Recalibration and validation of the SCORE risk chart in the Australian population: the AusSCORE chart

Chen, Lei; Tonkin, Andrew; Moon, Lynelle; Mitchell, Paul; Dobson, Annette; Giles, Graham G.; Hobbs, Michael; Phillips, Patrick J.; Shaw, Jonathan E.; Simmons, David; Simons, Leon A.; Fitzgerald, Anthony P.; Backer, Guy De; Bacquer, Dirk De

doi:10.1097/hjr.0b013e32832cd9cb

Cited by 37 publications

(21 citation statements)

References 24 publications

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“…4,5 This challenge can be dealt with by recalibrating the risk prediction equation; that is, resetting the mean risk factor levels and disease risk to current levels for the target population. 6–8 However, such recalibration is rarely done because most countries do not have the information needed, and current risk equations are difficult to recalibrate. 9 A previous set of risk charts published by WHO only provided predicted CVD risk for regions and not individual countries.…”

Section: Introductionmentioning

confidence: 99%

Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys

Ueda

Woodward

et al. 2017

The Lancet Diabetes & Endocrinology

105

135

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Summary Background Worldwide implementation of risk-based cardiovascular disease (CVD) prevention requires risk prediction tools that are contemporarily recalibrated for the target country and can be used where laboratory measurements are unavailable. We present two cardiovascular risk scores, with and without laboratory-based measurements, and the corresponding risk charts for 182 countries to predict 10-year risk of fatal and non-fatal CVD in adults aged 40–74 years. Methods Based on our previous laboratory-based prediction model (Globorisk), we used data from eight prospective studies to estimate coefficients of the risk equations using proportional hazard regressions. The laboratory-based risk score included age, sex, smoking, blood pressure, diabetes, and total cholesterol; in the non-laboratory (office-based) risk score, we replaced diabetes and total cholesterol with BMI. We recalibrated risk scores for each sex and age group in each country using country-specific mean risk factor levels and CVD rates. We used recalibrated risk scores and data from national surveys (using data from adults aged 40–64 years) to estimate the proportion of the population at different levels of CVD risk for ten countries from different world regions as examples of the information the risk scores provide; we applied a risk threshold for high risk of at least 10% for high-income countries (HICs) and at least 20% for low-income and middle-income countries (LMICs) on the basis of national and international guidelines for CVD prevention. We estimated the proportion of men and women who were similarly categorised as high risk or low risk by the two risk scores. Findings Predicted risks for the same risk factor profile were generally lower in HICs than in LMICs, with the highest risks in countries in central and southeast Asia and eastern Europe, including China and Russia. In HICs, the proportion of people aged 40–64 years at high risk of CVD ranged from 1% for South Korean women to 42% for Czech men (using a ≥10% risk threshold), and in low-income countries ranged from 2% in Uganda (men and women) to 13% in Iranian men (using a ≥20% risk threshold). More than 80% of adults were similarly classified as low or high risk by the laboratory-based and office-based risk scores. However, the office-based model substantially underestimated the risk among patients with diabetes. Interpretation Our risk charts provide risk assessment tools that are recalibrated for each country and make the estimation of CVD risk possible without using laboratory-based measurements.

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Section: Introductionmentioning

confidence: 99%

Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys

Ueda

Woodward

et al. 2017

The Lancet Diabetes & Endocrinology

105

135

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“…Our novel, nationally-recalibrated, risk algorithm includes traditional CVD risk factors, as used in Framingham [2] and the European SCORE [14], in addition to measures of socioeconomic deprivation and chronic kidney disease (eGFR), both of which have been shown to independently predict CVD risk [4, 5, 16]. Accounting for deprivation in a risk algorithm will ensure preventive treatments are more fairly and efficiently allocated and will help to reduce socioeconomic inequalities in CVD.…”

Section: Discussionmentioning

confidence: 99%

“…Second, because there has been a considerable annual decrease in ‘background’ CVD mortality rates in Australia since the studies used to create the primary score were inaugurated (Additional file 1: Figure S1). The primary score was thus recalibrated [10] using the most current (2013) national data on mortality [11] and risk factors [12], using similar methodology to the GLOBORISK project [13] and an earlier, unadopted, Australian risk score [14] that was recalibrated from European Systematic COronary Risk Evaluation (SCORE) estimates of risk [15]. …”

Section: Methodsmentioning

confidence: 99%

Development of an Australian cardiovascular disease mortality risk score using multiple imputation and recalibration from national statistics

Backholer

Hirakawa

Tonkin

et al. 2017

BMC Cardiovasc Disord

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ObjectiveTo develop and recalibrate an Australian 5-year cardiovascular disease (CVD) mortality risk score to produce contemporary predictions of risk.MethodsData were pooled from six Australian cohort studies (n = 54,829), with baseline data collected between 1989 and 2003. Participants included were aged 40–74 years and free of CVD at baseline. Variables were harmonised across studies and missing data were imputed using multiple imputation. Cox proportional hazards models were used to estimate the risk of CVD mortality associated with factors mutually independently predictive (p < 0.05) and a 5-year risk prediction algorithm was constructed. This algorithm was recalibrated to reflect contemporary national levels of CVD mortality and risk factors using national statistics.ResultsOver a mean 16.6 years follow-up, 1375 participants in the six studies died from CVD. The prediction model included age, sex, smoking, diabetes, systolic blood pressure, total and high-density lipoprotein cholesterol (HDLC), a social deprivation score, estimated glomerular filtration rate and its square and interactions of sex with diabetes, HDLC and deprivation score, and of age with systolic blood pressure and smoking. This model discriminated well when applied to a Scottish study population (c-statistic (95% confidence interval): 0.751 (0.709, 0.793)). Recalibration generally increased estimated risks, but well below those predicted by the European SCORE models.ConclusionsThe resulting risk score, which includes markers of both chronic kidney disease and socioeconomic deprivation, is the first CVD mortality risk prediction tool for Australia to be derived using Australian data. The primary model, and the method of recalibration, is applicable elsewhere.Electronic supplementary materialThe online version of this article (doi:10.1186/s12872-016-0462-5) contains supplementary material, which is available to authorized users.

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“…Чтобы нивелировать влияние расы ее добавляют в число предикторов, как например в шкале GWTG-HF, или проводят дополнительную калибровку шкалы [13,14].…”

Section: важно учитывать для каких пациентов приме-нима данная шкалаunclassified

Application of Prediction Scores in Clinical Medicine

Белялов¹

2016

Russ J Cardiol

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Recalibration and validation of the SCORE risk chart in the Australian population: the AusSCORE chart

Abstract: This new tool provides a valid and reliable method to predict risk of CVD mortality in the general Australian population.

Cited by 37 publications

References 24 publications

Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys

Laboratory-based and office-based risk scores and charts to predict 10-year risk of cardiovascular disease in 182 countries: a pooled analysis of prospective cohorts and health surveys

Development of an Australian cardiovascular disease mortality risk score using multiple imputation and recalibration from national statistics

Application of Prediction Scores in Clinical Medicine

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