20 to 24 years.

CONCLUSIONS

Greater weight gain associated with unhealthy lifestyle changes tended to occur in early rather than middle-to-old adulthood. It is important to deliver health promotion programs targeting young male workers.