Recalibration of the SCORE risk chart for the Russian population

Jdanov, Dmitri A.; Деев, А. Д.; Jasilionis, Domantas; Shalnova, S. А.; Ma, Shkol'nikova; Shkolnikov, Vladimir M.

doi:10.1007/s10654-014-9947-7

Cited by 17 publications

(6 citation statements)

References 22 publications

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“…We assessed a hypothetical impact of the reduction in male smoking on deaths from CVD in 2008-2016. The estimation procedure was based on data that have been used in our earlier estimation of the risk of CVD death in Russia associated with current smoking [24]. We used a proportional hazard model to calculate separate hazard ratios for the three ranges of ages at baseline: 15 to 49, 50 to 64, and 65 and older.…”

Section: Discussionmentioning

confidence: 99%

Time trends in smoking in Russia in the light of recent tobacco control measures: synthesis of evidence from multiple sources

et al. 2020

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Background The study aims at identifying long-term trends and patterns of current smoking by age, gender, and education in Russia, including the most recent period from 2008 during which tobacco control policies were implemented, and to estimate the impact on mortality of any reductions in prevalence. We present an in-depth analysis based on an unprecedentedly large array of survey data. Methods We examined pooled micro-data on smoking from 17 rounds of the Russian Longitudinal Monitoring Study of 1996–2016, 11 other surveys conducted in Russia in 1975–2017, and two comparator surveys from England and the USA. Standardization by age and education, regression and meta-analysis were used to estimate trends in the prevalence of current smoking by gender, age, and educational patterns. Results From the mid-1970s to the mid-2000s smoking prevalence among men was relatively stable at around 60%, after which time prevalence declined in every age and educational group. Among women, trends in smoking were more heterogeneous. Prevalence more than doubled above the age of 55 years from very low levels (< 5%). At younger ages, there were steep increases until the mid-2000s after which prevalence has declined. Trends differed by educational level, with women in the lowest educational category accounting for most of the long-term increase. We estimate that the decline in male smoking may have contributed 6.2% of the observed reduction in cardiovascular deaths among men in the period 2008–16. Conclusions The implementation of an effective tobacco control strategy in Russia starting in 2008 coincided with a decline in smoking prevalence among men from what had been stable, high levels over many decades regardless of age and education. Among women, the declines have been more uneven, with young women showing recent downturns, while the smoking prevalence in middle age has increased, particularly among those with minimal education. Among men, these positive changes will have made a small contribution to the reduction in mortality seen in Russia since 2005.

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Section: Discussionmentioning

confidence: 99%

Time trends in smoking in Russia in the light of recent tobacco control measures: synthesis of evidence from multiple sources

et al. 2020

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“…The calibration of the SCORE model was different among various populations. Sometimes it performed well, 26,27 although other studies in Europe, 24 and Russia 28 showed a degree of under-or overestimation. External validity depends on the truth of the regression coefficients, the distribution of the predictors, and the baseline CVD free survival.…”

Section: Discussionmentioning

confidence: 85%

Prediction of Cardiovascular Disease Mortality in a Middle Eastern Country: Performance of the Globorisk and Score Functions in Four Population-Based Cohort Studies of Iran

Fahimfar

Fotouhi

Mansournia

et al. 2020

Int J Health Policy Manag

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Background: Considering the importance of cardiovascular disease (CVD) risk prediction for healthcare systems and the limited information available in the Middle East, we evaluated the SCORE and Globorisk models to predict CVD death in a country of this region. Methods: We included 24 427 participants (11 187 men) aged 40-80 years from four population-based cohorts in Iran. Updating approaches were used to recalibrate the baseline survival and the overall effect of the predictors of the models. We assessed the models’ discrimination using C-index and then compared the observed with the predicted risk of death using calibration plots. The sensitivity and specificity of the models were estimated at the risk thresholds of 3%, 5%, 7%, and 10%. An agreement between models was assessed using the intra-class correlation coefficient (ICC). We applied decision analysis to provide perception into the consequences of using the models in general practice; for this reason, the clinical usefulness of the models was assessed using the net benefit (NB) and decision curve analysis. The NB is a sensitivity penalized by a weighted false positive (FP) rate in population level. Results: After 154 522 person-years of follow-up, 437 cardiovascular deaths (280 men) occurred. The 10-year observed risks were 4.2% (95% CI: 3.7%-4.8%) in men and 2.1% (1.8-2%.5%) in women. The c-index for SCORE function was 0.784 (0.756-0.812) in men and 0.780 (0.744-0.815) in women. Corresponding values for Globorisk were 0.793 (0.766- 0.820) and 0.793 (0.757-0.829). The deviation of the calibration slopes from one reflected a need for recalibration; after which, the predicted-to-observed ratio for both models was 1.02 in men and 0.95 in women. Models showed good agreement (ICC 0.93 in men, and 0.89 in women). Decision curve showed that using both models results in the same clinical usefulness at the risk threshold of 5%, in both men and women; however, at the risk threshold of 10%, Globorisk had better clinical usefulness in women (Difference: 8%, 95% CI: 4%-13%). Conclusion: Original Globorisk and SCORE models overestimate the CVD risk in Iranian populations resulting in a high number of people who need intervention. Recalibration could adopt these models to precisely predict CVD mortality. Globorisk showed better performance clinically, only among high-risk women.

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“…The main aspects of design and research methods are published previously [7,8]. 1,200 patients with dierent values of the cardiovascular risk, calculated on the scale of SCORE (Systematic Coronary Risk Evaluation), were included in the study [9]. The subgroup 1 patients with low to moderate risk of cardiovascular events (< 5%), the subgroup 2 patients without clinic of coronary heart disease (CHD), but with high and very high risk of cardiovascular events (≥ 5%), the subgroup 3 patients with ischemic heart disease (angina, myocardial infarction, revascularization).…”

Section: Methodsmentioning

confidence: 99%

Genome-Wide Analysis of Genetic Associations for Prediction of Polygenic Hypercholesterolemia with Bayesian Networks

Сулимов¹,

Мешков²,

Savkin³

et al. 2015

JCEM

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Healthcare of the Russian Federation, Moscow, Russian Federation. 4 Russian Cardiology Research and Production Complex of the Ministry of Healthcare of the Russian Federation, Moscow, Russian Federation.The genome-wide analysis of genetic associations with lipid metabolism indicators was carried out using the technology of Bayesian networks (BN). It was performed to diagnose polygenic hypercholesterolemia on the basis of genetic data of the Russian population of patients. The data of 1,200 patients was analyzed. 196725 SNPs as well as clinical data, lipid prole indicators dierent types of cholesterol were obtained for each of them. The genome-wide association analysis (GWAS) and the statistical method of Pearson's chisquared test were used for the initial selection of the most signicant parameters. Two of the patient states related to a lipid metabolism were studied. These states are the level of LDL-C (low density lipoprotein) and the level of HDL-C (high density lipoprotein). The Bayesian networks having the simplest topology naive were used to predict the level of lipoprotein. The construction of ROC-curves and the calculation of the area under these curves (AUC) were used to assess a quality (reliability) of the prediction. AUC value increased from 0,5 for the initial BN to 0,9 after selecting of signicant parameters using the GWAS method or the Pearson one. A further increase in AUC to 0,99 and decrease in the number of prognostic parameters to 150 was performed using Bayesian network optimization with respect to the number of parameters-nodes. Here the optimized function was value of AUC. The ambiguity of obtaining prognostic parameters at various ways of initial reducing the number of network nodes using the methods of GWAS and Pirson is shown. Low values of AUC were obtained for an independent control group of patients, despite very good results on the quality of the predictions, which were obtained on the training set. Further application of the proposed methodology is possible after the substantial reduction of the number of SNPs on the base of the analysis of the respective molecular mechanisms.

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Recalibration of the SCORE risk chart for the Russian population

Cited by 17 publications

References 22 publications

Time trends in smoking in Russia in the light of recent tobacco control measures: synthesis of evidence from multiple sources

Time trends in smoking in Russia in the light of recent tobacco control measures: synthesis of evidence from multiple sources

Prediction of Cardiovascular Disease Mortality in a Middle Eastern Country: Performance of the Globorisk and Score Functions in Four Population-Based Cohort Studies of Iran

Genome-Wide Analysis of Genetic Associations for Prediction of Polygenic Hypercholesterolemia with Bayesian Networks

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